12.1 What is a survey, and when should you use one?

Students in my research methods classes often feel that surveys are self-explanatory. This feeling is understandable. Surveys are part of our everyday lives. Every time you call customer service, purchase a meal, or participate in a program, someone is handing you a survey to complete. Survey results are often discussed in the news, and perhaps you’ve even carried our a survey yourself. What could be so hard? Ask people a few quick questions about your research question and you’re done, right?

Students quickly learn that there is more to constructing a good survey than meets the eye. Survey design takes a great deal of thoughtful planning and often many rounds of revision, but it is worth the effort. As we’ll learn in this section, there are many benefits to choosing survey research as your data collection method particularly for student projects. We’ll discuss what a survey is, its potential benefits and drawbacks, and what research projects are the best fit for survey design.

Is survey research right for your project?

To answer this question, the first thing we need to do is distinguish between a survey and a questionnaire. They might seem like they are the same thing, and in normal non-research contexts, they are used interchangeably. In this textbook, we define a survey as a research design in which a researcher poses a set of predetermined questions to an entire group, or sample, of individuals. That set of questions is the questionnaire, a research instrument consisting of a set of questions (items) intended to capture responses from participants in a standardized manner. Basically, researchers use questionnaires as part of survey research. Questionnaires are the tool. Surveys are one research design for using that tool.

Let’s contrast how survey research uses questionnaires with the other quantitative design we will discuss in this book—experimental design. Questionnaires in experiments are called pretests and posttests and they measure how participants change over time as a result of an intervention (e.g., a group therapy session) or a stimulus (e.g., watching a video of a political speech) introduced by the researcher. We will discuss experiments in greater detail in Chapter 13, but if testing an intervention or measuring how people react to something you do sounds like what you want to do with your project, experiments might be the best fit for you.

Surveys, on the other hand, do not measure the impact of an intervention or stimulus introduced by the researcher. Instead, surveys look for patterns that already exist in the world based on how people self-report on a questionnaire. Self-report simply means that the participants in your research study are answering questions about themselves, regardless of whether they are presented on paper, electronically, or read aloud by the researcher. Questionnaires structure self-report data into a standardized format—with everyone receiving the exact same questions and answer choices in the same order^[1]—which makes comparing data across participants much easier. Researchers using surveys try to influence their participants as little as possible because they want honest answers.

Questionnaires are completed by individual people, so the unit of observation is almost always individuals, rather than groups or organizations. Generally speaking, individuals provide the most informed data about their own lives and experiences, so surveys often also use individuals as the unit of analysis. Surveys are also helpful in analyzing dyads, families, groups, organizations, and communities, but regardless of the unit of analysis, the unit of observation for surveys is usually individuals. Keep this in mind as you think about sampling for your project.

In some cases, getting the most-informed person to complete your questionnaire may not be feasible. As we discussed in Chapter 2 and Chapter 6, ethical duties to protect clients and vulnerable community members mean student research projects often study practitioners and other less-vulnerable populations rather than clients and community members. The ethical supervision needed via the IRB to complete projects that pose significant risks to participants takes time and effort, and as a result, student projects often rely on key informants like clinicians, teachers, and administrators who are less likely to be harmed by the survey. Key informants are people who are especially knowledgeable about your topic. If your study is about nursing, you should probably survey nurses. These considerations are more thoroughly addressed in Chapter 10. Sometimes, participants complete surveys on behalf of people in your target population who are infeasible to survey for some reason. Some examples of key informants include a head of household completing a survey about family finances or an administrator completing a survey about staff morale on behalf of their employees. In this case, the survey respondent is a proxy, providing their best informed guess about the responses other people might have chosen if they were able to complete the survey independently. You are relying on an individual unit of observation (one person filling out a self-report questionnaire) and group or organization unit of analysis (the family or organization the researcher wants to make conclusions about). Proxies are commonly used when the target population is not capable of providing consent or appropriate answers, as in young children and people with disabilities.

Proxies are relying on their best judgment of another person’s experiences, and while that is valuable information, it may introduce bias and error into the research process. Student research projects, due to time and resource constraints, often include sampling people with second-hand knowledge, and this is simply one of many common limitations of their findings. Remember, every project has limitations. Social work researchers look for the most favorable choices in design and methodology, as there are no perfect projects. If you are planning to conduct a survey of people with second-hand knowledge of your topic, consider reworking your research question to be about something they have more direct knowledge about and can answer easily. One common missed opportunity I see is student researchers who want to understand client outcomes (unit of analysis) by surveying practitioners (unit of observation). If a practitioner has a caseload of 30 clients, it’s not really possible to answer a question like “how much progress have your clients made?” on a survey. Would they just average all 30 clients together? Instead, design a survey that asks them about their education, professional experience, and other things they know about first-hand. By making your unit of analysis and unit of observation the same, you can ensure the people completing your survey are able to provide informed answers.

Researchers may introduce measurement error if the person completing the questionnaire does not have adequate knowledge or has a biased opinion about the phenomenon of interest. For instance, many schools of social work market themselves based on the rankings of social work programs published by US News and World Report. Last updated in 2019, the methodology for these rankings is simply to send out a survey to deans, directors, and administrators at schools of social work. No graduation rates, teacher evaluations, licensure pass rates, accreditation data, or other considerations are a part of these rankings. It’s literally a popularity contest in which each school is asked to rank the others on a scale of 1-5, and ranked by highest average score. What if an informant is unfamiliar with a school or has a personal bias against a school?^[2] This could significantly skew results. One might also question the validity of such a questionnaire in assessing something as important and economically impactful as the quality of social work education. We might envision how students might demand and create more authentic measures of school quality. 

In summary, survey design best fits with research projects that have the following attributes: 

• Researchers plan to collect their own raw data, rather than secondary analysis of existing data.
• Researchers have access to the most knowledgeable people (that you can feasibly and ethically sample) to complete the questionnaire.
• Research question is best answered with quantitative methods.
• Individuals are the unit of observation, and in many cases, the unit of analysis.
• Researchers will try to observe things objectively and try not to influence participants to respond differently.
• Research questions asks about indirect observables—things participants can self-report on a questionnaire.
• There are valid, reliable, and commonly used scales (or other self-report measures) for the variables in the research question.

Strengths of survey methods

Researchers employing survey research as a research design enjoy a number of benefits. First, surveys are an excellent way to gather lots of information from many people. In a study by Blackstone (2013)^[3] on older people’s experiences in the workplace, researchers were able to mail a written questionnaire to around 500 people who lived throughout the state of Maine at a cost of just over $1,000. This cost included printing copies of a seven-page survey, printing a cover letter, addressing and stuffing envelopes, mailing the survey, and buying return postage for the survey. We realize that $1,000 is nothing to sneeze at, but just imagine what it might have cost to visit each of those people individually to interview them in person. You would have to dedicate a few weeks of your life at least, drive around the state, and pay for meals and lodging to interview each person individually. Researchers can double, triple, or even quadruple their costs pretty quickly by opting for an in-person method of data collection over a mailed survey. Thus, surveys are relatively cost-effective.

