research paper on survey methodology

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Article Contents

What is survey research, advantages and disadvantages of survey research, essential steps in survey research, research methods, designing the research tool, sample and sampling, data collection, data analysis.

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Good practice in the conduct and reporting of survey research

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KATE KELLEY, BELINDA CLARK, VIVIENNE BROWN, JOHN SITZIA, Good practice in the conduct and reporting of survey research, International Journal for Quality in Health Care , Volume 15, Issue 3, May 2003, Pages 261–266, https://doi.org/10.1093/intqhc/mzg031

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Survey research is sometimes regarded as an easy research approach. However, as with any other research approach and method, it is easy to conduct a survey of poor quality rather than one of high quality and real value. This paper provides a checklist of good practice in the conduct and reporting of survey research. Its purpose is to assist the novice researcher to produce survey work to a high standard, meaning a standard at which the results will be regarded as credible. The paper first provides an overview of the approach and then guides the reader step-by-step through the processes of data collection, data analysis, and reporting. It is not intended to provide a manual of how to conduct a survey, but rather to identify common pitfalls and oversights to be avoided by researchers if their work is to be valid and credible.

Survey research is common in studies of health and health services, although its roots lie in the social surveys conducted in Victorian Britain by social reformers to collect information on poverty and working class life (e.g. Charles Booth [ 1 ] and Joseph Rowntree [ 2 ]), and indeed survey research remains most used in applied social research. The term ‘survey’ is used in a variety of ways, but generally refers to the selection of a relatively large sample of people from a pre-determined population (the ‘population of interest’; this is the wider group of people in whom the researcher is interested in a particular study), followed by the collection of a relatively small amount of data from those individuals. The researcher therefore uses information from a sample of individuals to make some inference about the wider population.

Data are collected in a standardized form. This is usually, but not necessarily, done by means of a questionnaire or interview. Surveys are designed to provide a ‘snapshot of how things are at a specific time’ [ 3 ]. There is no attempt to control conditions or manipulate variables; surveys do not allocate participants into groups or vary the treatment they receive. Surveys are well suited to descriptive studies, but can also be used to explore aspects of a situation, or to seek explanation and provide data for testing hypotheses. It is important to recognize that ‘the survey approach is a research strategy, not a research method’ [ 3 ]. As with any research approach, a choice of methods is available and the one most appropriate to the individual project should be used. This paper will discuss the most popular methods employed in survey research, with an emphasis upon difficulties commonly encountered when using these methods.

Descriptive research

Descriptive research is a most basic type of enquiry that aims to observe (gather information on) certain phenomena, typically at a single point in time: the ‘cross-sectional’ survey. The aim is to examine a situation by describing important factors associated with that situation, such as demographic, socio-economic, and health characteristics, events, behaviours, attitudes, experiences, and knowledge. Descriptive studies are used to estimate specific parameters in a population (e.g. the prevalence of infant breast feeding) and to describe associations (e.g. the association between infant breast feeding and maternal age).

Analytical studies

Analytical studies go beyond simple description; their intention is to illuminate a specific problem through focused data analysis, typically by looking at the effect of one set of variables upon another set. These are longitudinal studies, in which data are collected at more than one point in time with the aim of illuminating the direction of observed associations. Data may be collected from the same sample on each occasion (cohort or panel studies) or from a different sample at each point in time (trend studies).

Evaluation research

This form of research collects data to ascertain the effects of a planned change.

Advantages:

The research produces data based on real-world observations (empirical data).

The breadth of coverage of many people or events means that it is more likely than some other approaches to obtain data based on a representative sample, and can therefore be generalizable to a population.

Surveys can produce a large amount of data in a short time for a fairly low cost. Researchers can therefore set a finite time-span for a project, which can assist in planning and delivering end results.

Disadvantages:

The significance of the data can become neglected if the researcher focuses too much on the range of coverage to the exclusion of an adequate account of the implications of those data for relevant issues, problems, or theories.

The data that are produced are likely to lack details or depth on the topic being investigated.

Securing a high response rate to a survey can be hard to control, particularly when it is carried out by post, but is also difficult when the survey is carried out face-to-face or over the telephone.

Research question

Good research has the characteristic that its purpose is to address a single clear and explicit research question; conversely, the end product of a study that aims to answer a number of diverse questions is often weak. Weakest of all, however, are those studies that have no research question at all and whose design simply is to collect a wide range of data and then to ‘trawl’ the data looking for ‘interesting’ or ‘significant’ associations. This is a trap novice researchers in particular fall into. Therefore, in developing a research question, the following aspects should be considered [ 4 ]:

Be knowledgeable about the area you wish to research.

Widen the base of your experience, explore related areas, and talk to other researchers and practitioners in the field you are surveying.

Consider using techniques for enhancing creativity, for example brainstorming ideas.

Avoid the pitfalls of: allowing a decision regarding methods to decide the questions to be asked; posing research questions that cannot be answered; asking questions that have already been answered satisfactorily.

The survey approach can employ a range of methods to answer the research question. Common survey methods include postal questionnaires, face-to-face interviews, and telephone interviews.

Postal questionnaires

This method involves sending questionnaires to a large sample of people covering a wide geographical area. Postal questionnaires are usually received ‘cold’, without any previous contact between researcher and respondent. The response rate for this type of method is usually low, ∼20%, depending on the content and length of the questionnaire. As response rates are low, a large sample is required when using postal questionnaires, for two main reasons: first, to ensure that the demographic profile of survey respondents reflects that of the survey population; and secondly, to provide a sufficiently large data set for analysis.

Face-to-face interviews

Face-to-face interviews involve the researcher approaching respondents personally, either in the street or by calling at people’s homes. The researcher then asks the respondent a series of questions and notes their responses. The response rate is often higher than that of postal questionnaires as the researcher has the opportunity to sell the research to a potential respondent. Face-to-face interviewing is a more costly and time-consuming method than the postal survey, however the researcher can select the sample of respondents in order to balance the demographic profile of the sample.

Telephone interviews

Telephone surveys, like face-to-face interviews, allow a two-way interaction between researcher and respondent. Telephone surveys are quicker and cheaper than face-to-face interviewing. Whilst resulting in a higher response rate than postal surveys, telephone surveys often attract a higher level of refusals than face-to-face interviews as people feel less inhibited about refusing to take part when approached over the telephone.

Whether using a postal questionnaire or interview method, the questions asked have to be carefully planned and piloted. The design, wording, form, and order of questions can affect the type of responses obtained, and careful design is needed to minimize bias in results. When designing a questionnaire or question route for interviewing, the following issues should be considered: (1) planning the content of a research tool; (2) questionnaire layout; (3) interview questions; (4) piloting; and (5) covering letter.

Planning the content of a research tool

The topics of interest should be carefully planned and relate clearly to the research question. It is often useful to involve experts in the field, colleagues, and members of the target population in question design in order to ensure the validity of the coverage of questions included in the tool (content validity).

Researchers should conduct a literature search to identify existing, psychometrically tested questionnaires. A well designed research tool is simple, appropriate for the intended use, acceptable to respondents, and should include a clear and interpretable scoring system. A research tool must also demonstrate the psychometric properties of reliability (consistency from one measurement to the next), validity (accurate measurement of the concept), and, if a longitudinal study, responsiveness to change [ 5 ]. The development of research tools, such as attitude scales, is a lengthy and costly process. It is important that researchers recognize that the development of the research tool is equal in importance—and deserves equal attention—to data collection. If a research instrument has not undergone a robust process of development and testing, the credibility of the research findings themselves may legitimately be called into question and may even be completely disregarded. Surveys of patient satisfaction and similar are commonly weak in this respect; one review found that only 6% of patient satisfaction studies used an instrument that had undergone even rudimentary testing [ 6 ]. Researchers who are unable or unwilling to undertake this process are strongly advised to consider adopting an existing, robust research tool.

Questionnaire layout

Questionnaires used in survey research should be clear and well presented. The use of capital (upper case) letters only should be avoided, as this format is hard to read. Questions should be numbered and clearly grouped by subject. Clear instructions should be given and headings included to make the questionnaire easier to follow.

The researcher must think about the form of the questions, avoiding ‘double-barrelled’ questions (two or more questions in one, e.g. ‘How satisfied were you with your personal nurse and the nurses in general?’), questions containing double negatives, and leading or ambiguous questions. Questions may be open (where the respondent composes the reply) or closed (where pre-coded response options are available, e.g. multiple-choice questions). Closed questions with pre-coded response options are most suitable for topics where the possible responses are known. Closed questions are quick to administer and can be easily coded and analysed. Open questions should be used where possible replies are unknown or too numerous to pre-code. Open questions are more demanding for respondents but if well answered can provide useful insight into a topic. Open questions, however, can be time consuming to administer and difficult to analyse. Whether using open or closed questions, researchers should plan clearly how answers will be analysed.

Interview questions

Open questions are used more frequently in unstructured interviews, whereas closed questions typically appear in structured interview schedules. A structured interview is like a questionnaire that is administered face to face with the respondent. When designing the questions for a structured interview, the researcher should consider the points highlighted above regarding questionnaires. The interviewer should have a standardized list of questions, each respondent being asked the same questions in the same order. If closed questions are used the interviewer should also have a range of pre-coded responses available.

If carrying out a semi-structured interview, the researcher should have a clear, well thought out set of questions; however, the questions may take an open form and the researcher may vary the order in which topics are considered.

A research tool should be tested on a pilot sample of members of the target population. This process will allow the researcher to identify whether respondents understand the questions and instructions, and whether the meaning of questions is the same for all respondents. Where closed questions are used, piloting will highlight whether sufficient response categories are available, and whether any questions are systematically missed by respondents.

When conducting a pilot, the same procedure as as that to be used in the main survey should be followed; this will highlight potential problems such as poor response.

Covering letter

All participants should be given a covering letter including information such as the organization behind the study, including the contact name and address of the researcher, details of how and why the respondent was selected, the aims of the study, any potential benefits or harm resulting from the study, and what will happen to the information provided. The covering letter should both encourage the respondent to participate in the study and also meet the requirements of informed consent (see below).

The concept of sample is intrinsic to survey research. Usually, it is impractical and uneconomical to collect data from every single person in a given population; a sample of the population has to be selected [ 7 ]. This is illustrated in the following hypothetical example. A hospital wants to conduct a satisfaction survey of the 1000 patients discharged in the previous month; however, as it is too costly to survey each patient, a sample has to be selected. In this example, the researcher will have a list of the population members to be surveyed (sampling frame). It is important to ensure that this list is both up-to date and has been obtained from a reliable source.

The method by which the sample is selected from a sampling frame is integral to the external validity of a survey: the sample has to be representative of the larger population to obtain a composite profile of that population [ 8 ].

There are methodological factors to consider when deciding who will be in a sample: How will the sample be selected? What is the optimal sample size to minimize sampling error? How can response rates be maximized?

The survey methods discussed below influence how a sample is selected and the size of the sample. There are two categories of sampling: random and non-random sampling, with a number of sampling selection techniques contained within the two categories. The principal techniques are described here [ 9 ].

Random sampling

Generally, random sampling is employed when quantitative methods are used to collect data (e.g. questionnaires). Random sampling allows the results to be generalized to the larger population and statistical analysis performed if appropriate. The most stringent technique is simple random sampling. Using this technique, each individual within the chosen population is selected by chance and is equally as likely to be picked as anyone else. Referring back to the hypothetical example, each patient is given a serial identifier and then an appropriate number of the 1000 population members are randomly selected. This is best done using a random number table, which can be generated using computer software (a free on-line randomizer can be found at http://www.randomizer.org/index.htm ).

Alternative random sampling techniques are briefly described. In systematic sampling, individuals to be included in the sample are chosen at equal intervals from the population; using the earlier example, every fifth patient discharged from hospital would be included in the survey. Stratified sampling selects a specific group and then a random sample is selected. Using our example, the hospital may decide only to survey older surgical patients. Bigger surveys may employ cluster sampling, which randomly assigns groups from a large population and then surveys everyone within the groups, a technique often used in national-scale studies.

Non-random sampling

Non-random sampling is commonly applied when qualitative methods (e.g. focus groups and interviews) are used to collect data, and is typically used for exploratory work. Non-random sampling deliberately targets individuals within a population. There are three main techniques. (1) purposive sampling: a specific population is identified and only its members are included in the survey; using our example above, the hospital may decide to survey only patients who had an appendectomy. (2) Convenience sampling: the sample is made up of the individuals who are the easiest to recruit. Finally, (3) snowballing: the sample is identified as the survey progresses; as one individual is surveyed he or she is invited to recommend others to be surveyed.

