Are female and male athletes at the professional and college levels treated equally? You might think decades after the passage of Title IX (the civil rights law that prohibits sex discrimination in education including athletics) and with growing visibility of women athletes in sports, such as golf, basketball, hockey, and tennis, that the answer would be an easy yes. But in the early 2000s, Professor Michael Messner’s (2002) unobtrusive research showed otherwise, as did Professors Jo Ann M. Buysse and Melissa Sheridan Embser-Herbert’s (2004) content analysis of college athletics media guide photographs.

In fact, Buysse and Embser-Herbert’s unobtrusive research showed that traditional definitions of femininity were fiercely maintained through colleges’ visual representations of women athletes as passive and overtly feminine (as opposed to strong and athletic). In addition, Messner and colleagues’ (Messner, Duncan, & Jensen, 1993) content analysis of verbal commentary in televised coverage of men’s and women’s sports showed that announcers’ comments varied depending on an athlete’s gender identity. Such commentary not only infantilized women athletes but also asserted an ambivalent stance toward their accomplishments. Without this unobtrusive research we might have been inclined to think that more had changed for women athletes in the 30 years since it passed than actually had changed.

Chapter Outline

• 10.1 Unobtrusive research: What is it and when should it be used?
• 10.2 Strengths and weaknesses of unobtrusive research
• 10.3 Unobtrusive data collected by the researcher
• 10.4 Secondary data analysis

Content Advisory

This chapter discusses or mentions the following topics: sexism, racism, depression, suicide, and cognitive impairment among older adults.

In this chapter, we will explore unobtrusive methods of collecting data. Unobtrusive research refers to methods of collecting data that don’t interfere with the subjects under study (i.e., these methods are not “obtrusive”). Both qualitative and quantitative researchers use unobtrusive research methods. Unobtrusive methods share the unique quality that they do not require the researcher to interact with the people she is studying. It may seem strange that social work, a discipline dedicated to helping people, would employ a methodology that requires no interaction with human beings. But humans create plenty of evidence of their behaviors—they write letters to the editor of their local paper, they create various sources of entertainment for themselves such as movies and televisions shows, they consume goods, they walk on sidewalks, and they lie on the grass in public parks. All these activities leave something behind—worn paths, trash, recorded shows, and printed papers. These are all potential sources of data for the unobtrusive researcher.

Social workers interested in history are likely to use unobtrusive methods, which are also well suited to comparative research. Historical comparative research is “research that focuses either on one or more cases over time (the historical part) or on more than one nation or society at one point in time (the comparative part)” (Esterberg, 2002, p. 129). While not all unobtrusive researchers necessarily conduct historical, comparative, or even some combination of historical and comparative work, unobtrusive methods are well suited to such work. As an example, Melissa Weiner (2010) used a historical comparative approach to study racial barriers historically experienced by Jewish people and African Americans in New York City public schools. Weiner analyzed public records from several years of newspapers, trial transcripts, and several organizations as well as private manuscript collections to understand how parents, children, and other activists responded to inequality and worked to reform schools. Not only did this work inform readers about little-known similarities between Jewish and African American experiences, but it also informs current debates over inequalities experienced in public schools today.

In this chapter, we’ll examine content analysis as well as analysis of data collected by others. Both types of analyses have in common their use of data that do not require direct interaction with human subjects, but the particular type and source of data for each type of analysis differs. We’ll explore these similarities and differences in the following sections, after we look at some of the pros and cons of unobtrusive research methods.

As is true of the other research designs examined in this text, unobtrusive research has a number of strengths and weaknesses.