Related to the benefit of cost-effectiveness is a survey’s potential for generalizability. Because surveys allow researchers to collect data from very large samples for a relatively low cost, survey methods lend themselves to probability sampling techniques, which we discussed in Chapter 10. When used with probability sampling approaches, survey research is the best method to use when one hopes to gain a representative picture of the attitudes and characteristics of a large group. Unfortunately, student projects are quite often not able to take advantage of the generalizability of surveys because they use availability sampling rather than the more costly and time-intensive random sampling approaches that are more likely to elicit a representative sample. While the conclusions drawn from availability samples have far less generalizability, surveys are still a great choice for student projects and they provide data that can be followed up on by well-funded researchers to generate generalizable research.

Survey research is particularly adept at investigating indirect observables. Indirect observables are things we have to ask someone to self-report because we cannot observe them directly, such as people’s preferences (e.g., political orientation), traits (e.g., self-esteem), attitudes (e.g., toward immigrants), beliefs (e.g., about a new law), behaviors (e.g., smoking or drinking), or factual information (e.g., income). Unlike qualitative studies in which these beliefs and attitudes would be detailed in unstructured conversations, surveys seek to systematize answers so researchers can make apples-to-apples comparisons across participants. Surveys are so flexible because you can ask about anything, and the variety of questions allows you to expand social science knowledge beyond what is naturally observable.

Survey research also tends to be a reliable method of inquiry. This is because surveys are standardized in that the same questions, phrased in exactly the same way, as they are posed to participants. Other methods, such as qualitative interviewing, which we’ll learn about in Chapter 18, do not offer the same consistency that a quantitative survey offers. This is not to say that all surveys are always reliable. A poorly phrased question can cause respondents to interpret its meaning differently, which can reduce that question’s reliability. Assuming well-constructed questions and survey design, one strength of this methodology is its potential to produce reliable results.

The versatility of survey research is also an asset. Surveys are used by all kinds of people in all kinds of professions. They can measure anything that people can self-report. Surveys are also appropriate for exploratory, descriptive, and explanatory research questions (though exploratory projects may benefit more from qualitative methods). Moreover, they can be delivered in a number of flexible ways, including via email, mail, text, and phone. We will describe the many ways to implement a survey later on in this chapter. 

In sum, the following are benefits of survey research:

• Cost-effectiveness
• Generalizability
• Variety
• Reliability
• Versatility

Weaknesses of survey methods

As with all methods of data collection, survey research also comes with a few drawbacks. First, while one might argue that surveys are flexible in the sense that you can ask any kind of question about any topic we want, once the survey is given to the first participant, there is nothing you can do to change the survey without biasing your results. Because surveys want to minimize the amount of influence that a researcher has on the participants, everyone gets the same questionnaire. Let’s say you mail a questionnaire out to 1,000 people and then discover, as responses start coming in, that your phrasing on a particular question seems to be confusing a number of respondents. At this stage, it’s too late for a do-over or to change the question for the respondents who haven’t yet returned their questionnaires. When conducting qualitative interviews or focus groups, on the other hand, a researcher can provide respondents further explanation if they’re confused by a question and can tweak their questions as they learn more about how respondents seem to understand them. Survey researchers often ask colleagues, students, and others to pilot test their questionnaire and catch any errors prior to sending it to participants; however, once researchers distribute the survey to participants, there is little they can do to change anything.

Depth can also be a problem with surveys. Survey questions are standardized; thus, it can be difficult to ask anything other than very general questions that a broad range of people will understand. Because of this, survey results may not provide as detailed of an understanding as results obtained using methods of data collection that allow a researcher to more comprehensively examine whatever topic is being studied. Let’s say, for example, that you want to learn something about voters’ willingness to elect an African American president. General Social Survey respondents were asked, “If your party nominated an African American for president, would you vote for him if he were qualified for the job?” (Smith, 2009).^[4] Respondents were then asked to respond either yes or no to the question. But what if someone’s opinion was more complex than could be answered with a simple yes or no? What if, for example, a person was willing to vote for an African American man, but only if that person was a conservative, moderate, anti-abortion, antiwar, etc. Then we would miss out on that additional detail when the participant responded “yes,” to our question. Of course, you could add a question to your survey about moderate vs. radical candidates, but could you do that for all of the relevant attributes of candidates for all people? Moreover, how do you know that moderate or antiwar means the same thing to everyone who participates in your survey? Without having a conversation with someone and asking them follow up questions, survey research can lack enough detail to understand how people truly think.

In sum, potential drawbacks to survey research include the following:

• Inflexibility
• Lack of depth
• Problems specific to cross-sectional surveys, which we will address in the next section.

Secondary analysis of survey data

This chapter is designed to help you conduct your own survey, but that is not the only option for social work researchers. Look back to Chapter 2 and recall our discussion of secondary data analysis. As we talked about previously, using data collected by another researcher can have a number of benefits. Well-funded researchers have the resources to recruit a large representative sample and ensure their measures are valid and reliable prior to sending them to participants. Before you get too far into designing your own data collection, make sure there are no existing data sets out there that you can use to answer your question. We refer you to Chapter 2 for all full discussion of the strengths and challenges of using secondary analysis of survey data.

12.2 Collecting data using surveys

As we discussed in the previous chapter, surveys are versatile and can be shaped and suited to most topics of inquiry. While that makes surveys a great research tool, it also means there are many options to consider when designing your survey. The two main considerations for designing surveys is how many times researchers will collect data from participants and how researchers contact participants and record responses to the questionnaire.

Cross-sectional surveys: A snapshot in time

Think back to the last survey you took. Did you respond to the questionnaire once or did you respond to it multiple times over a long period? Cross-sectional surveys are administered only one time. Chances are the last survey you took was a cross-sectional survey—a one-shot measure of a sample using a questionnaire. And chances are if you are conducting a survey to collect data for your project, it will be cross-sectional simply because it is more feasible to collect data once than multiple times.

Let’s take a very recent example, the COVID-19 pandemic. Enriquez and colleagues (2021)^[5] wanted to understand the impact of the pandemic on undocumented college students’ academic performance, attention to academics, financial stability, mental and physical health, and other factors. In cooperation with offices of undocumented student support at eighteen campuses in California, the researchers emailed undocumented students a few times from March through June of 2020 and asked them to participate in their survey via an online questionnaire. Their survey presents an compelling look at how COVID-19 worsened existing economic inequities in this population.