It is important to use the right method of sampling and to be aware of the limitations and statistical implications of each. The need to ensure that the sample is representative of the larger population was highlighted earlier and, alongside the sampling method, the degree of sampling error should be considered. Sampling error is the probability that any one sample is not completely representative of the population from which it has been drawn [ 9 ]. Although sampling error cannot be eliminated entirely, the sampling technique chosen will influence the extent of the error. Simple random sampling will give a closer estimate of the population than a convenience sample of individuals who just happened to be in the right place at the right time.

Sample size

What sample size is required for a survey? There is no definitive answer to this question: large samples with rigorous selection are more powerful as they will yield more accurate results, but data collection and analysis will be proportionately more time consuming and expensive. Essentially, the target sample size for a survey depends on three main factors: the resources available, the aim of the study, and the statistical quality needed for the survey. For ‘qualitative’ surveys using focus groups or interviews, the sample size needed will be smaller than if quantitative data is collected by questionnaire. If statistical analysis is to be performed on the data then sample size calculations should be conducted. This can be done using computer packages such as G * Power [ 10 ]; however, those with little statistical knowledge should consult a statistician. For practical recommendations on sample size, the set of survey guidelines developed by the UK Department of Health [ 11 ] should be consulted.

Larger samples give a better estimate of the population but it can be difficult to obtain an adequate number of responses. It is rare that everyone asked to participate in the survey will reply. To ensure a sufficient number of responses, include an estimated non-response rate in the sample size calculations.

Response rates are a potential source of bias. The results from a survey with a large non-response rate could be misleading and only representative of those who replied. French [ 12 ] reported that non-responders to patient satisfaction surveys are less likely to be satisfied than people who reply. It is unwise to define a level above which a response rate is acceptable, as this depends on many local factors; however, an achievable and acceptable rate is ∼75% for interviews and 65% for self-completion postal questionnaires [ 9 , 13 ]. In any study, the final response rate should be reported with the results; potential differences between the respondents and non-respondents should be explicitly explored and their implications discussed.

There are techniques to increase response rates. A questionnaire must be concise and easy to understand, reminders should be sent out, and method of recruitment should be carefully considered. Sitzia and Wood [ 13 ] found that participants recruited by mail or who had to respond by mail had a lower mean response rate (67%) than participants who were recruited personally (mean response 76.7%). A most useful review of methods to maximize response rates in postal surveys has recently been published [ 14 ].

Researchers should approach data collection in a rigorous and ethical manner. The following information must be clearly recorded:

How, where, how many times, and by whom potential respondents were contacted.

How many people were approached and how many of those agreed to participate.

How did those who agreed to participate differ from those who refused with regard to characteristics of interest in the study, for example how were they identified, where were they approached, and what was their gender, age, and features of their illness or health care.

How was the survey administered (e.g. telephone interview).

What was the response rate (i.e. the number of usable data sets as a proportion of the number of people approached).

The purpose of all analyses is to summarize data so that it is easily understood and provides the answers to our original questions: ‘In order to do this researchers must carefully examine their data; they should become friends with their data’ [ 15 ]. Researchers must prepare to spend substantial time on the data analysis phase of a survey (and this should be built into the project plan). When analysis is rushed, often important aspects of the data are missed and sometimes the wrong analyses are conducted, leading to both inaccurate results and misleading conclusions [ 16 ]. However, and this point cannot be stressed strongly enough, researchers must not engage in data dredging, a practice that can arise especially in studies in which large numbers of dependent variables can be related to large numbers of independent variables (outcomes). When large numbers of possible associations in a dataset are reviewed at P < 0.05, one in 20 of the associations by chance will appear ‘statistically significant’; in datasets where only a few real associations exist, testing at this significance level will result in the large majority of findings still being false positives [ 17 ].

The method of data analysis will depend on the design of the survey and should have been carefully considered in the planning stages of the survey. Data collected by qualitative methods should be analysed using established methods such as content analysis [ 18 ], and where quantitative methods have been used appropriate statistical tests can be applied. Describing methods of analysis here would be unproductive as a multitude of introductory textbooks and on-line resources are available to help with simple analyses of data (e.g. [ 19 , 20 ]). For advanced analysis a statistician should be consulted.

When reporting survey research, it is essential that a number of key points are covered (though the length and depth of reporting will be dependent upon journal style). These key points are presented as a ‘checklist’ below:

Explain the purpose or aim of the research, with the explicit identification of the research question.

Explain why the research was necessary and place the study in context, drawing upon previous work in relevant fields (the literature review).

State the chosen research method or methods, and justify why this method was chosen.

Describe the research tool. If an existing tool is used, briefly state its psychometric properties and provide references to the original development work. If a new tool is used, you should include an entire section describing the steps undertaken to develop and test the tool, including results of psychometric testing.

Describe how the sample was selected and how data were collected, including:

How were potential subjects identified?

How many and what type of attempts were made to contact subjects?

Who approached potential subjects?

Where were potential subjects approached?

How was informed consent obtained?

How many agreed to participate?

How did those who agreed differ from those who did not agree?

What was the response rate?

Describe and justify the methods and tests used for data analysis.

Present the results of the research. The results section should be clear, factual, and concise.

Interpret and discuss the findings. This ‘discussion’ section should not simply reiterate results; it should provide the author’s critical reflection upon both the results and the processes of data collection. The discussion should assess how well the study met the research question, should describe the problems encountered in the research, and should honestly judge the limitations of the work.

Present conclusions and recommendations.

The researcher needs to tailor the research report to meet:

The expectations of the specific audience for whom the work is being written.

The conventions that operate at a general level with respect to the production of reports on research in the social sciences.

Anyone involved in collecting data from patients has an ethical duty to respect each individual participant’s autonomy. Any survey should be conducted in an ethical manner and one that accords with best research practice. Two important ethical issues to adhere to when conducting a survey are confidentiality and informed consent.

The respondent’s right to confidentiality should always be respected and any legal requirements on data protection adhered to. In the majority of surveys, the patient should be fully informed about the aims of the survey, and the patient’s consent to participate in the survey must be obtained and recorded.

The professional bodies listed below, among many others, provide guidance on the ethical conduct of research and surveys.

American Psychological Association: http://www.apa.org

British Psychological Society: http://www.bps.org.uk

British Medical Association: http://www.bma.org.uk .

UK General Medical Council: http://www.gmc-uk.org

American Medical Association: http://www.ama-assn.org

UK Royal College of Nursing: http://www.rcn.org.uk

UK Department of Health: http://www.doh.gov

Survey research demands the same standards in research practice as any other research approach, and journal editors and the broader research community will judge a report of survey research with the same level of rigour as any other research report. This is not to say that survey research need be particularly difficult or complex; the point to emphasize is that researchers should be aware of the steps required in survey research, and should be systematic and thoughtful in the planning, execution, and reporting of the project. Above all, survey research should not be seen as an easy, ‘quick and dirty’ option; such work may adequately fulfil local needs (e.g. a quick survey of hospital staff satisfaction), but will not stand up to academic scrutiny and will not be regarded as having much value as a contribution to knowledge.

Address reprint requests to John Sitzia, Research Department, Worthing Hospital, Lyndhurst Road, Worthing BN11 2DH, West Sussex, UK. E-mail: [email protected]

London School of Economics, UK. Http://booth.lse.ac.uk/ (accessed 15 January 2003 ).

Vernon A. A Quaker Businessman: Biography of Joseph Rowntree (1836–1925) . London: Allen & Unwin, 1958 .

Denscombe M. The Good Research Guide: For Small-scale Social Research Projects . Buckingham: Open University Press, 1998 .

Robson C. Real World Research: A Resource for Social Scientists and Practitioner-researchers . Oxford: Blackwell Publishers, 1993 .

Streiner DL, Norman GR. Health Measurement Scales: A Practical Guide to their Development and Use . Oxford: Oxford University Press, 1995 .

Sitzia J. How valid and reliable are patient satisfaction data? An analysis of 195 studies. Int J Qual Health Care 1999 ; 11: 319 –328.

Bowling A. Research Methods in Health. Investigating Health and Health Services . Buckingham: Open University Press, 2002 .

American Statistical Association, USA. Http://www.amstat.org (accessed 9 December 2002 ).

Arber S. Designing samples. In: Gilbert N, ed. Researching Social Life . London: SAGE Publications, 2001 .

Heinrich Heine University, Dusseldorf, Germany. Http://www.psycho.uni-duesseldorf.de/aap/projects/gpower/index.html (accessed 12 December 2002 ).

Department of Health, England. Http://www.doh.gov.uk/acutesurvey/index.htm (accessed 12 December 2002 ).

French K. Methodological considerations in hospital patient opinion surveys. Int J Nurs Stud 1981 ; 18: 7 –32.

Sitzia J, Wood N. Response rate in patient satisfaction research: an analysis of 210 published studies. Int J Qual Health Care 1998 ; 10: 311 –317.

Edwards P, Roberts I, Clarke M et al. Increasing response rates to postal questionnaires: systematic review. Br Med J 2002 ; 324: 1183 .

Wright DB. Making friends with our data: improving how statistical results are reported. Br J Educ Psychol 2003 ; in press.

Wright DB, Kelley K. Analysing and reporting data. In: Michie S, Abraham C, eds. Health Psychology in Practice . London: SAGE Publications, 2003 ; in press.

Davey Smith G, Ebrahim S. Data dredging, bias, or confounding. Br Med J 2002 ; 325: 1437 –1438.

Morse JM, Field PA. Nursing Research: The Application of Qualitative Approaches . London: Chapman and Hall, 1996 .

Wright DB. Understanding Statistics: An Introduction for the Social Sciences . London: SAGE Publications, 1997 .

Sportscience, New Zealand. Http://www.sportsci.org/resource/stats/index.html (accessed 12 December 2002 ).

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• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyse the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyse the survey results, step 6: write up the survey results, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: Investigating the experiences and characteristics of different social groups
• Market research: Finding out what customers think about products, services, and companies
• Health research: Collecting data from patients about symptoms and treatments
• Politics: Measuring public opinion about parties and policies
• Psychology: Researching personality traits, preferences, and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and longitudinal studies , where you survey the same sample several times over an extended period.

Prevent plagiarism, run a free check.

Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalised to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every university student in the UK. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, online, or in person, and respondents fill it out themselves
• An interview , where the researcher asks a set of questions by phone or in person and records the responses

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by post is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g., residents of a specific region).
• The response rate is often low.

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping centre or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g., the opinions of a shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data : the researcher records each response as a category or rating and statistically analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g., yes/no or agree/disagree )
• A scale (e.g., a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g., age categories)
• A list of options with multiple answers possible (e.g., leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analysed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an ‘other’ field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by post, online, or in person.

There are many methods of analysing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also cleanse the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analysed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

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Home » Survey Research – Types, Methods, Examples

Survey Research – Types, Methods, Examples

Table of Contents

Survey Research

Definition:

Survey Research is a quantitative research method that involves collecting standardized data from a sample of individuals or groups through the use of structured questionnaires or interviews. The data collected is then analyzed statistically to identify patterns and relationships between variables, and to draw conclusions about the population being studied.

Survey research can be used to answer a variety of questions, including:

• What are people’s opinions about a certain topic?
• What are people’s experiences with a certain product or service?
• What are people’s beliefs about a certain issue?

Survey Research Methods

Survey Research Methods are as follows:

• Telephone surveys: A survey research method where questions are administered to respondents over the phone, often used in market research or political polling.
• Face-to-face surveys: A survey research method where questions are administered to respondents in person, often used in social or health research.
• Mail surveys: A survey research method where questionnaires are sent to respondents through mail, often used in customer satisfaction or opinion surveys.
• Online surveys: A survey research method where questions are administered to respondents through online platforms, often used in market research or customer feedback.
• Email surveys: A survey research method where questionnaires are sent to respondents through email, often used in customer satisfaction or opinion surveys.
• Mixed-mode surveys: A survey research method that combines two or more survey modes, often used to increase response rates or reach diverse populations.
• Computer-assisted surveys: A survey research method that uses computer technology to administer or collect survey data, often used in large-scale surveys or data collection.
• Interactive voice response surveys: A survey research method where respondents answer questions through a touch-tone telephone system, often used in automated customer satisfaction or opinion surveys.
• Mobile surveys: A survey research method where questions are administered to respondents through mobile devices, often used in market research or customer feedback.
• Group-administered surveys: A survey research method where questions are administered to a group of respondents simultaneously, often used in education or training evaluation.
• Web-intercept surveys: A survey research method where questions are administered to website visitors, often used in website or user experience research.
• In-app surveys: A survey research method where questions are administered to users of a mobile application, often used in mobile app or user experience research.
• Social media surveys: A survey research method where questions are administered to respondents through social media platforms, often used in social media or brand awareness research.
• SMS surveys: A survey research method where questions are administered to respondents through text messaging, often used in customer feedback or opinion surveys.
• IVR surveys: A survey research method where questions are administered to respondents through an interactive voice response system, often used in automated customer feedback or opinion surveys.
• Mixed-method surveys: A survey research method that combines both qualitative and quantitative data collection methods, often used in exploratory or mixed-method research.
• Drop-off surveys: A survey research method where respondents are provided with a survey questionnaire and asked to return it at a later time or through a designated drop-off location.
• Intercept surveys: A survey research method where respondents are approached in public places and asked to participate in a survey, often used in market research or customer feedback.
• Hybrid surveys: A survey research method that combines two or more survey modes, data sources, or research methods, often used in complex or multi-dimensional research questions.