Strengths of unobtrusive research

Researchers who seek evidence of what people actually do, as opposed to what they say they do (as in survey and interview research), might wish to consider using unobtrusive methods. As we discussed in Chapter 8, researchers often, as a result of their presence, have an impact on the participants in their study simply because they measure and observe them.  This effect is a type of reactivity threat to internal validity called the Hawthorne effect.  As an example, compare how you would behave at work if you knew someone was watching you versus when you knew you were alone.  Because researchers conducting unobtrusive research do not alert participants to their presence, they do not need to be concerned about the effect of their research on their subjects. The Hawthorne effect is not a concern for unobtrusive researchers because they do not interact directly with their research participants. In fact, this is one of the major strengths of unobtrusive research.

Another benefit of unobtrusive research is that it can be relatively low-cost compared to some of the other methods we’ve discussed. Because “participants” are generally inanimate objects (e.g., web journal entries, television shows, historical speeches) as opposed to human beings, researchers may be able to access data without having to worry about paying participants for their time (though certainly travel to or access to some documents and archives can be costly).

Unobtrusive research is also pretty forgiving. It is far easier to correct mistakes made in data collection when conducting unobtrusive research than when using any of the other methods described in this textbook. Imagine what you would do, for example, if you realized at the end of conducting 50 in-depth interviews that you’d accidentally omitted two critical questions from your interview guide. What are your options? Re-interview all 50 participants? Try to figure out what they might have said based on their other responses? Reframe your research question? Scratch the project entirely? Obviously, none of these options is ideal. The same problems arise if a mistake is made in survey research. Fortunately for unobtrusive researchers, going back to the source of the data to gather more information or correct some problem in the original data collection is a relatively straightforward prospect.

Finally, as described in the previous section, unobtrusive research is well suited to studies that focus on processes that occur over time. While longitudinal surveys and long-term field observations are also suitable ways of gathering such information, they cannot examine processes that occurred decades before data collection began. Unobtrusive methods, on the other hand, enable researchers to investigate events and processes that have long since passed. They also do not rely on retrospective accounts of participants, which may be subject to errors in memory.

In summary, the strengths of unobtrusive research include the following:

• There is no possibility for the Hawthorne effect.
• It is cost-effective.
• It is easier than other methods to correct mistakes in data collection.
• They are are conducive to examining processes that occur over time or in the past.

Weaknesses of unobtrusive research

While there are many benefits to unobtrusive research, this method also comes with a unique set of drawbacks. Because unobtrusive researchers analyze data that may have been created or gathered for purposes entirely different from the researcher’s aim, problems of validity sometimes arise in such projects. It may also be the case that data sources measuring whatever a researcher wishes to examine simply do not exist. This means that unobtrusive researchers may be forced to tweak their original research interests or questions to better suit the data that are available to them. Finally, it can be difficult in unobtrusive research projects to account for context. In an interview, for example, the researcher can ask what events lead up to some occurrence, but this level of personal interaction is impossible in unobtrusive research. So, while it can be difficult to ascertain why something occurred in unobtrusive research, we can gain a good understanding of what has occurred.

In sum, the weaknesses of unobtrusive research include the following:

• There may be problems with validity.
• The topics or questions that can be investigated are limited by data availability.
• It can be difficult to see or account for social context.

This section focuses on how to gather data unobtrusively and what to do with those data once they have been collected. There are two main ways of gathering data unobtrusively: conducting a content analysis of existing texts and analyzing physical traces of human behavior. We’ll explore both approaches.

Content analysis

One way of conducting unobtrusive research is to analyze texts. Texts come in all kinds of formats. At its core, content analysis addresses the questions of “Who says what, to whom, why, how, and with what effect?” (Babbie, 2010, pp. 328–329). Content analysis is a type of unobtrusive research that involves the study of texts and their meaning. Here we use a more liberal definition of text than you might find in your dictionary. The text that content analysts investigate includes such things as actual written copy (e.g., newspapers or letters) and content that we might see or hear (e.g., speeches or other performances). Content analysts might also investigate more visual representations of human communication, such as television shows, advertisements, or movies. Table 10.1 provides a few specific examples of the kinds of data that content analysts have examined in prior studies. Which of these sources of data might be of interest to you?