Strengths and weaknesses of cross-sectional surveys

Cross-sectional surveys are great. They take advantage of many of the strengths of survey design. They are easy to administer since you only need to measure your participants once, which makes them highly suitable for student projects. Keeping track of participants for multiple measures takes time and energy, two resources always under constraint in student projects. Conducting a cross-sectional survey simply requires collecting a sample of people and getting them to fill out your questionnaire—nothing more.

That convenience comes with a tradeoff. When you only measure people at one point in time, you can miss a lot. The events, opinions, behaviors, and other phenomena that such surveys are designed to assess don’t generally remain the same over time. Because nomothetic causal explanations seek a general, universal truth, surveys conducted a decade ago do not represent what people think and feel today or twenty years ago. In student research projects, this weakness is often compounded by the use of availability sampling, which further limits the generalizability of the results in student research projects to other places and times beyond the sample collected by the researcher. Imagine generalizing results on the use of telehealth in social work prior to the COVID-19 pandemic or managers’ willingness to allow employees to telecommute. Both as a result of shocks to the system—like COVID-19—and the linear progression of cultural, economic and social change—like human rights movements—cross-sectional surveys can never truly give us a timeless causal explanation. In our example about undocumented students during COVID-19, you can say something about the way things were in the moment that you administered your survey, but it is difficult to know whether things remained that way for long after you administered your survey or describe patterns that go back far in time.

Of course, just as society changes over time, so do people. Because cross-sectional surveys only measure people at one point in time, they have difficulty establishing cause-and-effect relationships for individuals because they cannot clearly establish whether the cause came before the effect. If your research question were about how school discipline (our independent variable) impacts substance use (our dependent variable), you would want to make that any changes in our dependent variable, substance use, came after changes in school discipline. That is, if your hypothesis is that says school discipline causes increases in substance use, you must establish that school discipline came first and increases in substance use came afterwards. However, it is perhaps just as likely that increased substance use might cause increases in school discipline. If you sent a cross-sectional survey to students asking them about their substance use and disciplinary record, you would get back something like “tried drugs or alcohol 6 times” and “has been suspended 5 times.” You could see whether similar patterns existed in other students, but you wouldn’t be able to tell which was the cause or the effect.

Because of these limitations, cross-sectional surveys are limited in how well they can establish whether a nomothetic causal relationship is true or not. Surveys are still a key part of establishing causality. But they need additional help and support to make causal arguments. That might come from combining data across surveys in meta-analyses and systematic reviews, integrating survey findings with theories that explain causal relationships among variables in the study, as well as corroboration from research using other designs, theories, and paradigms. Scientists can establish causal explanations, in part, based on survey research. However, in keeping with the assumptions of postpositivism, the picture of reality that emerges from survey research is only our best approximation of what is objectively true about human beings in the social world. Science requires a multi-disciplinary conversation among scholars to continually improve our understanding.

Longitudinal surveys: Measuring change over time

One way to overcome this sometimes-problematic aspect of cross-sectional surveys is to administer a longitudinal survey. Longitudinal surveys enable a researcher to make observations over some extended period of time. There are several types of longitudinal surveys, including trend, panel, and cohort surveys. We’ll discuss all three types here, along with retrospective surveys, which fall somewhere in between cross-sectional and longitudinal surveys.

The first type of longitudinal survey is called a trend survey. The main focus of a trend survey is, perhaps not surprisingly, trends. Researchers conducting trend surveys are interested in how people in a specific group change over time. Each time researchers gather data, they survey different people from the identified group because they are interested in the trends of the whole group, rather than changes in specific individuals. Let’s look at an example.

The Monitoring the Future Study is a trend study that described the substance use of high school children in the United States. It’s conducted annually by the National Institute on Drug Abuse (NIDA). Each year, the NIDA distributes surveys to children in high schools around the country to understand how substance use and abuse in that population changes over time. Perhaps surprisingly, fewer high school children reported using alcohol in the past month than at any point over the last 20 years—a fact that often surprises people because it cuts against the stereotype of adolescents engaging in ever-riskier behaviors. Nevertheless, recent data also reflected an increased use of e-cigarettes and the popularity of e-cigarettes with no nicotine over those with nicotine. By tracking these data points over time, we can better target substance abuse prevention programs towards the current issues facing the high school population.

Unlike trend surveys, panel surveys require the same people participate in the survey each time it is administered. As you might imagine, panel studies can be difficult and costly. Imagine trying to administer a survey to the same 100 people every year, for 5 years in a row. Keeping track of where respondents live, when they move, and when they change phone numbers takes resources that researchers often don’t have. However, when the researchers do have the resources to carry out a panel survey, the results can be quite powerful. The Youth Development Study (YDS), administered from the University of Minnesota, offers an excellent example of a panel study.

Since 1988, YDS researchers have administered an annual survey to the same 1,000 people. Study participants were in ninth grade when the study began, and they are now in their thirties. Several hundred papers, articles, and books have been written using data from the YDS. One of the major lessons learned from this panel study is that work has a largely positive impact on young people (Mortimer, 2003).^[6] Contrary to popular beliefs about the impact of work on adolescents’ school performance and transition to adulthood, work increases confidence, enhances academic success, and prepares students for success in their future careers. Without this panel study, we may not be aware of the positive impact that working can have on young people.

Another type of longitudinal survey is a cohort survey. In a cohort survey, the participants have a defining characteristic that the researcher is interested in studying. The same people don’t necessarily participate from year to year, but all participants must meet whatever categorical criteria fulfill the researcher’s primary interest. Common cohorts that researchers study include people of particular generations or people born around the same time period, graduating classes, people who began work in a given industry at the same time, or perhaps people who have some specific historical experience in common. An example of this sort of research can be seen in Lindert and colleagues (2020)^[7] work on healthy aging in men. Their article is a secondary analysis of longitudinal data collected as part of the Veterans Affairs Normative Aging Study conducted in 1985, 1988, and 1991.

Strengths and weaknesses of longitudinal surveys

All three types of longitudinal surveys share the strength that they permit a researcher to make observations over time. Whether a major world event takes place or participants mature, researchers can effectively capture the subsequent potential changes in the phenomenon or behavior of interest. This is the key strength of longitudinal surveys—their ability to establish temporality needed for nomothetic causal explanations. Whether your project investigates changes in society, communities, or individuals, longitudinal designs improve on cross-sectional designs by providing data at multiple points in time that better establish causality.

Of course, all of that extra data comes at a high cost. If a panel survey takes place over ten years, the research team must keep track of every individual in the study for those ten years, ensuring they have current contact information for their sample the whole time. Consider this study which followed people convicted of driving under the influence of drugs or alcohol (Kleschinsky et al., 2009).^[8] It took an average of 8.6 contacts for participants to complete follow-up surveys, and while this was a difficult-to-reach population, researchers engaging in longitudinal research must prepare for considerable time and expense in tracking participants. Keeping in touch with a participant for a prolonged period of time likely requires building participant motivation to stay in the study, maintaining contact at regular intervals, and providing monetary compensation. Panel studies are not the only costly longitudinal design. Trend studies need to recruit a new sample every time they collect a new wave of data at additional cost and time.