Types of Survey Research

There are several types of survey research that can be used to collect data from a sample of individuals or groups. following are Types of Survey Research:

• Cross-sectional survey: A type of survey research that gathers data from a sample of individuals at a specific point in time, providing a snapshot of the population being studied.
• Longitudinal survey: A type of survey research that gathers data from the same sample of individuals over an extended period of time, allowing researchers to track changes or trends in the population being studied.
• Panel survey: A type of longitudinal survey research that tracks the same sample of individuals over time, typically collecting data at multiple points in time.
• Epidemiological survey: A type of survey research that studies the distribution and determinants of health and disease in a population, often used to identify risk factors and inform public health interventions.
• Observational survey: A type of survey research that collects data through direct observation of individuals or groups, often used in behavioral or social research.
• Correlational survey: A type of survey research that measures the degree of association or relationship between two or more variables, often used to identify patterns or trends in data.
• Experimental survey: A type of survey research that involves manipulating one or more variables to observe the effect on an outcome, often used to test causal hypotheses.
• Descriptive survey: A type of survey research that describes the characteristics or attributes of a population or phenomenon, often used in exploratory research or to summarize existing data.
• Diagnostic survey: A type of survey research that assesses the current state or condition of an individual or system, often used in health or organizational research.
• Explanatory survey: A type of survey research that seeks to explain or understand the causes or mechanisms behind a phenomenon, often used in social or psychological research.
• Process evaluation survey: A type of survey research that measures the implementation and outcomes of a program or intervention, often used in program evaluation or quality improvement.
• Impact evaluation survey: A type of survey research that assesses the effectiveness or impact of a program or intervention, often used to inform policy or decision-making.
• Customer satisfaction survey: A type of survey research that measures the satisfaction or dissatisfaction of customers with a product, service, or experience, often used in marketing or customer service research.
• Market research survey: A type of survey research that collects data on consumer preferences, behaviors, or attitudes, often used in market research or product development.
• Public opinion survey: A type of survey research that measures the attitudes, beliefs, or opinions of a population on a specific issue or topic, often used in political or social research.
• Behavioral survey: A type of survey research that measures actual behavior or actions of individuals, often used in health or social research.
• Attitude survey: A type of survey research that measures the attitudes, beliefs, or opinions of individuals, often used in social or psychological research.
• Opinion poll: A type of survey research that measures the opinions or preferences of a population on a specific issue or topic, often used in political or media research.
• Ad hoc survey: A type of survey research that is conducted for a specific purpose or research question, often used in exploratory research or to answer a specific research question.

Types Based on Methodology

Based on Methodology Survey are divided into two Types:

Quantitative Survey Research

Qualitative survey research.

Quantitative survey research is a method of collecting numerical data from a sample of participants through the use of standardized surveys or questionnaires. The purpose of quantitative survey research is to gather empirical evidence that can be analyzed statistically to draw conclusions about a particular population or phenomenon.

In quantitative survey research, the questions are structured and pre-determined, often utilizing closed-ended questions, where participants are given a limited set of response options to choose from. This approach allows for efficient data collection and analysis, as well as the ability to generalize the findings to a larger population.

Quantitative survey research is often used in market research, social sciences, public health, and other fields where numerical data is needed to make informed decisions and recommendations.

Qualitative survey research is a method of collecting non-numerical data from a sample of participants through the use of open-ended questions or semi-structured interviews. The purpose of qualitative survey research is to gain a deeper understanding of the experiences, perceptions, and attitudes of participants towards a particular phenomenon or topic.

In qualitative survey research, the questions are open-ended, allowing participants to share their thoughts and experiences in their own words. This approach allows for a rich and nuanced understanding of the topic being studied, and can provide insights that are difficult to capture through quantitative methods alone.

Qualitative survey research is often used in social sciences, education, psychology, and other fields where a deeper understanding of human experiences and perceptions is needed to inform policy, practice, or theory.

Data Analysis Methods

There are several Survey Research Data Analysis Methods that researchers may use, including:

• Descriptive statistics: This method is used to summarize and describe the basic features of the survey data, such as the mean, median, mode, and standard deviation. These statistics can help researchers understand the distribution of responses and identify any trends or patterns.
• Inferential statistics: This method is used to make inferences about the larger population based on the data collected in the survey. Common inferential statistical methods include hypothesis testing, regression analysis, and correlation analysis.
• Factor analysis: This method is used to identify underlying factors or dimensions in the survey data. This can help researchers simplify the data and identify patterns and relationships that may not be immediately apparent.
• Cluster analysis: This method is used to group similar respondents together based on their survey responses. This can help researchers identify subgroups within the larger population and understand how different groups may differ in their attitudes, behaviors, or preferences.
• Structural equation modeling: This method is used to test complex relationships between variables in the survey data. It can help researchers understand how different variables may be related to one another and how they may influence one another.
• Content analysis: This method is used to analyze open-ended responses in the survey data. Researchers may use software to identify themes or categories in the responses, or they may manually review and code the responses.
• Text mining: This method is used to analyze text-based survey data, such as responses to open-ended questions. Researchers may use software to identify patterns and themes in the text, or they may manually review and code the text.

Applications of Survey Research

Here are some common applications of survey research:

• Market Research: Companies use survey research to gather insights about customer needs, preferences, and behavior. These insights are used to create marketing strategies and develop new products.
• Public Opinion Research: Governments and political parties use survey research to understand public opinion on various issues. This information is used to develop policies and make decisions.
• Social Research: Survey research is used in social research to study social trends, attitudes, and behavior. Researchers use survey data to explore topics such as education, health, and social inequality.
• Academic Research: Survey research is used in academic research to study various phenomena. Researchers use survey data to test theories, explore relationships between variables, and draw conclusions.
• Customer Satisfaction Research: Companies use survey research to gather information about customer satisfaction with their products and services. This information is used to improve customer experience and retention.
• Employee Surveys: Employers use survey research to gather feedback from employees about their job satisfaction, working conditions, and organizational culture. This information is used to improve employee retention and productivity.
• Health Research: Survey research is used in health research to study topics such as disease prevalence, health behaviors, and healthcare access. Researchers use survey data to develop interventions and improve healthcare outcomes.

Examples of Survey Research

Here are some real-time examples of survey research:

• COVID-19 Pandemic Surveys: Since the outbreak of the COVID-19 pandemic, surveys have been conducted to gather information about public attitudes, behaviors, and perceptions related to the pandemic. Governments and healthcare organizations have used this data to develop public health strategies and messaging.
• Political Polls During Elections: During election seasons, surveys are used to measure public opinion on political candidates, policies, and issues in real-time. This information is used by political parties to develop campaign strategies and make decisions.
• Customer Feedback Surveys: Companies often use real-time customer feedback surveys to gather insights about customer experience and satisfaction. This information is used to improve products and services quickly.
• Event Surveys: Organizers of events such as conferences and trade shows often use surveys to gather feedback from attendees in real-time. This information can be used to improve future events and make adjustments during the current event.
• Website and App Surveys: Website and app owners use surveys to gather real-time feedback from users about the functionality, user experience, and overall satisfaction with their platforms. This feedback can be used to improve the user experience and retain customers.
• Employee Pulse Surveys: Employers use real-time pulse surveys to gather feedback from employees about their work experience and overall job satisfaction. This feedback is used to make changes in real-time to improve employee retention and productivity.

Survey Sample

Purpose of survey research.

The purpose of survey research is to gather data and insights from a representative sample of individuals. Survey research allows researchers to collect data quickly and efficiently from a large number of people, making it a valuable tool for understanding attitudes, behaviors, and preferences.

Here are some common purposes of survey research:

• Descriptive Research: Survey research is often used to describe characteristics of a population or a phenomenon. For example, a survey could be used to describe the characteristics of a particular demographic group, such as age, gender, or income.
• Exploratory Research: Survey research can be used to explore new topics or areas of research. Exploratory surveys are often used to generate hypotheses or identify potential relationships between variables.
• Explanatory Research: Survey research can be used to explain relationships between variables. For example, a survey could be used to determine whether there is a relationship between educational attainment and income.
• Evaluation Research: Survey research can be used to evaluate the effectiveness of a program or intervention. For example, a survey could be used to evaluate the impact of a health education program on behavior change.
• Monitoring Research: Survey research can be used to monitor trends or changes over time. For example, a survey could be used to monitor changes in attitudes towards climate change or political candidates over time.

When to use Survey Research

there are certain circumstances where survey research is particularly appropriate. Here are some situations where survey research may be useful:

• When the research question involves attitudes, beliefs, or opinions: Survey research is particularly useful for understanding attitudes, beliefs, and opinions on a particular topic. For example, a survey could be used to understand public opinion on a political issue.
• When the research question involves behaviors or experiences: Survey research can also be useful for understanding behaviors and experiences. For example, a survey could be used to understand the prevalence of a particular health behavior.
• When a large sample size is needed: Survey research allows researchers to collect data from a large number of people quickly and efficiently. This makes it a useful method when a large sample size is needed to ensure statistical validity.
• When the research question is time-sensitive: Survey research can be conducted quickly, which makes it a useful method when the research question is time-sensitive. For example, a survey could be used to understand public opinion on a breaking news story.
• When the research question involves a geographically dispersed population: Survey research can be conducted online, which makes it a useful method when the population of interest is geographically dispersed.

How to Conduct Survey Research

Conducting survey research involves several steps that need to be carefully planned and executed. Here is a general overview of the process:

• Define the research question: The first step in conducting survey research is to clearly define the research question. The research question should be specific, measurable, and relevant to the population of interest.
• Develop a survey instrument : The next step is to develop a survey instrument. This can be done using various methods, such as online survey tools or paper surveys. The survey instrument should be designed to elicit the information needed to answer the research question, and should be pre-tested with a small sample of individuals.
• Select a sample : The sample is the group of individuals who will be invited to participate in the survey. The sample should be representative of the population of interest, and the size of the sample should be sufficient to ensure statistical validity.
• Administer the survey: The survey can be administered in various ways, such as online, by mail, or in person. The method of administration should be chosen based on the population of interest and the research question.
• Analyze the data: Once the survey data is collected, it needs to be analyzed. This involves summarizing the data using statistical methods, such as frequency distributions or regression analysis.
• Draw conclusions: The final step is to draw conclusions based on the data analysis. This involves interpreting the results and answering the research question.

Advantages of Survey Research

There are several advantages to using survey research, including:

• Efficient data collection: Survey research allows researchers to collect data quickly and efficiently from a large number of people. This makes it a useful method for gathering information on a wide range of topics.
• Standardized data collection: Surveys are typically standardized, which means that all participants receive the same questions in the same order. This ensures that the data collected is consistent and reliable.
• Cost-effective: Surveys can be conducted online, by mail, or in person, which makes them a cost-effective method of data collection.
• Anonymity: Participants can remain anonymous when responding to a survey. This can encourage participants to be more honest and open in their responses.
• Easy comparison: Surveys allow for easy comparison of data between different groups or over time. This makes it possible to identify trends and patterns in the data.
• Versatility: Surveys can be used to collect data on a wide range of topics, including attitudes, beliefs, behaviors, and preferences.

Limitations of Survey Research

Here are some of the main limitations of survey research:

• Limited depth: Surveys are typically designed to collect quantitative data, which means that they do not provide much depth or detail about people’s experiences or opinions. This can limit the insights that can be gained from the data.
• Potential for bias: Surveys can be affected by various biases, including selection bias, response bias, and social desirability bias. These biases can distort the results and make them less accurate.
• L imited validity: Surveys are only as valid as the questions they ask. If the questions are poorly designed or ambiguous, the results may not accurately reflect the respondents’ attitudes or behaviors.
• Limited generalizability : Survey results are only generalizable to the population from which the sample was drawn. If the sample is not representative of the population, the results may not be generalizable to the larger population.
• Limited ability to capture context: Surveys typically do not capture the context in which attitudes or behaviors occur. This can make it difficult to understand the reasons behind the responses.
• Limited ability to capture complex phenomena: Surveys are not well-suited to capture complex phenomena, such as emotions or the dynamics of interpersonal relationships.

Following is an example of a Survey Sample:

Welcome to our Survey Research Page! We value your opinions and appreciate your participation in this survey. Please answer the questions below as honestly and thoroughly as possible.