Table 10.1 Content analysis examples

Data	Research question	Author(s) (year)
Spam e-mails	What is the form, content, and quantity of unsolicited e- mails?	Berzins (2009)
James Bond films	How are female characters portrayed in James Bond films, and what broader lessons can be drawn from these portrayals?	Neuendorf, Gore, Dalessandro, Janstova, and Snyder-Suhy (2010)
Console video games	How is male and female sexuality portrayed in the best-selling console video games?	Downs and Smith (2010)
Newspaper articles	How do newspapers cover closed-circuit television surveillance in Canada, and what are the implications of coverage for public opinion and policymaking?	Greenberg and Hier (2009)
Pro-eating disorder websites	What are the features of pro-eating disorder websites, and what are the messages to which users may be exposed?	Borzekowski, Schenk, Wilson, and Peebles (2010)

One thing you might notice about Table 10.1 is that the data sources represent primary sources. That is, they are the original documents written by people who observed the event or analyzed the data. Secondary sources, on the other hand, are sources that report on primary sources. Often, secondary sources are created by looking at primary sources and analyzing their contents.

Shulamit Reinharz (1992) offers a helpful way of distinguishing between these two types of sources in her methods text. She explains that while primary sources represent “the ‘raw’ materials of history,” secondary sources are “the ‘cooked’ analyses of those materials” (p. 155). The distinction between primary and secondary sources is important for many aspects of social science, but it is especially important to understand when conducting content analysis. While there are certainly instances of content analysis in which secondary sources are analyzed, it is more common for content analysts to analyze primary sources.

In those instances where secondary sources are analyzed, the researcher’s focus is usually on the process by which the original analyst or presenter of data reached his conclusions or on the choices that were made in terms of how and in what ways to present the data. For example, James Loewen (2007)  conducted a content analysis of high school history textbooks. His aim was not to learn about history, but to understand how students are taught American history in high school. The results of his inquiry uncovered that the books often glossed over issues of racism, leaving students with an incomplete understanding of the trans-Atlantic slave trade, the extermination of Indigenous peoples, and the civil rights movement.

Sometimes students new to research methods struggle to grasp the difference between a content analysis of secondary sources and a literature review. In a literature review, researchers analyze theoretical, practical, and empirical sources to try to understand what we know and what we don’t know about a particular topic. The sources used to conduct a scholarly review of the literature are typically peer-reviewed sources, written by trained scholars, published in some academic journal or press. These sources are culled in a literature review to arrive at some conclusion about our overall knowledge about a topic. Findings from sources are generally taken at face value.

Conversely, a content analysis of scholarly literature would raise questions not addressed in a literature review. A researcher who uses content analyst to examine scholarly articles would try to learn something about the authors (e.g., who publishes what and where), publication outlets (e.g., how well do different journals represent the diversity of the discipline), or topics (e.g., how has the popularity of topics shifted over time). A content analysis of scholarly articles would be a “study of the studies” as opposed to a “review of studies.” Perhaps, for example, a researcher wishes to know whether more men than women authors are published in the top-ranking journals in the discipline. The researcher could conduct a content analysis of different journals and count authors by gender (though this may be a tricky prospect if relying only on names to indicate gender). Or perhaps a researcher would like to learn whether or how various topics of investigation go in and out of style. She could investigate changes over time in topical coverage in various journals. In these latter two instances, the researcher is not aiming to summarize the content of the articles, as in a literature review, but instead is looking to learn something about how, why, or by whom particular articles came to be published.

Content analysis can be qualitative or quantitative, and often researchers will use both strategies to strengthen their investigations. In qualitative content analysis, the aim is to identify themes in the text being analyzed and to identify the underlying meaning of those themes. For example, Alyssa Goolsby (2007) conducted qualitative content analysis in her study of national identity in the United States. To understand how the boundaries of citizenship were constructed in the United States, she conducted a qualitative content analysis of key historical congressional debates focused on immigration law.