In my years as a research methods instructor, I have never seen a longitudinal survey design used in a student research project because students do not have enough time to complete them. Cross-sectional surveys are simply the most convenient and feasible option. Nevertheless, social work researchers with more time to complete their studies use longitudinal surveys to understand causal relationships that they cannot manipulate themselves. A researcher could not ethically experiment on participants by assigning a jail sentence or relapse, but longitudinal surveys allow us to systematically investigate such sensitive phenomena ethically. Indeed, because longitudinal surveys observe people in everyday life, outside of the artificial environment of the laboratory (as in experiments), the generalizability of longitudinal survey results to real-world situations may make them superior to experiments, in some cases.

Table 12.1 summarizes these three types of longitudinal surveys.

Retrospective surveys: Good, but not the best of both worlds

Retrospective surveys try to strike a middle ground between the two types of surveys. They are similar to other longitudinal studies in that they deal with changes over time, but like a cross-sectional study, data are collected only once. In a retrospective survey, participants are asked to report events from the past. By having respondents report past behaviors, beliefs, or experiences, researchers are able to gather longitudinal-like data without actually incurring the time or expense of a longitudinal survey. Of course, this benefit must be weighed against the possibility that people’s recollections of their pasts may be faulty. Imagine that you are participating in a survey that asks you to respond to questions about your feelings on Valentine’s Day. As last Valentine’s Day can’t be more than 12 months ago, there is a good chance that you are able to provide a pretty accurate response of how you felt. Now let’s imagine that the researcher wants to know how last Valentine’s Day compares to previous Valentine’s Days, so the survey asks you to report on the preceding six Valentine’s Days. How likely is it that you will remember how you felt at each one? Will your responses be as accurate as they might have been if your data were collected via survey once a year rather reporting the past few years today? The main limitation with retrospective surveys are that they are not as reliable as cross-section or longitudinal surveys. That said, retrospective surveys are a feasible way to collect longitudinal data when the researcher only has access to the population once, and for this reason, they may be worth the drawback of greater risk of bias and error in the measurement process.

Because quantitative research seeks to build nomothetic causal explanations, it is important to determine the order in which things happen. When using survey design to investigate causal relationships between variables in a research question, longitudinal surveys are certainly preferable because they can track changes over time and therefore provide stronger evidence for cause-and-effect relationships. As we discussed, the time and cost required to administer a longitudinal survey can be prohibitive, and most survey research in the scholarly literature is cross-sectional because it is more feasible to collect data once. Well designed cross-sectional surveys provide can provide important evidence for a causal relationship, even if it is imperfect. Once you decide how many times you will collect data from your participants, the next step is to figure out how to get your questionnaire in front of participants.

Self-administered questionnaires

If you are planning to conduct a survey for your research project, chances are you have thought about how you might deliver your survey to participants. If you don’t have a clear picture yet, look back at your work from Chapter 11 on the sampling approach for your project. How are you planning to recruit participants from your sampling frame? If you are considering contacting potential participants via phone or email, perhaps you want to collect your data using a phone or email survey attached to your recruitment materials. If you are planning to collect data from students, colleagues, or other people you most commonly interact with in-person, maybe you want to consider a pen-and-paper survey to collect your data conveniently. As you review the different approaches to administering surveys below, consider how each one matches with your sampling approach and the contact information you have for study participants. Ensure that your sampling approach is feasible conduct before building your survey design from it. For example, if you are planning to administer an online survey, make sure you have email addresses to send your questionnaire or permission to post your survey to an online forum.

Surveys are a versatile research approach. Survey designs vary not only in terms of when they are administered but also in terms of how they are administered. One common way to collect data is in the form of self-administered questionnaires. Self-administered means that the research participant completes the questions independently, usually in writing. Paper questionnaires can be delivered to participants via mail or in person whenever you see your participants. Generally, student projects use in-person collection of paper questionnaires, as mail surveys require physical addresses, spending money, and waiting for the mail. It is common for academic researchers to administer surveys in large social science classes, so perhaps you have taken a survey that was given to you in-person during undergraduate classes. These professors were taking advantage of the same convenience sampling approach that student projects often do. If everyone in your sampling frame is in one room, going into that room and giving them a quick paper survey to fill out is a feasible and convenient way to collect data. Availability sampling may involve asking your sampling frame to complete your study during when they naturally meet—colleagues at a staff meeting, students in the student lounge, professors in a faculty meeting—and self-administered questionnaires are one way to take advantage of this natural grouping of your target population. Try to pick a time and situation when people have the downtime needed to complete your questionnaire, and you can maximize the likelihood that people will participate in your in-person survey. Of course, this convenience may come at the cost of privacy and confidentiality. If your survey addresses sensitive topics, participants may alter their responses because they are in close proximity to other participants while they complete the survey. Regardless of whether participants feel self-conscious or talk about their answers with one another, by potentially altering the participants’ honest response you may have introduced bias or error into your measurement of the variables in your research question.

Because student research projects often rely on availability sampling, collecting data using paper surveys from whoever in your sampling frame is convenient makes sense because the results will be of limited generalizability. But for researchers who aim to generalize (and students who want to publish their study!), self-administered surveys may be better distributed via the mail or electronically. While is very unusual for a student project to send a questionnaire via the mail, this method is used quite often in the scholarly literature and for good reason. Survey researchers who deliver their surveys via postal mail often provide some advance notice to respondents about the survey to get people thinking and preparing to complete it. They may also follow up with their sample a few weeks after their survey has been sent out. This can be done not only to remind those who have not yet completed the survey to please do so but also to thank those who have already returned the survey. Most survey researchers agree that this sort of follow-up is essential for improving mailed surveys’ return rates (Babbie, 2010). [6] Other helpful tools to increase response rate are to create an attractive and professional survey, offer monetary incentives, and provide a pre-addressed, stamped return envelope. These are also effective for other types of surveys.

While snail mail may not be feasible for student project, it is increasingly common for student projects and social science projects to use email and other modes of online delivery like social media to collect responses to a questionnaire. Researchers like online delivery for many reasons. It’s quicker than knocking on doors in a neighborhood for an in-person survey or waiting for mailed surveys to be returned. It’s cheap, too. There are many free tools like Google Forms and Survey Monkey (which includes a premium option). While you are affiliated with a university, you may have access to commercial research software like Redcap or Qualtrics which provide much more advanced tools for collecting survey data than free options. Online surveys can take advantage of the advantages of computer-mediated data collection by playing a video before asking a question, tracking how long participants take to answer each question, and making sure participants don’t fill out the survey more than once (to name a few examples. Moreover, survey data collected via online forms can be exported for analysis in spreadsheet software like Google Sheets or Microsoft Excel or statistics software like SPSS or JASP, a free and open-source alternative to SPSS. While the exported data still need to be checked before analysis, online distribution saves you the trouble of manually inputting every response a participant writes down on a paper survey into a computer to analyze.