1. What is your age?

• A) Under 18
• G) 65 or older

2. What is your highest level of education completed?

• A) Less than high school
• B) High school or equivalent
• C) Some college or technical school
• D) Bachelor’s degree
• E) Graduate or professional degree

3. What is your current employment status?

• A) Employed full-time
• B) Employed part-time
• C) Self-employed
• D) Unemployed

4. How often do you use the internet per day?

•  A) Less than 1 hour
• B) 1-3 hours
• C) 3-5 hours
• D) 5-7 hours
• E) More than 7 hours

5. How often do you engage in social media per day?

6. Have you ever participated in a survey research study before?

7. If you have participated in a survey research study before, how was your experience?

• A) Excellent
• E) Very poor

8. What are some of the topics that you would be interested in participating in a survey research study about?

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

9. How often would you be willing to participate in survey research studies?

• A) Once a week
• B) Once a month
• C) Once every 6 months
• D) Once a year

10. Any additional comments or suggestions?

Thank you for taking the time to complete this survey. Your feedback is important to us and will help us improve our survey research efforts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A Comprehensive Guide to Survey Research Methodologies

For decades, researchers and businesses have used survey research to produce statistical data and explore ideas. The survey process is simple, ask questions and analyze the responses to make decisions. Data is what makes the difference between a valid and invalid statement and as the American statistician, W. Edwards Deming said:

“Without data, you’re just another person with an opinion.” - W. Edwards Deming

In this article, we will discuss what survey research is, its brief history, types, common uses, benefits, and the step-by-step process of designing a survey.

What is Survey Research

A survey is a research method that is used to collect data from a group of respondents in order to gain insights and information regarding a particular subject. It’s an excellent method to gather opinions and understand how and why people feel a certain way about different situations and contexts.

Brief History of Survey Research

Survey research may have its roots in the American and English “social surveys” conducted around the turn of the 20th century. The surveys were mainly conducted by researchers and reformers to document the extent of social issues such as poverty. ( 1 ) Despite being a relatively young field to many scientific domains, survey research has experienced three stages of development ( 2 ):

-       First Era (1930-1960)

-       Second Era (1960-1990)

-       Third Era (1990 onwards)

Over the years, survey research adapted to the changing times and technologies. By exploiting the latest technologies, researchers can gain access to the right population from anywhere in the world, analyze the data like never before, and extract useful information.

Survey Research Methods & Types

Survey research can be classified into seven categories based on objective, data sources, methodology, deployment method, and frequency of deployment.

Surveys based on Objective

Exploratory survey research.

Exploratory survey research is aimed at diving deeper into research subjects and finding out more about their context. It’s important for marketing or business strategy and the focus is to discover ideas and insights instead of gathering statistical data.

Generally, exploratory survey research is composed of open-ended questions that allow respondents to express their thoughts and perspectives. The final responses present information from various sources that can lead to fresh initiatives.

Predictive Survey Research

Predictive survey research is also called causal survey research. It’s preplanned, structured, and quantitative in nature. It’s often referred to as conclusive research as it tries to explain the cause-and-effect relationship between different variables. The objective is to understand which variables are causes and which are effects and the nature of the relationship between both variables.

Descriptive Survey Research

Descriptive survey research is largely observational and is ideal for gathering numeric data. Due to its quantitative nature, it’s often compared to exploratory survey research. The difference between the two is that descriptive research is structured and pre-planned.

 The idea behind descriptive research is to describe the mindset and opinion of a particular group of people on a given subject. The questions are every day multiple choices and users must choose from predefined categories. With predefined choices, you don’t get unique insights, rather, statistically inferable data.

Survey Research Types based on Concept Testing

Monadic concept testing.

Monadic testing is a survey research methodology in which the respondents are split into multiple groups and ask each group questions about a separate concept in isolation. Generally, monadic surveys are hyper-focused on a particular concept and shorter in duration. The important thing in monadic surveys is to avoid getting off-topic or exhausting the respondents with too many questions.

Sequential Monadic Concept Testing

Another approach to monadic testing is sequential monadic testing. In sequential monadic surveys, groups of respondents are surveyed in isolation. However, instead of surveying three groups on three different concepts, the researchers survey the same groups of people on three distinct concepts one after another. In a sequential monadic survey, at least two topics are included (in random order), and the same questions are asked for each concept to eliminate bias.

Based on Data Source

Primary data.

Data obtained directly from the source or target population is referred to as primary survey data. When it comes to primary data collection, researchers usually devise a set of questions and invite people with knowledge of the subject to respond. The main sources of primary data are interviews, questionnaires, surveys, and observation methods.

 Compared to secondary data, primary data is gathered from first-hand sources and is more reliable. However, the process of primary data collection is both costly and time-consuming.

Secondary Data

Survey research is generally used to collect first-hand information from a respondent. However, surveys can also be designed to collect and process secondary data. It’s collected from third-party sources or primary sources in the past.

 This type of data is usually generic, readily available, and cheaper than primary data collection. Some common sources of secondary data are books, data collected from older surveys, online data, and data from government archives. Beware that you might compromise the validity of your findings if you end up with irrelevant or inflated data.

Based on Research Method

Quantitative research.

Quantitative research is a popular research methodology that is used to collect numeric data in a systematic investigation. It’s frequently used in research contexts where statistical data is required, such as sciences or social sciences. Quantitative research methods include polls, systematic observations, and face-to-face interviews.

Qualitative Research

Qualitative research is a research methodology where you collect non-numeric data from research participants. In this context, the participants are not restricted to a specific system and provide open-ended information. Some common qualitative research methods include focus groups, one-on-one interviews, observations, and case studies.

Based on Deployment Method

Online surveys.

With technology advancing rapidly, the most popular method of survey research is an online survey. With the internet, you can not only reach a broader audience but also design and customize a survey and deploy it from anywhere. Online surveys have outperformed offline survey methods as they are less expensive and allow researchers to easily collect and analyze data from a large sample.

Paper or Print Surveys

As the name suggests, paper or print surveys use the traditional paper and pencil approach to collect data. Before the invention of computers, paper surveys were the survey method of choice.

Though many would assume that surveys are no longer conducted on paper, it's still a reliable method of collecting information during field research and data collection. However, unlike online surveys, paper surveys are expensive and require extra human resources.

Telephonic Surveys

Telephonic surveys are conducted over telephones where a researcher asks a series of questions to the respondent on the other end. Contacting respondents over a telephone requires less effort, human resources, and is less expensive.

What makes telephonic surveys debatable is that people are often reluctant in giving information over a phone call. Additionally, the success of such surveys depends largely on whether people are willing to invest their time on a phone call answering questions.

One-on-one Surveys

One-on-one surveys also known as face-to-face surveys are interviews where the researcher and respondent. Interacting directly with the respondent introduces the human factor into the survey.

Face-to-face interviews are useful when the researcher wants to discuss something personal with the respondent. The response rates in such surveys are always higher as the interview is being conducted in person. However, these surveys are quite expensive and the success of these depends on the knowledge and experience of the researcher.

Based on Distribution

The easiest and most common way of conducting online surveys is sending out an email. Sending out surveys via emails has a higher response rate as your target audience already knows about your brand and is likely to engage.

Buy Survey Responses

Purchasing survey responses also yields higher responses as the responders signed up for the survey. Businesses often purchase survey samples to conduct extensive research. Here, the target audience is often pre-screened to check if they're qualified to take part in the research.

Embedding Survey on a Website

Embedding surveys on a website is another excellent way to collect information. It allows your website visitors to take part in a survey without ever leaving the website and can be done while a person is entering or exiting the website.

Post the Survey on Social Media

Social media is an excellent medium to reach abroad range of audiences. You can publish your survey as a link on social media and people who are following the brand can take part and answer questions.

Based on Frequency of Deployment

Cross-sectional studies.

Cross-sectional studies are administered to a small sample from a large population within a short period of time. This provides researchers a peek into what the respondents are thinking at a given time. The surveys are usually short, precise, and specific to a particular situation.

Longitudinal Surveys

Longitudinal surveys are an extension of cross-sectional studies where researchers make an observation and collect data over extended periods of time. This type of survey can be further divided into three types:

-       Trend surveys are employed to allow researchers to understand the change in the thought process of the respondents over some time.

-       Panel surveys are administered to the same group of people over multiple years. These are usually expensive and researchers must stick to their panel to gather unbiased opinions.

-       In cohort surveys, researchers identify a specific category of people and regularly survey them. Unlike panel surveys, the same people do not need to take part over the years, but each individual must fall into the researcher’s primary interest category.

Retrospective Survey

Retrospective surveys allow researchers to ask questions to gather data about past events and beliefs of the respondents. Since retrospective surveys also require years of data, they are similar to the longitudinal survey, except retrospective surveys are shorter and less expensive.

Why Should You Conduct Research Surveys?

“In God we trust. All others must bring data” - W. Edwards Deming

 In the information age, survey research is of utmost importance and essential for understanding the opinion of your target population. Whether you’re launching a new product or conducting a social survey, the tool can be used to collect specific information from a defined set of respondents. The data collected via surveys can be further used by organizations to make informed decisions.

Furthermore, compared to other research methods, surveys are relatively inexpensive even if you’re giving out incentives. Compared to the older methods such as telephonic or paper surveys, online surveys have a smaller cost and the number of responses is higher.

 What makes surveys useful is that they describe the characteristics of a large population. With a larger sample size , you can rely on getting more accurate results. However, you also need honest and open answers for accurate results. Since surveys are also anonymous and the responses remain confidential, respondents provide candid and accurate answers.

Common Uses of a Survey

Surveys are widely used in many sectors, but the most common uses of the survey research include:

-       Market research : surveying a potential market to understand customer needs, preferences, and market demand.

-       Customer Satisfaction: finding out your customer’s opinions about your services, products, or companies .

-       Social research: investigating the characteristics and experiences of various social groups.

-       Health research: collecting data about patients’ symptoms and treatments.

-       Politics: evaluating public opinion regarding policies and political parties.

-       Psychology: exploring personality traits, behaviors, and preferences.

6 Steps to Conduct Survey Research

An organization, person, or company conducts a survey when they need the information to make a decision but have insufficient data on hand. Following are six simple steps that can help you design a great survey.

Step 1: Objective of the Survey

The first step in survey research is defining an objective. The objective helps you define your target population and samples. The target population is the specific group of people you want to collect data from and since it’s rarely possible to survey the entire population, we target a specific sample from it. Defining a survey objective also benefits your respondents by helping them understand the reason behind the survey.

Step 2: Number of Questions

The number of questions or the size of the survey depends on the survey objective. However, it’s important to ensure that there are no redundant queries and the questions are in a logical order. Rephrased and repeated questions in a survey are almost as frustrating as in real life. For a higher completion rate, keep the questionnaire small so that the respondents stay engaged to the very end. The ideal length of an interview is less than 15 minutes. ( 2 )

Step 3: Language and Voice of Questions

While designing a survey, you may feel compelled to use fancy language. However, remember that difficult language is associated with higher survey dropout rates. You need to speak to the respondent in a clear, concise, and neutral manner, and ask simple questions. If your survey respondents are bilingual, then adding an option to translate your questions into another language can also prove beneficial.

Step 4: Type of Questions

In a survey, you can include any type of questions and even both closed-ended or open-ended questions. However, opt for the question types that are the easiest to understand for the respondents, and offer the most value. For example, compared to open-ended questions, people prefer to answer close-ended questions such as MCQs (multiple choice questions)and NPS (net promoter score) questions.

Step 5: User Experience

Designing a great survey is about more than just questions. A lot of researchers underestimate the importance of user experience and how it affects their response and completion rates. An inconsistent, difficult-to-navigate survey with technical errors and poor color choice is unappealing for the respondents. Make sure that your survey is easy to navigate for everyone and if you’re using rating scales, they remain consistent throughout the research study.

Additionally, don’t forget to design a good survey experience for both mobile and desktop users. According to Pew Research Center, nearly half of the smartphone users access the internet mainly from their mobile phones and 14 percent of American adults are smartphone-only internet users. ( 3 )

Step 6: Survey Logic

Last but not least, logic is another critical aspect of the survey design. If the survey logic is flawed, respondents may not continue in the right direction. Make sure to test the logic to ensure that selecting one answer leads to the next logical question instead of a series of unrelated queries.

How to Effectively Use Survey Research with Starlight Analytics

Designing and conducting a survey is almost as much science as it is an art. To craft great survey research, you need technical skills, consider the psychological elements, and have a broad understanding of marketing.

The ultimate goal of the survey is to ask the right questions in the right manner to acquire the right results.

Bringing a new product to the market is a long process and requires a lot of research and analysis. In your journey to gather information or ideas for your business, Starlight Analytics can be an excellent guide. Starlight Analytics' product concept testing helps you measure your product's market demand and refine product features and benefits so you can launch with confidence. The process starts with custom research to design the survey according to your needs, execute the survey, and deliver the key insights on time.