Quantitative content analysis, on the other hand, involves assigning numerical values to raw data so that it can be analyzed statistically. Jason Houle (2008) conducted a quantitative content analysis of song lyrics. Inspired by an article on the connections between fame, chronic self- consciousness (as measured by frequent use of first-person pronouns), and self-destructive behavior (Schaller, 1997),  Houle counted first-person pronouns in Elliott Smith song lyrics. Houle found that Smith’s use of self-referential pronouns increased steadily from the time of his first album release in 1994 until his suicide in 2003.  We’ll elaborate on how qualitative and quantitative researchers collect, code, and analyze unobtrusive data in the final portion of this section.

Indirect measures

Texts are not the only sort of data that researchers can collect unobtrusively. Unobtrusive researchers might also be interested in analyzing the evidence that humans leave behind that tells us something about who they are or what they do. This kind evidence includes the physical traces left by humans and the material artifacts that tell us something about their beliefs, values, or norms. Physical traces include such things as worn paths across campus, the materials in a landfill or in someone’s trash can, indentations in furniture, or empty shelves in the grocery store. Examples of material artifacts include video games and video game equipment, sculptures, mementos left on gravestones, housing structures, flyers for an event, or even kitchen utensils.

One challenge with analyzing physical traces and material artifacts is that you generally don’t have access to the people who left the traces or created the artifacts that you are analyzing. (And if you did find a way to contact them, then your research would no longer qualify as unobtrusive!) It can be especially tricky to analyze meanings of these materials if they come from some historical or cultural context other than your own. Situating the traces or artifacts you wish to analyze both in their original contexts and in your own is not always easy and can lead to problems during data analysis. How do you know that you are viewing an object or physical trace in the way that it was intended to be viewed? Do you have the necessary understanding or knowledge about the background of its original creators or users to understand where they were coming from when they created it?

Imagine an alien trying to understand some aspect of Western human culture simply by examining our artifacts. Cartoonist Mark Parisi (1989) demonstrates the misunderstanding that could ensue in his drawing featuring three very small aliens standing atop a toilet. One alien says, “Since water is the life-blood on this planet, this must be a temple of some sort…Let’s stick around and see how they show their respect” (1989). Without a contextual understanding of Western human culture, the aliens misidentified the purpose of the toilet, and they will be in for quite a surprise when someone shows up to use it!

The point is that while physical traces and material artifacts make excellent sources of data, analyzing their meaning takes more than simply trying to understand them from your own contextual position. This can be challenging, but the good news is that social workers have been trained in cultural humility, and they strive for cultural competence.  This means they recognize that their own cultural lenses may not provide accurate perspectives on situations.  Social work researchers using physical traces and material artifacts must be aware of who caused the physical trace or created the artifact, when they created it, why they created, and for whom they created it. Answering these questions requires accessing materials in addition to the traces or artifacts themselves, such as historical documents or, if analyzing a contemporary trace or artifact, perhaps using another method of data collection such as interviews with its creators.

One type of unobtrusive research allows you to skip the data collection phase altogether. To many, skipping the data collection phase is preferable since it allows the researcher to proceed directly to answering their question through data analysis. When researchers analyze data originally gathered by another person or entity, they engage in secondary data analysis. Researchers gain access to data collected by other researchers, government agencies, and other unique sources by making connections with individuals engaged in primary research or accessing their data via publicly available sources.

Imagine you wanted to study whether race or gender influenced what major people chose at your college. You could do your best to distribute a survey to a representative sample of students, but perhaps a better idea would be to ask your college registrar for this information. Your college already collects this information on all of its students. Wouldn’t it be better to simply ask for access to this information, rather than collecting it yourself? Maybe.