The process of collecting data online depends on your sampling frame and approach to recruitment. If your project plans to reach out to people via email to ask them to participate in your study, you should attach your survey to your recruitment email. You already have their attention, and you may not get it again (even if you remind them). Think pragmatically. You will need access to the email addresses of people in your sampling frame. You may be able to piece together a list of email addresses based on public information (e.g., faculty email addresses are on their university webpage, practitioner emails are in marketing materials). In other cases, you may know of a pre-existing list of email addresses to which your target population subscribes (e.g., all undergraduate students in a social work program, all therapists at an agency), and you will need to gain the permission of the list’s administrator recruit using the email platform. Other projects will identify an online forum in which their target population congregates and recruit participants there. For example, your project might identify a Facebook group used by students in your social work program or practitioners in your local area to distribute your survey. Of course, you can post a survey to your personal social media account (or one you create for the survey), but depending on your question, you will need a detailed plan on how to reach participants with enough relevant knowledge about your topic to provide informed answers to your questionnaire.

Many of the suggestions that were provided earlier to improve the response rate of hard copy questionnaires also apply to online questionnaires, including the development of an attractive survey and sending reminder emails. One challenge not present in mail surveys is the spam filter or junk mail box. While people will at least glance at recruitment materials send via mail, email programs may automatically filter out recruitment emails so participants never see them at all. While the financial incentives that can be provided online differ from those that can be given in person or by mail, online survey researchers can still offer completion incentives to their respondents. Over the years, I’ve taken numerous online surveys. Often, they did not come with any incentive other than the joy of knowing that I’d helped a fellow social scientist do their job. However, some surveys have their perks. One survey offered a coupon code to use for $30 off any order at a major online retailer and another allowed the opportunity to be entered into a lottery with other study participants to win a larger gift, such as a $50 gift card or a tablet computer. Student projects should not pay participants unless they have grant funding to cover that cost, and there should be no expectations of any out-of-pocket costs for students to complete their research project.

One area in which online surveys are less suitable than mail or in-person surveys is when your target population includes individuals with limited, unreliable, or no access to the internet or individuals with limited computer skills. For these groups, an online survey is inaccessible. At the same time, online surveys offer the most feasible way to collect data anonymously. By posting recruitment materials to a Facebook group or list of practitioners at an agency, you can avoid collecting identifying information from people who participated in your study. For studies that address sensitive topics, online surveys also offer the opportunity to complete the survey privately (again, assuming participants have access to a phone or personal computer). If you have the person’s email address, physical address, or met them in-person, your participants are not anonymous, but if you need to collect data anonymously, online tools offer a feasible way to do so.

The best way to collect data using self-administered questionnaires depends on numerous factors. The strengths and weaknesses of in-person, mail, and electronic self-administered surveys are reviewed in Table 12.2. Ultimately, you must make the best decision based on its congruence with your sampling approach and what you can feasibly do. Decisions about survey design should be done with a deep appreciation for your study’s target population and how your design choices may impact their responses to your survey.

Quantitative interviews: Researcher-administered questionnaires

There are some cases in which it is not feasible to provide a written questionnaire to participants, either on paper or digitally. In this case, the questionnaire can be administered verbally by the researcher to respondents. Rather than the participant reading questions independently on paper or digital screen, the researcher reads questions and answer choices aloud to participants and records their responses for analysis. Another word for this kind of questionnaire is an interview schedule. It’s called a schedule because each question and answer is posed in the exact same way each time.

Consistency is key in quantitative interviews. By presenting each question and answer option in exactly the same manner to each interviewee, the researcher minimizes the potential for the interviewer effect, which encompasses any possible changes in interviewee responses based on how or when the researcher presents question-and-answer options. Additionally, in-person surveys may be video recorded and you can typically take notes without distracting the interviewee due to the closed-ended nature of survey questions, making them helpful for identifying how participants respond to the survey or which questions might be confusing.

Quantitative interviews can take place over the phone or in-person. Phone surveys are often conducted by political polling firms to understand how the electorate feels about certain candidates or policies. In both cases, researchers verbally pose questions to participants. For many years, live-caller polls (a live human being calling participants in a phone survey) were the gold-standard in political polling. Indeed, phone surveys were excellent for drawing representative samples prior to mobile phones. Unlike landlines, cell phone numbers are portable across carriers, associated with individuals as opposed to households, and do not change their first three numbers when people move to a new geographical area. For this reason, many political pollsters have moved away from random-digit phone dialing and toward a mix of data collection strategies like texting-based surveys or online panels to recruit a representative sample and generalizable results for the target population (Silver, 2021).^[9]

I guess I should admit that I often decline to participate in phone studies when I am called. In my defense, it’s usually just a customer service survey! My point is that it is easy and even socially acceptable to abruptly hang up on an unwanted caller asking you to participate in a survey, and given the high incidence of spam calls, many people do not pick up the phone for numbers they do not know. We will discuss response rates in greater detail at the end of the chapter. One of the benefits of phone surveys is that a person can complete them in their home or a safe place. At the same time, a distracted participant who is cooking dinner, tending to children, or driving may not provide accurate answers to your questions. Phone surveys make it difficult to control the environment in which a person answers your survey. When administering a phone survey, the researcher can record responses on a paper questionnaire or directly into a computer program. For large projects in which many interviews must be conducted by research staff, computer-assisted telephone interviewing (CATI) ensures that each question and answer option are presented the same way and input into the computer for analysis. For student projects, you can read from a digital or paper copy of your questionnaire and record participants responses into a spreadsheet program like Excel or Google Sheets.

Interview schedules must be administered in such a way that the researcher asks the same question the same way each time. While questions on self-administered questionnaires may create an impression based on the way they are presented, having a researcher pose the questions verbally introduces additional variables that might influence a respondent. Controlling one’s wording, tone of voice, and pacing can be difficult over the phone, but it is even more challenging in-person because the researcher must also control their non-verbal expressions and behaviors that may bias survey respondents. Even a slight shift in emphasis or wording may bias the respondent to answer differently. As we’ve mentioned earlier, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. But what happens if a participant asks a question of the researcher? Unlike self-administered questionnaires, quantitative interviews allow the participant to speak directly with the researcher if they need more information about a question. While this can help participants respond accurately, it can also introduce inconsistencies between how the survey administered to each participant. Ideally, the researcher should draft sample responses researchers might provide to participants if they are confused on certain survey items. The strengths and weaknesses of phone and in-person quantitative interviews are summarized in Table 12.3 below.