• Survey research in the United States: roots and emergence, 1890-1960 https://searchworks.stanford.edu/view/10733873    
• How to create a survey questionnaire that gets great responses https://luc.id/knowledgehub/how-to-create-a-survey-questionnaire-that-gets-great-responses/    
• Internet/broadband fact sheet https://www.pewresearch.org/internet/fact-sheet/internet-broadband/    

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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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See an example

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

Prevent plagiarism. Run a free check.

To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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• Open access
• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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Assessment Survey and evaluation of LGBT-Psychology in Nigeria: current state and recommendations

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There is no gainsaying that individuals with diverse sexual orientations and gender identities are faced with serious socio-legal, and medical discrimination following the enactment of anti-homosexuality law in Nigeria. However, not much is known of the effort of an organized body of psychology in the country to ensure adequate knowledge and competence among Nigerian psychologists. This article, therefore, appraises the stance of Lesbian, Gay, Bisexual, and Transgender (LGBT) psychology in Nigeria in relation to the cardinal quadrants: Advocacy, Education, Research, and Practice. A multi-method design was adopted to sort for both primary and secondary data. Purposive sampling was adopted to involve 124 practicing psychologists. Findings revealed that the Nigerian psychology curriculum limits its scope to sexual and gender disorders (sexual dysfunction, gender dysphoria, and paraphilic disorders) while missing out on sexual and gender diversity content. Furthermore, the outcome shows that not much is documented on the contribution of the field of psychology to the knowledge of LGBT. Many of the participants had a history (and still) working with LGBT clients and did not have formal LGBT-affirmative training. The study concluded that the integration of LGBT psychology is essential for significant achievement in the space of advocacy, education, research, and professional practices.

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Introduction

The psychology profession has numerous sub-fields albeit course contents bore into existence to excavate and further deepen the area of concern or interests. One of the most emerging course contents in psychology is the lesbian, gay, bisexual, and transgender (LGBT Footnote 1 ) psychology. LGBT psychology is a sub-field of psychology developed to research the scientific understanding surrounding the lives and teach a diverse range of psychological and social perspectives of persons with diverse sexual orientations and gender identities (Balsam et al., 2005 ). However, it is important to note that the emergence of LGBT psychology was accompanied by a series of historical global events.

Historically (before the 1950s), sexually and gender diverse (SGD) persons and communities remained targets of hate violence and backlash from privileged heterosexual persons throughout the world; such that victims were regarded as sick and criminals, and not the perpetrators of violence against the SGD populations. Throughout the 50s and 60s, SGD persons and communities continued to be at risk of psychiatric institutionalization, as well as criminal incarceration, and predisposed to other social consequences, such as losing jobs, and child custody, among others (Glassgold et al., 2007 ). Arguably, the breakthrough into the understanding of SGD people and communities started with the submissions of the article titled “The Homosexual in America” by Donald Webster Cory (Pseudo name for Edward Sagarin) in 1951, which paved the way for further scientific research, understanding, and attitudinal change in the United States of America (USA; Sagarin, 1971 ).

Thereafter, research interest began to grow significantly among the populations. In 1956, Evelyn Hooker won a grant from the National Institute of Mental Health to study the psychology of gay men (Hooker, 1956 ). Many scholars across the globe began to expand their niche research interests at that time (Ardila, 2015 ; Hookers, 1956 ). Domination of similar scientifically proven outcomes was reported across different studies, which culminated in the ordination of the first out-gay ministers by the United Church of Christ in 1972; the formation of Parents and Friends of Lesbians and Gays (PFLAG) in the same year; explosion of political actions through the establishment of National Gay and Lesbian Task Force, the Human Rights Campaign; and the election of openly gay and lesbian representatives into the political space (De Waal & Manion, 2006 ; Hooker, 1956 ).

History and responses to LGBT psychology differ from country to country, and there is no exception to Nigerian history. However, the historical processes and attitudes toward same-sexuality and gender diversity are almost the same across countries (Ardila, 2015 ). The current study assessed the historical events of the Nigerian LGBT in tandem with the reports from a Western country (i.e., the USA) and an African country (i.e., South Africa). Below is the historical timelines across the three countries.

Historically, 1950s, 1980s, and 2000s were considered the era of a dark age for SGD persons living in the USA, South Africa, and Nigeria, respectively. In this context, a dark age is characterized by the absence of scientific inquiry about the phenomenon of discussion. At that time, the understanding and knowledge about the SGD populations were informed by religion, socio-cultural, and subjective rational thoughts. Historically, in the case of Nigeria, the dark era started when the Same-Sex Marriage (Prohibition) Act (SSMPA) of 2013 was signed into law in 2014 (Human Rights Watch, 2016; Thoreson & Cook, 2011 ).

The Renaissance period is a period after the Dark Ages, that is characterized by classical sort of knowledge and findings that are scientifically rooted (Copenhaver, 1992 ). The Renaissance period in the USA was contextualized as a post-publication of the finding of Donald Webster (1951) and Evelyn Hooker ( 1956 ). In South Africa, the Renaissance period was ascribed to when the first LGBT + Civil Society Organization (CSO) was established, which involved the initiatives of some pioneering psychologists and volunteers in Cape Town and Johannesburg (De Waal & Manion, 2006 ; Hoad et al., 2005 ; Reddy et al., 2009 ). In Nigeria, several CSOs and Non-governmental Organisations (NGOs) were established to stimulate, educate, and further deepen the rights of the SGD populations in the country. In 2017 for example, a significant increase was reported in heterosexual dispositions toward SGD persons and communities compared to the 2015 survey polls, such that a 07% and 39% opinion increase was reported in the acceptance of SGD communities, and access to basic (healthcare, education, and housing) amenities, respectively (Olamide, 2018 ).

The liberation phase in the USA continued until 1973 when the American Psychiatric Association removed homosexuality as an “illness” classification in its diagnostic manual. Likewise, the American Psychological Association in 1987 published a major revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM)-III, where the “ego-dystonic homosexuality” classification was removed. Therefore, most organized bodies of the psychology profession have begun to mobilize support, and sensitization (workshops) for the rationale of the removal of diverse sexual orientations as a disorder. Similarly, the South African government in 2016 acknowledged and signed that LGBT + equality rights, which afforded the country global recognition for its progressive constitution that was the first to include non-discrimination based on diverse sexual orientations in the African continent and fifth in the world (Hoad et al., 2005 ; Judge et al., 2008 ; Nel, 2014 ; Republic of South Africa, 1996). Nigeria seems stuck at the renaissance stage, and not much is documented about the efforts of the organized body of psychology, which explains the persistent problems and challenges confronting the SGD persons and communities to date (Human Rights Watch, 2016).

In Nigeria, there is ambivalence in the global position of an organized body of the psychology profession and the sociopolitical stance. Table 1 below shows the summary of the current social and legal context and the roles of organized institutions.

The Nigerian government passed the anti-homosexuality law on January 7, 2014. The same-sex marriage (prohibition) bill signed into law criminalizes any form of civil union between persons of the same sex, punishable under the law (Okuefuna, 2016 ). The law stipulated that persons engaged in same-sex acts in the country are liable for being imprisoned for 14 years. The law also criminalizes any form of support to persons of diverse sexual orientations. The offense is punishable under the law with 10 years of imprisonment. Similarly, an anti-homosexuality law was earlier adopted in 1999 by twelve northern states (Bauchi, Borno, Gombe, Jigawa, Kaduna, Kano, Katsina, Kebbi, Niger, Sokoto, Yobe, and Zamfara) of Nigeria under the auspice of the Sharia law. The adoption of the Islamic legal systems by the 12 Northern States is a legacy punishment for offenders of the same sexuality among the Muslims in the region.

However, the position of the organized body of psychology and psychiatry posited that people with diverse sexual orientations do not suffer from mental health problems (depathologization) but are minority groups that require support (APA, 2010 : 2016 ; Hooker, 2006). The position of depathologization was reflected in the universally accepted manuals of practice in psychology and psychiatry professions, that is, the DSM-5, and the International Classification of Diseases 10th Revision (ICD-10).

The anti-homosexuality law and the Sharia law were reported to have culminated in various social problems for people with diverse sexual orientations in the country (Human Rights Watch, 2016; Thoreson & Cook, 2011 ). ). The passage of the anti-homosexuality law was immediately followed by legitimized extortions and extensive media reports of high levels of violence, including mob attacks (Human Rights Watch, 2016; Thoreson & Cook, 2011 ). Sexual assaults have also been reported to be on the increase (Adie, 2019 ; Giwa et al., 2020 ).

No formal information is known about the activities of the organized body of psychology in the increase of awareness and provision of affirmative practices that conform to international standards. However, some NGOs in the country provide medical, psychological, and social services to people with diverse sexual orientations. For instance, Diadem Consults, as an NGO provides HIV and healthcare support to SGD persons. Numerous NGOs, such as the Outright Action International, and The Initiative for Equal Rights provide psychosocial support to SGD persons in Nigeria. The proposed imminent solution to the identified gap is the institutionalization of LGBT psychology.

The field of behavioural sciences (such as psychiatry and psychology) is saddled with the core responsibilities of scientifically determining what is normal and abnormal, what is adaptive and maladaptive in fairness to humanity (Glassgold & Drescher, 2007 ). Non-implementation of LGBT Psychology and affirmative practices for professionals in the academic and practice, respectively, contributes significantly to the pathologization, criminalization, and greater stigma experienced by the SGD communities (Matza et al., 2015 ). Knowledge of LGBT psychology is expected not only to advance human rights and development but also to provide means for ensuring and maintaining the mental health of people with diverse sexual orientations and gender identities.

Organized bodies of psychology domiciled in advanced countries have expanded the psychology curriculum that speaks to the reality of complexes in sexuality and gender nonconforming. The understanding and topics around sexualities and gender identities are core to the discipline of psychology, so every psychologist-in-training is saddled with the responsibilities of understanding what sexuality or gender identities are considered adaptive and maladaptive and the psychological rationale of its various classifications. Core to the ethics of the psychology discipline is the well-being of people and groups and the alienation of threats to human well-being (Ardila, 2015 ; Glassgold & Drescher, 2007 ). A large body of research suggests that mental health concerns are common among LGB individuals and often exceed the prevalence rates of the general population (King et al., 2017; World Health Organization [WHO], 2013 ). LGBT + people experience high rates of physical victimization, criminalization, and social exclusion, which appear to contribute to depression, anxiety, and suicidal ideation (Horne et al., 2009 ).

The ambivalent concept of ‘depathologization’ of the same sexuality in the most adopted diagnostic manual in the field of psychology (DSM-5; in Nigeria) and ‘criminalization’ of sexual minorities by the Nigerian government created significant gaps in the teaching curriculum and practice of specialists within the field of behavioral sciences (psychiatry, psychology, etc.). Hence, there is a need for an updated training curriculum, and competent professionals to address numerous intrapsychic factors, such as depression, anxiety, internalized homophobia, and social challenges, such as; victimization/bullying/Hate speech, discrimination, sexual assaults and abuse confronting the LGBT + persons and communities (Adie, 2019 ; Giwa et al., 2020 ; Makanjuola et al., 2018 ; Ogunbanjo et al., 2020).

Theoretical framework

This research is informed by the concepts of the Minority Stress Model (MSM: Meyer, 2003 ). The Minority Stress Model is fast becoming one of the most prominent theoretical and explanatory frameworks of SGD persons and communities. The concept of minority stress derives from several psychosocial theoretical directions, resulting in conflicts between minorities and dominant values, and the social environment experienced by members of minority groups (Meyer, 1995 ). The minority stress theory is that the differences between sexually and gender-diverse individuals and communities can be largely explained by stressors caused by hostile, homo-, bi, and transphobic cultures, often leading to lifelong harassment, abuse, discrimination, and harm (Meyer, 2003 ) and may ultimately affect quadrants of LGBT-Psychology (curriculum, research, outreach, & affirmative knowledge).

There is overwhelming evidence of increased mental health concerns among SGD people and communities, yet there are limited competent mental health providers to meet mental health needs (King et al., 2017; Nel & Victor, 2018; WHO, 2013 ). However, despite the passage of the anti-homosexuality law in 2014 putting pressure on the activities of the non-academic actors, some NGOs have documented much progress in terms of sensitization and provision of medical and psychosocial support, while not much is documented about the activities of the academic actors. The major course designated to bridge the gap in developed (and some developing) countries is LGBT psychology, designed to reconcile the gap between fallible social knowledge and scientific findings.