Challenges in secondary data analysis

Some of you may be thinking, “I never gave my college permission to share my information with other researchers.” Depending on the policies of your university, this may or may not be true. In any case, secondary data is usually anonymized or does not contain identifying information. In our example, students’ names, student ID numbers, home towns, and other identifying details would not be shared with a secondary researcher. Instead, just the information on the variables—race, gender, and major—would be shared. Techniques to make data anonymous are not foolproof, however, and this is a challenge to secondary data analysis.  Researchers have been able to identify individuals in “anonymized” data from credit card companies, Netflix, AOL, and online advertising companies have been able to be unmasked (Bode, 2017; de Montjoy, Radaelli, Singh, & Pentland, 2015).

Another challenge with secondary data stems from the lack of control over the data collection process. Perhaps your university made a mistake on their forms or entered data incorrectly. If this were your data, you could correct errors like this right away. With secondary data, you are less able to correct for any errors made by the original source during data collection. More importantly, you may not know these errors exist and reach erroneous conclusions as a result. Researchers using secondary data should evaluate the procedures used to collect the data wherever possible, and data that lacks documentation on procedures should be used with caution.

Secondary researchers, particularly those conducting quantitative research, must also ensure that their conceptualization and operationalization of variables matches that of the primary researchers. If your secondary analysis focuses on a variable that was not a major part of the original analysis, you may not have enough information about that variable to conduct a thorough analysis. For example, you want to study whether depression is associated with income for students and you found a dataset that included those variables. If depression was not a focus of the dataset, the original researchers may only have included a question like, “Have you ever been diagnosed with major depressive disorder?” While answers to this question will give you some information about depression, it will not give you the depth that a scale like Beck’s Depression Inventory or the Hamilton Rating Scale for Depression would or provide information about severity of symptoms like hospitalization or suicide attempts. Without this level of depth, your analysis may lack validity. Even when operationalization for your variables of interest is thorough, researchers may conceptualize variables differently than you do. Perhaps they were interested in whether a person was diagnosed with depression anytime in their life, whereas, you are concerned with their current symptoms of depression. For these reasons, understanding the original study thoroughly by reading the study documentation is a requirement for rigorous secondary data analysis.

The lack of control over the data collection process also hamstrings the research process itself. While some studies are created perfectly, most are refined through pilot testing and feedback before the full study is conducted (Engel & Schutt, 2016).  Secondary data analysis does not allow you to engage in this process. For qualitative researchers in particular, this is an important challenge. Qualitative research, particularly from the interpretivist paradigm, uses emergent processes in which research questions, conceptualization of terms, and measures develop and change over the course of the study. Secondary data analysis inhibits this process from taking place because the data are already collected. Because qualitative methods often involve analyzing the context in which data are collected, secondary researchers may have difficulty authentically and accurately representing the original data in a new analysis.

Another challenge for research using secondary data can be getting access to the data. Researchers seeking access to data collected by universities (or hospitals, health insurers, human service agencies, etc.) must have the support of the administration. It may be important for researchers to form a partnership with the agency or university whose data is included in the secondary data analysis. Administrators will trust people who they perceive as competent, reputable, and objective. They must trust you to engage in rigorous and conscientious research.  Some secondary data are available in repositories where the researcher can have somewhat automatic access if she is able to demonstrate her competence to complete the analysis, shares her data analysis plan, and receives ethical approval from an IRB. Administrators of data that are often accessed by researchers, such as Medicaid or Census data, may fall into this category.

Strengths of secondary data analysis

While the challenges associated with secondary data analysis are important, the strengths of secondary data analysis often outweigh these limitations. Most importantly, secondary data analysis is quicker and cheaper than a traditional study because the data are already collected. Once a researcher gains access to the data, it is simply a matter of analyzing it and writing up the results to complete the project. Data can take a long time to gather and be quite resource-intensive. So, avoiding this step is a significant strength of secondary data analysis. If the primary researchers had access to more resources, they may also be able to engage in data collection that is more rigorous than a secondary researcher could. In this way, outsourcing the data collection to someone with more resources may make your design stronger, not weaker. Finally, secondary researchers ask new questions that the primary researchers may not have considered. In this way, secondary data analysis deepens our understanding of existing data in the field. Table 10.3 summarizes the strengths and limitations of existing data.