Students using survey design should settle on a delivery method that presents the most favorable tradeoff between strengths and challenges for their unique context. One key consideration is your sampling approach. If you already have the participant on the phone and they agree to be a part of your sample…you may as well ask them your survey questions right then if the participant can do so. These feasibility concerns make in-person quantitative interviews a poor fit for student projects. It is far easier and quicker to distribute paper surveys to a group of people it is to administer the survey verbally to each participant individually. Ultimately, you are the one who has to carry out your research design. Make sure you can actually follow your plan!

12.3 Bias and cultural considerations

The logic of survey design

As you may have noticed with survey designs, everything about them is intentional—from the delivery method, to question wording, to what response options are offered. It’s helpful to spell out the underlying logic behind survey design and how well it meets the criteria for nomothetic causal explanations. Because we are trying to isolate the causal relationship between our dependent and independent variable, we must try to control for as many possible confounding factors as possible. Researchers using survey design do this in multiple ways:

• Using well-established, valid, and reliable measures of key variables, including triangulating variables using multiple measures
• Measuring control variables and including them in their statistical analysis
• Avoiding biased wording, presentation, or procedures that might influence the sample to respond differently
• Pilot testing questionnaires, preferably with people similar to the sample

In other words, survey researchers go through a lot of trouble to make sure they are not the ones causing the changes they observe in their study. Of course, every study falls a little short of this ideal bias-free design, and some studies fall far short of it. This section is all about how bias and error can inhibit the ability of survey results to meaningfully tell us about causal relationships in the real world.

Bias in questionnaires, questions, and response options

The use of surveys is based on methodological assumptions common to research in the postpositivist paradigm. Figure 12.5 presents a model the methodological assumptions behind survey design—what researchers assume is the cognitive processes that people engage in when responding to a survey item (Sudman, Bradburn, & Schwarz, 1996).^[10] Respondents must interpret the question, retrieve relevant information from memory, form a tentative judgment, convert the tentative judgment into one of the response options provided (e.g., a rating on a 1-to-7 scale), and finally edit their response as necessary.

Consider, for example, the following questionnaire item:

1. How many alcoholic drinks do you consume in a typical day?

1. • a lot more than average
   • somewhat more than average
   • average
   • somewhat fewer than average
   • a lot fewer than average

Although this item at first seems straightforward, it poses several difficulties for respondents. First, they must interpret the question. For example, they must decide whether “alcoholic drinks” include beer and wine (as opposed to just hard liquor) and whether a “typical day” is a typical weekday, typical weekend day, or both. Even though Chang and Krosnick (2003)^[11] found that asking about “typical” behavior has been shown to be more valid than asking about “past” behavior, their study compared “typical week” to “past week” and may be different when considering typical weekdays or weekend days).

Once respondents have interpreted the question, they must retrieve relevant information from memory to answer it. But what information should they retrieve, and how should they go about retrieving it? They might think vaguely about some recent occasions on which they drank alcohol, they might carefully try to recall and count the number of alcoholic drinks they consumed last week, or they might retrieve some existing beliefs that they have about themselves (e.g., “I am not much of a drinker”). Then they must use this information to arrive at a tentative judgment about how many alcoholic drinks they consume in a typical day. For example, this mental calculation might mean dividing the number of alcoholic drinks they consumed last week by seven to come up with an average number per day. Then they must format this tentative answer in terms of the response options actually provided. In this case, the options pose additional problems of interpretation. For example, what does “average” mean, and what would count as “somewhat more” than average? Finally, they must decide whether they want to report the response they have come up with or whether they want to edit it in some way. For example, if they believe that they drink a lot more than average, they might not want to report that for fear of looking bad in the eyes of the researcher, so instead, they may opt to select the “somewhat more than average” response option.

At first glance, this question is clearly worded and includes a set of mutually exclusive, exhaustive, and balanced response options. However, it is difficult to follow the logic of what is truly being asked. Again, this complexity can lead to unintended influences on respondents’ answers. Confounds like this are often referred to as context effects because they are not related to the content of the item but to the context in which the item appears (Schwarz & Strack, 1990).^[12] For example, there is an item-order effect when the order in which the items are presented affects people’s responses. One item can change how participants interpret a later item or change the information that they retrieve to respond to later items. For example, researcher Fritz Strack and his colleagues asked college students about both their general life satisfaction and their dating frequency (Strack, Martin, & Schwarz, 1988).^[13] When the life satisfaction item came first, the correlation between the two was only −.12, suggesting that the two variables are only weakly related. But when the dating frequency item came first, the correlation between the two was +.66, suggesting that those who date more have a strong tendency to be more satisfied with their lives. Reporting the dating frequency first made that information more accessible in memory so that they were more likely to base their life satisfaction rating on it.

The response options provided can also have unintended effects on people’s responses (Schwarz, 1999).^[14] For example, when people are asked how often they are “really irritated” and given response options ranging from “less than once a year” to “more than once a month,” they tend to think of major irritations and report being irritated infrequently. But when they are given response options ranging from “less than once a day” to “several times a month,” they tend to think of minor irritations and report being irritated frequently. People also tend to assume that middle response options represent what is normal or typical. So if they think of themselves as normal or typical, they tend to choose middle response options (i.e., fence-sitting). For example, people are likely to report watching more television when the response options are centered on a middle option of 4 hours than when centered on a middle option of 2 hours. To mitigate against order effects, rotate questions and response items when there is no natural order. Counterbalancing or randomizing the order of presentation of the questions in online surveys are good practices for survey questions and can reduce response order effects that show that among undecided voters, the first candidate listed in a ballot receives a 2.5% boost simply by virtue of being listed first!^[15]

Other context effects that can confound the causal relationship under examination in a survey include social desirability bias, recall bias, and common method bias. As we discussed in Chapter 11, social desirability bias occurs when we create questions that lead respondents to answer in ways that don’t reflect their genuine thoughts or feelings to avoid being perceived negatively. With negative questions such as, “do you think that your project team is dysfunctional?”, “is there a lot of office politics in your workplace?”, or “have you ever illegally downloaded music files from the Internet?”, the researcher may not get truthful responses. This tendency among respondents to “spin the truth” in order to portray themselves in a socially desirable manner is called social desirability bias, which hurts the validity of responses obtained from survey research. There is practically no way of overcoming social desirability bias in a questionnaire survey outside of wording questions using nonjudgmental language. However, in a quantitative interview, a researcher may be able to spot inconsistent answers and ask probing questions or use personal observations to supplement respondents’ comments.

As you can see, participants’ responses to survey questions often depend on their motivation, memory, and ability to respond. Particularly when dealing with events that happened in the distant past, respondents may not adequately remember their own motivations or behaviors, or perhaps their memory of such events may have evolved with time and are no longer retrievable. This phenomenon is know as recall bias. For instance, if a respondent is asked to describe their utilization of computer technology one year ago, their response may not be accurate due to difficulties with recall. One possible way of overcoming the recall bias is by anchoring the respondent’s memory in specific events as they happened, rather than asking them to recall their perceptions and motivations from memory.