Clarke et al. ( 2010 ) shed more light on the understanding and contents of LGBT psychology for trainees in the field of behavioural science. Clarke et al. ( 2010 ) identified the following outlines [1] understanding the branch of psychology that is affirmative of LGBT people, [2] understanding the challenge of prejudice and discrimination faced by LGBT people, [3] the privilege of heterosexuality in psychology, and in the broader society, [4] LGBT concerns as legitimate contents in psychological research, 5) provision of a range of psychological perspectives on the lives and experiences of LGBT people, sexualities,, and genders. The perspectives of Clarke et al. (2010) account for both the practice and research gaps in LGBT psychology in Nigeria. The field of psychology and psychiatry housed the reserved right of society and science to define what is abnormal and normal with a sense of fairness, both within and outside the profession (Glassgold & Drescher, 2007).

In sum, the need to advance sexuality and gender knowledge motivates the organized body of psychology to respond to the emerging knowledge gap within the academic space, through the development and integration of LGBT psychology into the conventional psychology curriculum.

The current study set to assess and evaluate the current state of LGBT psychology in Nigeria and its implications for recommendations. The following specific objectives were developed based on the quadrants of LGBT psychology, which are to assess the.

‘Curriculum and Education’ quadrant of LGBT psychology.

‘Research’ quadrant of LGBT psychology.

‘Outreach’ quadrant of LGBT psychology.

‘Professional’ quadrant of LGBT psychology.

Research questions

Does the Nigerian undergraduate curriculum entail LGBT-psychology content compared to what is obtained in the United States of America and South Africa?

To what extent do psychology professionals research LGBT-related matters in Nigeria?

How engaged (outreach) is the organized body of psychology in Nigeria to the LGBT communities?

To what extent are the practicing psychologists caring for LGBT + persons or communities in Nigeria exposed to LGBT + affirmative training?

Study area/settings

The study setting is Nigeria, Africa’s most populous country with over 180 million people, and is in the western part of the African continent (Wright & Okolo, 2018). The Nigerian climate, like most other countries in Africa, has a long history of SGD populations (Alimi, 2015). The popular assumption among Nigerians was that the concept of LGBT is a Western imposition on African communities (Alimi, 2015; Mohammed, 2019). Nigeria also has the most diverse cultures in Africa, with more than 250 local languages.

All dominant tribes in Nigeria had and still have their historical cultural understanding of diverse sexual orientations and gender identities. For example, ancient Yoruba identified sexual minorities (SM) as ‘adofuro’ (a Yoruban word that means someone who engages in anal sex) and gender diverse (GD) individuals as ‘Lakiriboto’ (absence of binary gender assignment at birth due to ambiguous external genitalia) and/or ‘làgbedemeji’ (a person with a combination of penile and vaginal characteristics) (Alimi, 2015). Similarly, a historical reference to Hausa and/or Fulani of Northern Nigeria revealed that northerners identified SGD persons with the descriptive name Yan Daudu (in the Hausa language, meaning that men are considered ‘wives’ to men). The Yan Dauda communities were typically same-sex attracted by the same sex, who thrived (and still thrive) in northern Nigeria (Alimi, 2015). In 2014, the Nigerian government passed into law an anti-homosexuality law against SM in the country (Omilusi, 2021).

Research design/approach

The research utilized a multi-method approach (positivistic & survey) to sort both primary and secondary data used in the study. To conform to the positivist paradigm and the deductive approach. Survey-based questionnaires are preferred for observing populations and answering quantitative research questions (LaDonna et al., 2018). The approaches permit researchers to explore the public documents of the organized body of psychology (including newsletter), approved training curriculum, publications, and survey subset of the population of interest in the country.

Population and sample

The population of the study survey phase is practicing therapists in Nigeria with experience/history of working with LGBT + persons or communities. The study participants are the one hundred twenty-four participants ( n  = 124) practicing therapists who consented to participate in the study. 57.3% ( n  = 71) of the study’s participants were female practitioners, while 42.7 ( n  = 53) self-identified as male practitioners. The participants’ age ranges between 21 and 66 years (mean = 39.5; SD = 05.03). Regarding participants’ sexual orientation, all the participants (100%) self-identified as heterosexuals.

Research tools

The qualitative phase of the synthesized needed information from the benchmark minimum academic standards (BMAS) for undergraduate psychology programs authored by the National Universities Commission (NUC), a governmental body saddled with the responsibilities of regulating and periodically ensuring that the curriculum of psychology teachings in the country is universal and meets the minimum standard as stipulated in the BMAS document.

The questionnaire booklets were made up of widely used and psychometrically sound instruments for the collection of data in the study. The questionnaire was made up of two sections, Section A-C:

Socio-Demographics section that measured respondents’ data such as specialty, gender identity, age, marital status, highest educational attainment, and length of experience.

Checklist of previous experience with LGBT training. This section explored the categorical checklist for participants to tick as applied. The checklists entail a tick for the absence of formal and informal training, a tick for the history of previous formal training (applicable to foreign-trained therapists), and a tick for the history of informal training experience (i.e. training through webinars, conferences, YouTube, etc.).

Self-Efficacy working with LGBT clients was measured using the Lesbian, Gay, and Bisexual Affirmative Counselling Self-Efficacy Inventory (LGB-CSI). LGB-CSI is a 32-item scale developed by Dillon and Worthington (2003) to measure participants’ self-efficacy in performing LGBT+-affirmative psychotherapy in Nigeria. The scale has five dimensions, namely advocacy skills, knowledge application, awareness, assessment, and relationship. LGB-CSI scores are obtained by adding all items of the mentioned subscales. LGB-CSI is a six-point Likert scale with good internal consistency (Cronbach’s α > 0.70).

Data collection and procedures

As the study was a mixture of qualitative and quantitative kinds, qualitative content was recovered from the current benchmark for minimum academic standards (B-MAS), public documents of the Nigerian Psychological Associations, and published qualifying articles on some selected database databases (Google Scholar; PudMed & Elsevier) database between January 30, 2015 (period after the enactment of anti-homosexuality law in Nigeria) and April 2023 (deadline for data collection). The selected articles were LGBT-based publications by researchers / co-researchers affiliated with Nigerian institution(s). However, the quantitative data were retrieved through a set of in-print, structured, and validated questionnaires, which enabled an objective assessment of the constructs of interest in the study. Participants who self-identified as psychologists were included and met other inclusion criteria were included in the study. A detailed informed consent form (stating all ethical requirements) was made available to prospective participants who willingly consented and participated in the study. Participants were recruited using a purposive sampling technique because data collection of this nature is cumbersome to retrieve from the specialist due to the existing anti-homosexuality law in Nigeria. The data collection for the study spans from June 08, 2022, to April 25, 2023.

Showing the numbers of LGBT-related Publications for the year 2015–2022 in Nigeria

Showing the number of psychologists with a history working with LGBT clients in Nigeria.

Data analysis

The document analysis method was adopted for the qualitative phase of the study, while a one-way analysis of covariance was used to test the importance of affirmative training of LGBT in self-efficacy for psychotherapy with SGD populations. Quantitative data were analysed using the statistical package for social sciences (SPSS v.27) and Prism Graph pad (version 16.0).

Results/Outcomes

This section presents the data analyses and results of the study. This section presents the interpretations of the document analyses the four cardinals of LGBT-Psychology and establishes the quantitative findings of the study objectives that established the interplay between the study objectives 1 (curriculum and education) and 4 (professionalism).

Study outcome 1 (curriculum and education)

The finding in study objective 1 that proposed to assess the curriculum and educational quadrant of LGBT psychology in Nigeria was synthesized from the B-MAS for undergraduate psychology programs compared to the psychology curriculum obtained from the United States of America and South Africa as presented in Table 2 .

The results in Table 2 show that related course titles, such as clinical psychology/pathology, contemporary issues in psychology, and psychology of social change, were included in the Nigerian curriculum and training standard as available in South Africa and the USA. However, the Nigerian course contents under clinical psychology/psychopathology cover topics like sexual dysfunction, gender dysphoria, and paraphilic disorder, but the scopes are not expanded and cover topics like sexual and gender diversity and sexual health. Similarly, the course title Contemporary Issues in Psychology does not cover the discussion of diverse sexual orientations and gender identities as a course content just like the curriculum of counterparts within the African continent (e.g. South Africa) and the Western communities (e.g. USA). However, the content of LGBT psychology subsumed under the course title ‘Psychology of social change named social change and identity crises’ was not covered in the Nigerian curriculum despite the inclusion of the psychology of social change in the curriculum.

Furthermore, Table 2 revealed that the Nigerian psychology curriculum does not incorporate LGBT Psychology/Psychology of Sexual and Gender Diversity into the existing training curriculum like what is available in SA and the USA. The LGBT-Psychology/ Psychology of Sexual and Gender Diversity curriculum highlighted the following course contents: [1] historical perspectives of diverse sexual orientations and gender identity [2] LGBT terminology [3] theories of identity development [4] Mental health and well-being of sexual and gender minorities [5] Approaches and ethical approaches to LGBT research [6] Issues that impact LGBTQ + individuals and communities [7] Understanding the role the field of psychology plays in supporting marginalized communities, specifically sexual and gender minorities.

Study outcome 2 (research)

The finding in objective 2 of the study that proposed to assess the research quadrant of LGBT psychology in Nigeria was synthesized from related published articles from 2015 to 2022 in the three main and rated publications (Google Scholar; PudMed & Elsevier) as presented in Fig. 1 .

The descriptive analysis of the synthesized literature as shown in Fig. 1 revealed that the majority (69.2%) of the reviewed articles (e.g. Oginni et al., 2021 ; Mapayi et al., 2016 ; 2022; Sekoni et al., 2022 ; Sekoni et al., 2020 ; Ogunbajo et al., 2021 ; Makanjuola et al., 2018 ; and Oginni et al., 2021 ) were co-published by psychiatrists. The results also revealed that 14.28% of the LGBT-related articles (e.g. Ogunbanjo et al., 2020; Sekoni et al., 2016 ; McKay et al., 2017 ) were co-published by public health specialists, 07.1% of the LGBT-related articles were affiliated with the department of law (e.g. Okuefuna, 2016 ; Arimoro, 2018 ), 03.8% were affiliated with the department of sociology (e.g. Akanle et al., 2019 ), 03.8% of the articles were affiliated with the department of performing theatre (e.g. Okpadah, 2020 ), while none (0%) was affiliated with the department of psychology.

Study outcome 3 (Outreach)

The finding in objective 3 of the study that proposed to assess the outreach quadrant of LGBT psychology in Nigeria was synthesized from previously published flyers/workshops/conferences/outreach/communications issued by the organized body of psychology in Nigeria between 2015 and 2022 as presented in Table 3 .

The results in Table 3 revealed that there was no documented outreach to the LGBT community based on an organized body of psychology in Nigeria. In other words, there was no record of the involvement of the organized body in national discussions, community engagements, or the publication of a position document on LGBT populations. In social media handles, there was no formal LGBT-based broadcast in the newsletters, websites, WhatsApp, and telegram handles of the organized body of psychology. Similarly, there were no LGBT-related topics recorded in the workshop/conference previously organized by the body of psychology between 2015 and 2022.

Study outcome 4 (Professional Practice)

The finding in study objective 4 that proposed to assess the professional practice quadrant of LGBT psychology in Nigeria was synthesized among practicing clinical psychologists caring for LGBT + persons or communities was presented in Figs. 3 and 4 .

Figure 2 revealed that majorityof the participants (81%, n  = 101) reported having previously and/or currently provided psychological services to members of the LGBT communities, while the counterpart minority (19%, n  = 23) reported no history of working with self-disclosed clients

Figure 3 revealed that majority of the participants (91.9%, n  = 114) reported no history of formal and informal LGBT training, while 5.65% ( n  = 07) of the participants had informal LGBT + affirmative training, while 2.42% ( n  = 03) of practicing psychologists had formal LGBT + affirmative training (during their foreign education pursuit) The findings in Figs. 3 and 4 revealed that most of the practicing psychologists who had (or still) attended to LGBT persons and communities had not informed training tailored toward the populations. The findings informed the need to examine the impact of Quadrant 1 (curriculum and education) on Quadrant 4 (Professional Practice) of LGBT psychology. Table 4 examines the influence of the LGBT training experience on self-efficacy in working with LGBT clients The results in Table 4 showed that the effectiveness of psychologists working with LGBT clients was significantly influenced by the experience of LGBT training (F (03,120) = 52.66; p  < 0.01; n p 2 = 0.568). Such that 56.8% (eta value x 100) of the perceived self-efficacy working with LGBT clients was accounted for by previous LGBT training experience. Since the significance was established in the F-value, a post hoc analysis was therefore conducted to determine the magnitude of the F-value (see Fig. 4 )

Showing the distribution of previous training experience on affirmative psychotherapy for the SGD populations

Figure 4 revealed that psychologists with formal LGBT-affirmative training (M = 51.60; SD = 02.67) exhibited greater efficacy working with LGBT clients than counterparts with informal training (M = 38.85; SD = 02.59) and psychologists without formal and informal LGBT-affirmative training (M = 32.25; SD = 01.07). However, there were no significant differences in the efficacy of working with LGBT clients by psychologists without informal/formal training and those with informal pieces of training (MD = 06.60; p  > 0.05).