Table 10.3 Strengths and limitations of existing data

Strengths	Limitations
Reduces the time needed to complete the project Cheaper to conduct, in many cases Primary researcher may have more resources to conduct a rigorous data collection than you Helps us deepen our understanding of data already in the literature Useful for historical research	Anonymous data may not be truly anonymous No control over data collection process Cannot refine questions, measures, or procedure based on feedback or pilot tests May operationalize or conceptualize concepts differently than primary researcher Missing qualitative context Barriers to access and conflicts of interest

Ultimately, you will have to weigh the strengths and limitations of using secondary data on your own. Engel and Schutt (2016, p. 327) propose six questions to ask before using secondary data:

1. What were the agency’s or researcher’s goals in collecting the data?
2. What data were collected, and what were they intended to measure?
3. When was the information collected?
4. What methods were used for data collection? Who was responsible for data collection, and what were their qualifications? Are they available to answer questions about the data?
5. How is the information organized (by date, individual, family, event, etc.)? Are there identifiers used to identify different types of data available?
6. What is known about the success of the data collection effort? How are missing data indicated and treated? What kind of documentation is available? How consistent are the data with data available from other sources?

Sources of secondary data

Many sources of quantitative data are publicly available. The General Social Survey (GSS), which was discussed in Chapter 7 , is one of the most commonly used sources of publicly available data among quantitative researchers. Data for the GSS have been collected regularly since 1972, thus offering social researchers the opportunity to investigate changes in Americans’ attitudes and beliefs over time. Questions on the GSS cover an extremely broad range of topics, from family life to political and religious beliefs to work experiences.

Other sources of quantitative data include Add Health, a study that was initiated in 1994 to learn about the lives and behaviors of adolescents in the United States, and the Wisconsin Longitudinal Study, a study that has, for over 40 years, surveyed a panel of 10,000 people who graduated from Wisconsin high schools in 1957. Quantitative researchers interested in studying social processes outside of the United States also have many options when it comes to publicly available data sets. Data from the British Household Panel Study, a longitudinal, representative survey of households in Britain, are freely available to those conducting academic research (private entities are charged for access to the data). The International Social Survey Programme merges the GSS with its counterparts in other countries around the globe. These represent just a few of the many sources of publicly available quantitative data.

Unfortunately for qualitative researchers, far fewer sources of free, publicly available qualitative data exist. This is slowly changing, however, as technical sophistication grows and it becomes easier to digitize and share qualitative data. Despite comparatively fewer sources than for quantitative data, there are still a number of data sources available to qualitative researchers whose interests or resources limit their ability to collect data on their own. The Murray Research Archive, housed at the Institute for Quantitative Social Science at Harvard University, offers case histories and qualitative interview data. The Global Feminisms project at the University of Michigan offers interview transcripts and videotaped oral histories focused on feminist activism; women’s movements; and academic women’s studies in China, India, Poland, and the United States. At the University of Connecticut, the Oral History Office provides links to a number of other oral history sites. Not all the links offer publicly available data, but many do. Finally, the Southern Historical Collection at University of North Carolina–Chapel Hill offers digital versions of many primary documents online such as journals, letters, correspondence, and other papers that document the history and culture of the American South.

Keep in mind that the resources mentioned here represent just a snapshot of the many sources of publicly available data that can be easily accessed via the web. Table 10.4 summarizes the data sources discussed in this section.