Cross-sectional and retrospective surveys are particularly vulnerable to recall bias as well as common method bias. Common method bias can occur when measuring both independent and dependent variables at the same time (like a cross-section survey) and using the same instrument (like a questionnaire). In such cases, the phenomenon under investigation may not be adequately separated from measurement artifacts. Standard statistical tests are available to test for common method bias, such as Harmon’s single-factor test (Podsakoff et al. 2003),^[16], Lindell and Whitney’s (2001)^[17] market variable technique, and so forth. This bias can be potentially avoided if the independent and dependent variables are measured at different points in time, using a longitudinal survey design, or if these variables are measured using different data sources, such as medical or student records rather than self-report questionnaires.

Bias in recruitment and response to surveys

So far, we have discussed errors that researchers make when they design questionnaires that accidentally influence participants to respond one way or another. However, even well designed questionnaires can produce biased results when administered to survey respondents because of the biases in who actually responds to your survey.

Survey research is notorious for its low response rates. A response rate of 15-20% is typical in a mail survey, even after two or three reminders. If the majority of the targeted respondents fail to respond to a survey, then a legitimate concern is whether non-respondents are not responding due to a systematic reason, which may raise questions about the validity and generalizability of the study’s results, especially as this relates to the representativeness of the sample. This is known as non-response bias. For instance, dissatisfied customers tend to be more vocal about their experience than satisfied customers, and are therefore more likely to respond to satisfaction questionnaires. Hence, any respondent sample is likely to have a higher proportion of dissatisfied customers than the underlying population from which it is drawn.^[18] In this instance, the results would not be generalizable beyond this one biased sample. Here are several strategies for addressing non-response bias:

• Advance notification: A short letter sent in advance to the targeted respondents soliciting their participation in an upcoming survey can prepare them and improve likelihood of response. The letter should state the purpose and importance of the study, mode of data collection (e.g., via a phone call, a survey form in the mail, etc.), and appreciation for their cooperation. A variation of this technique may request the respondent to return a postage-paid postcard indicating whether or not they are willing to participate in the study.
• Ensuring that content is relevant: If a survey examines issues of relevance or importance to respondents, then they are more likely to respond.
• Creating a respondent-friendly questionnaire: Shorter survey questionnaires tend to elicit higher response rates than longer questionnaires. Furthermore, questions that are clear, inoffensive, and easy to respond to tend to get higher response rates.
• Having the project endorsed: For organizational surveys, it helps to gain endorsement from a senior executive attesting to the importance of the study to the organization. Such endorsements can be in the form of a cover letter or a letter of introduction, which can improve the researcher’s credibility in the eyes of the respondents.
• Providing follow-up requests: Multiple follow-up requests may coax some non-respondents to respond, even if their responses are late.
• Ensuring that interviewers are properly trained: Response rates for interviews can be improved with skilled interviewers trained on how to request interviews, use computerized dialing techniques to identify potential respondents, and schedule callbacks for respondents who could not be reached.
• Providing incentives: Response rates, at least with certain populations, may increase with the use of incentives in the form of cash or gift cards, giveaways such as pens or stress balls, entry into a lottery, draw or contest, discount coupons, the promise of contribution to charity, and so forth.
• Providing non-monetary incentives: Organizations in particular are more prone to respond to non-monetary incentives than financial incentives. An example of such a non-monetary incentive sharing trainings and other resources based on the results of a project with a key stakeholder.
• Making participants fully aware of confidentiality and privacy: Finally, assurances that respondents’ private data or responses will not fall into the hands of any third party may help improve response rates.

Nonresponse bias impairs the ability of the researcher to generalize from the total number of respondents in the sample to the overall sampling frame. Of course, this assumes that the sampling frame is itself representative and generalizable to the larger target population. Sampling bias is present when the people in our sampling frame or the approach we use to sample them results in a sample that does not represent our population in some way. Telephone surveys conducted by calling a random sample of publicly available telephone numbers will systematically exclude people with unlisted telephone numbers, mobile phone numbers, and will include a disproportionate number of respondents who have land-line telephone service and stay home during much of the day, such as people who are unemployed, disabled, or of advanced age. Likewise, online surveys tend to include a disproportionate number of students and younger people who are more digitally connected, and systematically exclude people with limited or no access to computers or the Internet, such as the poor and the elderly. A different kind of sampling bias relates to generalizing from key informants to a target population, such as asking teachers (or parents) about the academic learning of their students (or children) or asking CEOs about operational details in their company. These sampling frames may provide a clearer picture of what key informants think and feel, rather than the target population.

Cultural bias

The acknowledgement that most research in social work and other adjacent fields is overwhelmingly based on so-called WEIRD (Western, educated, industrialized, rich and democratic) populations—a topic we discussed in Chapter 10—has given way to intensified research funding, publication, and visibility of collaborative cross-cultural studies across the social sciences that expand the geographical range of study populations. Many of the so-called non-WEIRD communities who increasingly participate in research are Indigenous, from low- and middle-income countries in the global South, live in post-colonial contexts, and/or are marginalized within their political systems, revealing and reproducing power differentials between researchers and researched (Whiteford & Trotter, 2008).^[19] Cross-cultural research has historically been rooted in racist, capitalist ideas and motivations (Gordon, 1991).^[20] Scholars have long debated whether research aiming to standardize cross-cultural measurements and analysis is tacitly engaged and/or continues to be rooted in colonial and imperialist practices (Kline et al., 2018; Stearman, 1984).^[21] Given this history, it is critical that scientists reflect upon these issues and be accountable to their participants and colleagues for their research practices. We argue that cross-cultural research be grounded in the recognition of the historical, political, sociological and cultural forces acting on the communities and individuals of focus. These perspectives are often contrasted with ‘science’; here we argue that they are necessary as a foundation for the study of human behavior.

We stress that our goal is not to review the literature on colonial or neo-colonial research practices, to provide a comprehensive primer on decolonizing approaches to field research, nor to identify or admonish past harms in these respects—harms to which many of the authors of this piece would readily admit. Furthermore, we acknowledge that we ourselves are writing from a place of privilege as researchers educated and trained in disciplines with colonial pasts. Our goal is simply to help students understand the broader issues in cross-cultural studies for appropriate consideration of diverse communities and culturally appropriate methodologies for student research projects.