Scheffe post hoc analysis showing the influence of training experience on self-efficacy working with LGBT clients

The study evaluated the stance of LGBT psychology in Nigeria, and the outcome also revealed that the Nigerian curriculum is somewhat sufficient with that of the reference counterpart in the study (i.e. USA and South Africa), following the enrolment of same courses (such as clinical psychology/pathology, contemporary issues in psychology and psychology of social change) in the Nigerian curriculum and training but the scope are limited and do not cover some important contents like sexual and gender diversity, sexual health, and social change and identity crises. Furthermore, the Nigerian psychology curriculum does not incorporate LGBT Psychology/Psychology of Sexual and Gender Diversity into the existing training curriculum as what is available in SA and the USA. The organized bodies of psychology in some developed and developing communities (such as the USA, UK, Philippines, Canada, Australia, South Africa, etc.) identified overwhelming knowledge and scientific findings of contemporary events of sexualities and gender identity and incorporated the identified knowledge gaps into a stand-alone course entitled ‘LGBT Psychology’ to keep psychology students abreast of the specific knowledge needed to understand human sexual and gender behaviours (Ardila, 2015 ; Clarke et al., 2010 ; Moreno et al., 2020 ) For the second objective, the descriptive outcome established that most of the published articles were co-published by psychiatrists, public health specialists, lawyers, sociologists, and academic artists. However, none of the reviewed articles was published by a psychologist. Research outputs played an important role in the scientific understanding of diverse sexuality and gender, co-morbid mental distress, and lived experiences of LGBT persons and communities, rather than the primitive dispositions that are well-rooted in religious ideology, punishable by death (Morgan & Nerison, 1993 ). In other words, superior arguments through scientific discoveries have changed the narrative of the same sexuality over the years, just like mental health illnesses that were at an early stage attributed to spiritual torments (Hooker, 1956 ; Sagarin, 1951). The finding implied that LGBT psychology has no visible place in the research focus of psychologists in Nigeria. This is evidenced in the the study that none of the authors of published articles on LGBT persons and communities self-identified as a psychologist or member of the Department of Psychology at any higher institution in Nigeria. There is a need to discuss LGBT Psychology at conventions or conferences, to incorporate scientific matters about the SGD populations. Meanwhile, the discussion of LGBT matter and scientific findings contributed significantly to the development of LGBT psychology in countries such as the Philippines (Ofreneo, 2013 ) and South Africa (Nel, 2009 ) The third objective revealed that there were no documented LGBT community-based outreach, broadcast, and/or inclusive LGBT-related themes to workshops/conferences organized by the body of psychology, indicating the passive disposition of the psychology body in national discussions, newsletters, community engagements, or issuance of position paper regarding the SGD populations. Behavioural scientists such as psychologists are the core custodians of community well-being and psychology (PsySSA, 2017 ). Outreach is one of the responsibilities of professionals in taking scientific knowledge from the community members for public interest or further enhancing the community’s mental health and well-being (Smith, 1990 ). Psychologists as experts share knowledge to inform policymakers, engage media on issues of human behaviour, and take principle and formal stands on pressing social issues, especially when behavioural expertise is needed to contribute to debate and decision-making (Cohen et al., 2012 ). Outreach can be done through various social media channels (such as Facebook, newsletter, emails, etc.) or formal outreach (involvement in national discussions, academic conferences, community engagements, etc.). In South Africa, psychologists worked closely with CSOs to sensitize the masses and ensure competence in working with SGD populations (De Waal & Manion, 2006 ; Hoad et al., 2005 ; Reddy et al., 2009 ; Van Zyl & Steyn, 2005 ; Victor & Nel, 2017) The outcome of objective 4 showed that most of the participants reported having previously and/or currently rendered psychological services to members of the LGBT communities, while most also reported having no history of formal and informal LGBT training. In other words, most practicing psychologists lack informed training tailored to the needs of SGD populations. Further research revealed that the effectiveness of psychologists working with LGBT clients was significantly influenced by the LGBT training experience.

Recommendations

Based on the outcome of the study and as behavioural scientists and practitioners, the following recommendations were presented The study recommends that the NUC expand some of the existing course content that talks about sexual disorders and gender identities to discuss the overview and scientific reasons why homosexuality was considered a disorder, while people with diverse sexual orientations were considered a marginalized set of people. The introduction of LGBT Psychology will ensure a good understanding of the history of LGBT psychology, affirmative practices, knowledge of past and current attitudes and behaviours towards LGBT people, including common misconceptions, prejudice, and discrimination, research, and ethics working with LGBT and other identified contents are considered very important to fill the knowledge gap identified The organized body of psychology is encouraged to update the psychology curriculum of Nigeria to bridge the training and theoretical gaps of students studying psychology in Nigeria. The curriculum adjustment will guide to exploration of LGBT issues and concerns in different areas of psychology and other content reported in the results section. In this regard, psychologists’ academic outputs are expected to increase in publications, and thus address the need for more inclusive pedagogical and research practices, which will contribute to the challenging heteronormativity as it was experienced in global communities and South Africa (Nduna et al., 2017 ; Nel, 2009 ). For example, the organized body of psychology in South Africa took a leading role in Africa through the early introduction of LGBT psychology and the development of the Psychological Society of South Africa (PsySSA)’s Affirmative Practice Guidelines for Psychology Professionals, sufficiently promoted by the Specialized Division of Sexuality and Gender. The division focus areas are Research, Training, and Development; Education and Training; Experiential workshops; and Advocacy and Expert opinion (Nel, 2014 ) The implication of adjusted teaching, learning, and research into LGBT psychology also have significant and impactful implications in the ethics and practice guidelines for attending to people with diverse sexual orientations and gender identities. The American Psychology Association (APA) for the USA and the PsySSA for South Africans developed and published an affirmative guideline that assists practicing psychologists to operate within professional conduct and competencies while handling patients who are members of the LGBT community Researchers or psychologists in practice are encouraged to collaborate with scholars from other countries to recognize the relative, cultural, and national specificities of LGBT lives and, in turn, contribute immensely to the international discussion and approach to LGBT psychology.

Limitations

The researchers evaluated and discussed LGBT Psychology in Nigeria, from the unique field of psychology mainly, other disciplines and scholars from different fields should explore and appraise the disposition and contributions to the LGBT course. The use of in-depth interviews and Focus Group Discussions to engage stakeholders in the organized body of psychology or key players in curriculum development may provide a more in-depth understanding of the factors affecting SGD populations and LGBT psychology in Nigeria and proffer potential solutions.

Conclusions

This article has provided information on the development and assessment of LGBT psychology in Nigeria, and what is available in other countries, specifically the USA and South Africa. The study concluded that the Nigerian course contents are sufficient as much as their counterpart nations (USA & SA), however, lacking some important course content (i.e. social change and identity crises; LGBT-Psychology/ Psychology of Sexual and Gender Diversity. The study further established that no LGBT-related published articles from 2015 to 2022 in Nigeria were credited/affiliated with the Department of Psychology. There was no documented outreach to the minority (LGBT) groups by the organized body of psychology. Lastly, the majority of the practicing psychologists reported having previously and/or currently providing psychological services to members of the LGBT communities, without formal and informal LGBT training. This article proposes specific recommendations to facilitate the emergence of LGBT psychology and to help develop the field in Nigeria, as it has already been established in many developed and developing countries as a formal area of psychological science.

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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Acknowledgements

The authors would like to thank the practicing psychologists/counseling psychologists who volunteered to take partake in this study.

Open access funding provided by University of South Africa. The research was independently funded by the researchers. No funding was obtained from external sources for this research.

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AOO conceived the research ideas, organized the research, performed the studies, analyzed the data, and drafted the manuscript. JAN co-conceived the research ideas, provided the overall leadership across every role, and revised the entire manuscript. All authors contributed to writing sections of the manuscript and read and approved the submitted version.

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Olaseni, A.O., Nel, J.A. Assessment Survey and evaluation of LGBT-Psychology in Nigeria: current state and recommendations. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06608-y

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The American Trends Panel (ATP), created by Pew Research Center, is a nationally representative panel of randomly selected U.S. adults. Panelists participate via self-administered web surveys. Panelists who do not have internet access at home are provided with a tablet and wireless internet connection. Interviews are conducted in both English and Spanish. The panel is being managed by Ipsos.

Data in this report is drawn from ATP Wave 141, conducted from Jan. 22 to 28, 2024, and includes an oversample of non-Hispanic Asian adults, non-Hispanic Black men and Hispanic men in order to provide more precise estimates of the opinions and experiences of these smaller demographic subgroups. These oversampled groups are weighted back to reflect their correct proportions in the population. A total of 5,146 panelists responded out of 5,604 who were sampled, for a response rate of 92%. The cumulative response rate accounting for nonresponse to the recruitment surveys and attrition is 3%. The break-off rate among panelists who logged on to the survey and completed at least one item is 1%. The margin of sampling error for the full sample of 5,146 respondents is plus or minus 1.7 percentage points.

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The ATP was created in 2014, with the first cohort of panelists invited to join the panel at the end of a large, national, landline and cellphone random-digit-dial survey that was conducted in both English and Spanish. Two additional recruitments were conducted using the same method in 2015 and 2017, respectively. Across these three surveys, a total of 19,718 adults were invited to join the ATP, of whom 9,942 (50%) agreed to participate.

In August 2018, the ATP switched from telephone to address-based sampling (ABS) recruitment. A study cover letter and a pre-incentive are mailed to a stratified, random sample of households selected from the U.S. Postal Service’s Delivery Sequence File. This Postal Service file has been estimated to cover as much as 98% of the population, although some studies suggest that the coverage could be in the low 90% range. 1

Within each sampled household, the adult with the next birthday is asked to participate. Other details of the ABS recruitment protocol have changed over time but are available upon request. 2

We have recruited a national sample of U.S. adults to the ATP approximately once per year since 2014. In some years, the recruitment has included additional efforts (known as an “oversample”) to boost sample size with underrepresented groups. For example, Hispanic adults, Black adults and Asian adults were oversampled in 2019, 2022 and 2023, respectively.

Across the six address-based recruitments, a total of 23,862 adults were invited to join the ATP, of whom 20,917 agreed to join the panel and completed an initial profile survey. Of the 30,859 individuals who have ever joined the ATP, 11,927 remained active panelists and continued to receive survey invitations at the time this survey was conducted.

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The questionnaire was developed by Pew Research Center in consultation with Ipsos. The web program was rigorously tested on both PC and mobile devices by the Ipsos project management team and Pew Research Center researchers. The Ipsos project management team also populated test data that was analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey.

All respondents were offered a post-paid incentive for their participation. Respondents could choose to receive the post-paid incentive in the form of a check or a gift code to Amazon.com or could choose to decline the incentive. Incentive amounts ranged from $5 to $20 depending on whether the respondent belongs to a part of the population that is harder or easier to reach. Differential incentive amounts were designed to increase panel survey participation among groups that traditionally have low survey response propensities.

Data collection protocol

The data collection field period for this survey was Jan. 22 to Jan. 28, 2024. Postcard notifications were mailed to a subset of ATP panelists with a known residential address on Jan. 22. 3

Invitations were sent out in two separate launches: soft launch and full launch. Sixty panelists were included in the soft launch, which began with an initial invitation sent on Jan. 22. The ATP panelists chosen for the initial soft launch were known responders who had completed previous ATP surveys within one day of receiving their invitation. All remaining English- and Spanish-speaking sampled panelists were included in the full launch and were sent an invitation on Jan. 23.

All panelists with an email address received an email invitation and up to two email reminders if they did not respond to the survey. All ATP panelists who consented to SMS messages received an SMS invitation and up to two SMS reminders.

Data quality checks

To ensure high-quality data, the Center’s researchers performed data quality checks to identify any respondents showing clear patterns of satisficing. This includes checking for whether respondents left questions blank at very high rates or always selected the first or last answer presented. As a result of this checking, three ATP respondents were removed from the survey dataset prior to weighting and analysis.

The ATP data is weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. First, each panelist begins with a base weight that reflects their probability of selection for their initial recruitment survey. These weights are then rescaled and adjusted to account for changes in the design of ATP recruitment surveys from year to year. Finally, the weights are calibrated to align with the population benchmarks in the accompanying table to correct for nonresponse to recruitment surveys and panel attrition. If only a subsample of panelists was invited to participate in the wave, this weight is adjusted to account for any differential probabilities of selection.

Among the panelists who completed the survey, this weight is then calibrated again to align with the population benchmarks identified in the accompanying table and trimmed at the 2nd and 98th percentiles to reduce the loss in precision stemming from variance in the weights. This trimming is performed separately among non-Hispanic Black, non-Hispanic Asian, Hispanic and all other respondents. Sampling errors and tests of statistical significance take into account the effect of weighting.