Equivalence of measures across cultures

Data collection methods largely stemming from WEIRD intellectual traditions are being exported to a range of cultural contexts. This is often done with insufficient consideration of the translatability (e.g. equivalence or applicability) or implementation of such concepts and methods in different contexts, as already well documented (e.g., Hruschka et al., 2018).^[22] For example, in a developmental psychology study conducted by Broesch and colleagues (2011),^[23] the research team exported a task to examine the development and variability of self-recognition in children across cultures. Typically, this milestone is measured by surreptitiously placing a mark on a child’s forehead and allowing them to discover their reflective image and the mark in a mirror. While self-recognition in WEIRD contexts typically manifests in children by 18 months of age, the authors tested found that only 2 out of 82 children (aged 1–6 years) ‘passed’ the test by removing the mark using the reflected image. The authors’ interpretation of these results was that the test produced false negatives and instead measured implicit compliance to the local authority figure who placed the mark on the child. This raises the possibility that the mirror test may lack construct validity in cross-cultural contexts—in other words, that it may not measure the theoretical construct it was designed to measure.

As we discussed previously, survey researchers want to make sure everyone receives the same questionnaire, but how can we be sure everyone understands the questionnaire in the same way? Cultural equivalence means that a measure produces comparable data when employed in different cultural populations (Van de Vijver & Poortinga, 1992).^[24] If concepts differ in meaning across cultures, cultural bias may better explain what is going on with your key variables better than your hypotheses. Cultural bias may result because of poor item translation, inappropriate content of items, and unstandardized procedures (Waltz et al., 2010).^[25] Of particular importance is construct bias, or “when the construct measured is not identical across cultures or when behaviors that characterize the construct are not identical across cultures” (Meiring et al., 2005, p. 2)^[26] Construct bias emerges when there is: a) disagreement about the appropriateness of content, b) inadequate sampling, c) underrepresentation of the construct, and d) incomplete overlap of the construct across cultures (Van de Vijver & Poortinga, 1992).^[27]

Addressing cultural bias

To address these issues, we propose that careful scrutiny of (a) study site selection, (b) community involvement and (c) culturally appropriate research methods. Particularly for those initiating collaborative cross-cultural projects, we focus here on pragmatic and implementable steps. For student researchers, it is important to be aware of these issues and assess for them in the strengths and limitations of your own study, though the degree to which you can feasibly implement some of these measures will be impaired by a lack of resources.

Study site selection

Researchers are increasingly interested in cross-cultural research applicable outside of WEIRD contexts., but this has sometimes led to an uncritical and haphazard inclusion of ‘non-WEIRD’ populations in cross-cultural research without further regard for why specific populations should be included (Barrett, 2020).^[28] One particularly egregious example is the grouping of all non-Western populations as a comparative sample to the cultural West (i.e. the ‘West versus rest’ approach) is often unwittingly adopted by researchers performing cross-cultural research (Henrich, 2010).^[29] Other researcher errors include the exoticization of particular cultures or viewing non-Western cultures as a window into the past rather than cultures that have co-evolved over time.

Thus, some of the cultural biases in survey research emerge when researchers fail to identify a clear theoretical justification for inclusion of any subpopulation—WEIRD or not—based on knowledge of the relevant cultural and/or environmental context (see Tucker, 2017^[30] for a good example). For example, a researcher asking about satisfaction with daycare must acquire the relevant cultural and environmental knowledge about a daycare that caters exclusively to Orthodox Jewish families. Simply including this study site without doing appropriate background research and identifying a specific aspect of this cultural group that is of theoretical interest in your study (e.g., spirituality and parenthood) indicates a lack of rigor in research. It undercuts the validity and generalizability of your findings by introducing sources of cultural bias that are unexamined in your study.

Sampling decisions are also important as they involve unique ethical and social challenges. For example, foreign researchers (as sources of power, information and resources) represent both opportunities for and threats to community members. These relationships are often complicated by power differentials due to unequal access to wealth, education and historical legacies of colonization. As such, it is important that investigators are alert to the possible bias among individuals who initially interact with researchers, to the potential negative consequences for those excluded, and to the (often unspoken) power dynamics between the researcher and their study participants (as well as among and between study participants).

We suggest that a necessary first step is to carefully consult existing resources outlining best practices for ethical principles of research before engaging in cross-cultural research. Many of these resources have been developed over years of dialogue in various academic and professional societies (e.g. American Anthropological Association, International Association for Cross Cultural Psychology, International Union of Psychological Science). Furthermore, communities themselves are developing and launching research-based codes of ethics and providing carefully curated open-access materials such as those from the Indigenous Peoples’ Health Research Centre, often written in consultation with ethicists in low- to middle-income countries (see Schroeder et al., 2019).^[31]

Community involvement

Too often researchers engage in ‘extractive’ research, whereby a researcher selects a study community and collects the necessary data to exclusively further their own scientific and/or professional goals without benefiting the community. This reflects a long history of colonialism in social science. Extractive methods lead to methodological flaws and alienate participants from the scientific process, poisoning the well of scientific knowledge on a macro level. Many researchers are associated with institutions tainted with colonial, racist and sexist histories, sentiments and in some instances perpetuating into the present. Much cross-cultural research is carried out in former or contemporary colonies, and in the colonial language. Explicit and implicit power differentials create ethical challenges that can be acknowledged by researchers and in the design of their study (see Schuller, 2010^[32] for an example in which the power and politics of various roles played by researchers).

An understanding of cultural norms may ensure that data collection and questionnaire design are culturally and linguistically relevant. This can be achieved by implementing several complementary strategies. A first step may be to collaborate with members of the study community to check the relevance of the instruments being used. Incorporating perspectives from the study community from the outset can reduce the likelihood of making scientific errors in measurement and inference (First Nations Information Governance Centre, 2014).^[33]

An additional approach is to use mixed methods in data collection, such that each method ‘checks’ the data collected using the other methods. A recent paper by Fisher and Poortinga (2018)^[34] provides suggestions for a rigorous methodological approach to conducting cross-cultural comparative psychology, underscoring the importance of using multiple methods with an eye towards a convergence of evidence. A mixed-method approach can incorporate a variety of qualitative methods over and on top of a quantitative survey including open-ended questions, focus groups, and interviews.

Research design and methods

It is critical that researchers translate the language, technological references and stimuli as well as examine the underlying cultural context of the original method for assumptions that rely upon WEIRD epistemologies (Hrushcka, 2020).^[35] This extends to non-complex visual aids, attempting to ensure that even scales measure what the researcher is intending (see Purzycki and Lang, 2019^[36] for discussion on the use of a popular economic experiment in small-scale societies).

For more information on assessing cultural equivalence, consult this free training from RTI International, a well-regarded non-profit research firm, entitled “The essential role of language in survey design” and this free training from the Center for Capacity Building in Survey Methods and Statistics entitled “Questionnaire design: For surveys in 3MC (multinational, multiregional, and multi cultural) contexts. These trainings guide researchers using survey design through the details of evaluating and writing survey questions using culturally sensitive language. Moreover, if you are planning to conduct cross-cultural research, you should consult this guide for assessing measurement equivalency and bias across cultures, as well.