The following table shows the unweighted sample sizes and the error attributable to sampling that would be expected at the 95% level of confidence for different groups in the survey.

Sample sizes and sampling errors for other subgroups are available upon request. In addition to sampling error, one should bear in mind that question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls.

Dispositions and response rates

• AAPOR Task Force on Address-based Sampling. 2016. “ AAPOR Report: Address-based Sampling .” ↩
• Email [email protected] . ↩
• Postcard notifications are sent to 1) panelists who have been provided with a tablet to take ATP surveys, 2) panelists who were recruited within the last two years, and 3) panelists recruited prior to the last two years who opt to continue receiving postcard notifications. ↩

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• DOI: 10.1108/jpmh-04-2024-0055
• Corpus ID: 272230078

Defining mental health literacy: a systematic literature review and educational inspiration

• Shengnan Zeng , Richard Bailey , +1 author Xiaohui Chen
• Published in Journal of Public Mental… 2 September 2024
• Psychology, Education

56 References

A systematic review of the limitations and associated opportunities of chatgpt, deductive qualitative analysis: evaluating, expanding, and refining theory, conceptualising and measuring positive mental health literacy: a systematic literature review, mental health education integration into the school curriculum needs to be implemented, review: school-based mental health literacy interventions to promote help-seeking - a systematic review., public opinion towards mental health (the case of the vologda region), quantifying the global burden of mental disorders and their economic value, mental health literacy: it is now time to put knowledge into practice, clarifying the concept of mental health literacy: protocol for a scoping review, positive mental health literacy: a concept analysis, related papers.

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A quick guide to survey research

1 University of Cambridge,, UK

2 Cambridge University Hospitals NHS Foundation Trust,, UK

Questionnaires are a very useful survey tool that allow large populations to be assessed with relative ease. Despite a widespread perception that surveys are easy to conduct, in order to yield meaningful results, a survey needs extensive planning, time and effort. In this article, we aim to cover the main aspects of designing, implementing and analysing a survey as well as focusing on techniques that would improve response rates.

Medical research questionnaires or surveys are vital tools used to gather information on individual perspectives in a large cohort. Within the medical realm, there are three main types of survey: epidemiological surveys, surveys on attitudes to a health service or intervention and questionnaires assessing knowledge on a particular issue or topic. 1

Despite a widespread perception that surveys are easy to conduct, in order to yield meaningful results, a survey needs extensive planning, time and effort. In this article, we aim to cover the main aspects of designing, implementing and analysing a survey as well as focusing on techniques that would improve response rates.

Clear research goal

The first and most important step in designing a survey is to have a clear idea of what you are looking for. It will always be tempting to take a blanket approach and ask as many questions as possible in the hope of getting as much information as possible. This type of approach does not work as asking too many irrelevant or incoherent questions reduces the response rate 2 and therefore reduces the power of the study. This is especially important when surveying physicians as they often have a lower response rate than the rest of the population. 3 Instead, you must carefully consider the important data you will be using and work on a ‘need to know’ rather than a ‘would be nice to know’ model. 4

After considering the question you are trying to answer, deciding whom you are going to ask is the next step. With small populations, attempting to survey them all is manageable but as your population gets bigger, a sample must be taken. The size of this sample is more important than you might expect. After lost questionnaires, non-responders and improper answers are taken into account, this sample must still be big enough to be representative of the entire population. If it is not big enough, the power of your statistics will drop and you may not get any meaningful answers at all. It is for this reason that getting a statistician involved in your study early on is absolutely crucial. Data should not be collected until you know what you are going to do with them.

Directed questions

After settling on your research goal and beginning to design a questionnaire, the main considerations are the method of data collection, the survey instrument and the type of question you are going to ask. Methods of data collection include personal interviews, telephone, postal or electronic ( Table 1 ).

Advantages and disadvantages of survey methods

Collected data are only useful if they convey information accurately and consistently about the topic in which you are interested. This is where a validated survey instrument comes in to the questionnaire design. Validated instruments are those that have been extensively tested and are correctly calibrated to their target. They can therefore be assumed to be accurate. 1 It may be possible to modify a previously validated instrument but you should seek specialist advice as this is likely to reduce its power. Examples of validated models are the Beck Hopelessness Scale 5 or the Addenbrooke’s Cognitive Examination. 6

The next step is choosing the type of question you are going to ask. The questionnaire should be designed to answer the question you want answered. Each question should be clear, concise and without bias. Normalising statements should be included and the language level targeted towards those at the lowest educational level in your cohort. 1 You should avoid open, double barrelled questions and those questions that include negative items and assign causality. 1 The questions you use may elicit either an open (free text answer) or closed response. Open responses are more flexible but require more time and effort to analyse, whereas closed responses require more initial input in order to exhaust all possible options but are easier to analyse and present.

Questionnaire

Two more aspects come into questionnaire design: aesthetics and question order. While this is not relevant to telephone or personal questionnaires, in self-administered surveys the aesthetics of the questionnaire are crucial. Having spent a large amount of time fine-tuning your questions, presenting them in such a way as to maximise response rates is pivotal to obtaining good results. Visual elements to think of include smooth, simple and symmetrical shapes, soft colours and repetition of visual elements. 7

Once you have attracted your subject’s attention and willingness with a well designed and attractive survey, the order in which you put your questions is critical. To do this you should focus on what you need to know; start by placing easier, important questions at the beginning, group common themes in the middle and keep questions on demographics to near the end. The questions should be arrayed in a logical order, questions on the same topic close together and with sensible sections if long enough to warrant them. Introductory and summary questions to mark the start and end of the survey are also helpful.

Pilot study

Once a completed survey has been compiled, it needs to be tested. The ideal next step should highlight spelling errors, ambiguous questions and anything else that impairs completion of the questionnaire. 8 A pilot study, in which you apply your work to a small sample of your target population in a controlled setting, may highlight areas in which work still needs to be done. Where possible, being present while the pilot is going on will allow a focus group-type atmosphere in which you can discuss aspects of the survey with those who are going to be filling it in. This step may seem non-essential but detecting previously unconsidered difficulties needs to happen as early as possible and it is important to use your participants’ time wisely as they are unlikely to give it again.

Distribution and collection

While it should be considered quite early on, we will now discuss routes of survey administration and ways to maximise results. Questionnaires can be self-administered electronically or by post, or administered by a researcher by telephone or in person. The advantages and disadvantages of each method are summarised in Table 1 . Telephone and personal surveys are very time and resource consuming whereas postal and electronic surveys suffer from low response rates and response bias. Your route should be chosen with care.

Methods for maximising response rates for self-administered surveys are listed in Table 2 , taken from a Cochrane review.2 The differences between methods of maximising responses to postal or e-surveys are considerable but common elements include keeping the questionnaire short and logical as well as including incentives.

Methods for improving response rates in postal and electronic questionnaires 2

• – Involve a statistician early on.
• – Run a pilot study to uncover problems.
• – Consider using a validated instrument.
• – Only ask what you ‘need to know’.
• – Consider guidelines on improving response rates.

The collected data will come in a number of forms depending on the method of collection. Data from telephone or personal interviews can be directly entered into a computer database whereas postal data can be entered at a later stage. Electronic questionnaires can allow responses to go directly into a computer database. Problems arise from errors in data entry and when questionnaires are returned with missing data fields. As mentioned earlier, it is essential to have a statistician involved from the beginning for help with data analysis. He or she will have helped to determine the sample size required to ensure your study has enough power. The statistician can also suggest tests of significance appropriate to your survey, such as Student’s t-test or the chi-square test.

Conclusions

Survey research is a unique way of gathering information from a large cohort. Advantages of surveys include having a large population and therefore a greater statistical power, the ability to gather large amounts of information and having the availability of validated models. However, surveys are costly, there is sometimes discrepancy in recall accuracy and the validity of a survey depends on the response rate. Proper design is vital to enable analysis of results and pilot studies are critical to this process.

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Virtual tools for testing autonomous driving: a survey and benchmark of simulators, datasets, and competitions.

1. Introduction

1.1. related works, 1.2. motivations and contributions.

• This survey is the first to systematically investigate autonomous driving simulators by providing a deep analysis of their physics and rendering engines to support informed simulator selection.
• This survey provides an in-depth analysis of three key functions in autonomous driving simulators: scenario simulation, sensor simulation, and the implementation of vehicle dynamics simulation.
• This survey is the first to systematically review virtual autonomous driving competitions that are valuable for virtually testing autonomous driving systems.

1.3. Organizations

2. autonomous driving simulators, 2.1. open source simulators.

• Baidu Apollo

2.2. Non-Open-Source Simulators

• Ansys Autonomy

Click here to enlarge figure

• HUAWEI Octopus
• NVIDIA D RIVE Constellation
• SCANeR Studio
• TAD Sim 2.0

2.3. Discussions of Autonomous Driving Simulators

2.3.1. accessibility, 2.3.2. physics engines.

• Unigine Engine
• Chaos Physics
• Selection of Physics Engines

2.3.3. Rendering Engines

• Unreal Engine
• Unity Engine
• Selection of Rendering Engines

2.3.4. Critical Functions

• Scenario Simulation
• Sensor Simulation
• Implementation of Vehicle Dynamics Simulation

3. Autonomous Driving Datasets

3.1. datasets.

• CamVid Dataset
• Caltech Pedestrian Dataset
• KITTI Dataset
• Cityscapes Dataset
• Oxford RobotCar Dataset
• SYNTHIA Dataset
• Mapillary Vistas Dataset
• Bosch Small Traffic Lights Dataset
• KAIST Urban Dataset
• ApolloScape Dataset
• CULane Dataset
• DBNet Dataset
• HDD Dataset
• KAIST Multispectral Dataset
• IDD Dataset
• NightOwls Dataset
• EuroCity Persons Dataset
• BDD100K Dataset
• DR(eye)VE Dataset
• Argoverse Dataset
• nuScenes Dataset
• Waymo Open Dataset
• Unsupervised Llamas Dataset
• D 2 -City Dataset
• Highway Driving Dataset
• CADC Dataset
• Mapillary Traffic Sign Dataset
• A2D2 Dataset
• nuPlan Dataset
• AutoMine Dataset
• AIODrive Dataset
• SHIFT Dataset
• OPV2V Dataset
• TAS-NIR Dataset
• OpenLane-V2 Dataset

3.2. Discussions of Autonomous Driving Datasets

• The CADC dataset focuses on autonomous driving in adverse weather conditions.
• The CULane dataset is designed for road detection.
• The KAIST multispectral dataset is suitable for low-light environments.
• The DR(eye)VE dataset addresses driver attention prediction.
• The Caltech Pedestrian, NightOwls, and EuroCity Persons datasets focus on pedestrian detection.
• The HDD and DBNet datasets are centered on human driver behavior.
• The Oxford RobotCar dataset emphasizes long-term autonomous driving.
• The Complex Urban, D 2 -City, and Cityscapes datasets are aimed at urban scenarios.
• The KAIST Urban dataset is mainly for SLAM tasks.
• The Argoverse dataset targets 3D tracking and motion prediction.
• The Mapillary Traffic Sign dataset focuses on traffic signs.
• The SYNTHIA, SHIFT, and OPV2V datasets originate from virtual worlds.

4. Virtual Autonomous Driving Competitions

4.1. virtual competitions.

• Baidu Apollo Starfire Autonomous Driving Competition
• China Intelligent and Connected Vehicle Algorithm Competition
• CVPR Autonomous Driving Challenge
• Waymo Open Dataset Challenge
• Argoverse Challenge
• BDD100K Challenge
• CARLA Autonomous Driving Challenge
• CARSMOS International Autonomous Driving Algorithm Challenge
• The Competition of Trajectory Planning for Automated Parking
• OnSite Autonomous Driving Challenge

4.2. Discussions of Virtual Autonomous Driving Competitions

5. perspectives of simulators, datasets, and competitions.

• Closing the Gap Between Simulators and the Real World
• Modeling Sensors Accurately
• Generating Critical Scenarios
• Enhancing Data Diversity
• Enhancing Privacy Protection
• Enhancing Competitiveness in Competitions
• Optimizing Algorithm Reliability Verification

6. Conclusions

Author contributions, data availability statement, conflicts of interest.

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Share and Cite

Zhang, T.; Liu, H.; Wang, W.; Wang, X. Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions. Electronics 2024 , 13 , 3486. https://doi.org/10.3390/electronics13173486

Zhang T, Liu H, Wang W, Wang X. Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions. Electronics . 2024; 13(17):3486. https://doi.org/10.3390/electronics13173486

Zhang, Tantan, Haipeng Liu, Weijie Wang, and Xinwei Wang. 2024. "Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions" Electronics 13, no. 17: 3486. https://doi.org/10.3390/electronics13173486

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