Table 10.4 Sources of publicly available data

Organizational home	Focus/topic	Data	Web address
National Opinion Research Center	General Social Survey; demographic, behavioral, attitudinal, and special interest questions; national sample	Quantitative	http://www.norc.uchicago.edu/GSS+Website/
Carolina Population Center	Add Health; longitudinal social, economic, psychological, and physical well-being of cohort in grades 7–12 in 1994	Quantitative	http://www.cpc.unc.edu/projects/addhealth
Center for Demography of Health and Aging	Wisconsin Longitudinal Study; life course study of cohorts who graduated from high school in 1957	Quantitative	https://www.ssc.wisc.edu/wlsresearch/
Institute for Social & Economic Research	British Household Panel Survey; longitudinal study of British lives and well- being	Quantitative	https://www.iser.essex.ac.uk/bhps
International Social Survey Programme	International data similar to GSS	Quantitative	http://www.issp.org/
The Institute for Quantitative Social Science at Harvard University	Large archive of written data, audio, and video focused on many topics	Quantitative and qualitative	http://dvn.iq.harvard.edu/dvn/dv/mra
Institute for Research on Women and Gender	Global Feminisms Project; interview transcripts and oral histories on feminism and women’s activism	Qualitative	http://www.umich.edu/~glblfem/index.html
Oral History Office	Descriptions and links to numerous oral history archives	Qualitative	http://www.oralhistory.uconn.edu/links.html
UNC Wilson Library	Digitized manuscript collection from the Southern Historical Collection	Qualitative	http://dc.lib.unc.edu/ead/archivalhome.php? CISOROOT=/ead

Spotlight on UTA school of social work

Secondary Data analysis

Dr. Kathy Lee of the University of Texas at Arlington’s School of Social Work is interested in mental health and quality of life among vulnerable and marginalized older adults and their family caregivers. Dr. Lee is particularly interested in social participation interventions and psychosocial intervention that promote their health and well-being outcomes. The majority of Dr. Lee’s work has been conducted with panel survey data from the Health and Retirement Study (HRS). Some advantages of using secondary data includes building evidence based on high quality data (i.e., nationally representative data that are easily accessible) and allowing researchers to understand social trends over time. Although secondary analysis requires time and efforts to be familiar with the dataset due to its complexity and breadth, researchers can answer a wide range of research questions, particularly with knowledge of survey statistics and methods.

HRS is the first and the largest longitudinal study, consisting of over 37,000 individuals age 50 and over in 23,000 households in the United States. The purpose of HRS is to inform researchers and policymakers of important issues of retirement and health of aging populations and to promote discussion to respond to the rapidly aging society. Since 1992, a variety of content and data have been included, such as physical measures, biomarkers, and psychosocial factors, making the study multi-disciplinary. The data are collected through multiple modes: face-to-face, telephone, and mail. The survey is conducted biannually with support from the National Institute on Aging and the Social Security Administration. Panel survey data from publicly available databases, like HRS, are very essential for researchers to better understand opportunities and challenges to aging.

Dr. Lee’s dissertation research (Lee, 2018) examined (1) the impact of volunteering on cognitive health among older adults with cognitive impairment, and (2) the complex relationships between volunteer behaviors, psychological well-being, and cognitive health. Using HRS data collected from 2004 to 2014, her study included older adults age 65 and older living with cognitive impairment based on the Telephone Interview for Cognitive Status (≤11 out of the total score of 27). With a focus on a description of change over time, Dr. Lee tested linear mixed effects models to examine growth or decline in cognitive health of older adults with cognitive impairment by volunteer and non-volunteer group. Dr. Lee also employed structural equation modeling to observe the snapshot of variables of interest – volunteer behaviors, psychological well-being, and cognitive health. The study data showed that (1) the level of cognitive functioning slightly increased over time only among those who volunteered, and (2) the relationship between psychological well-being and cognitive functioning was significantly greater than the relationship between volunteering and cognitive functioning, suggesting the importance of providing volunteer activities that can increase one’s psychological well-being.

Research involving secondary data can be an important contribution to improving the lives of social work clients. The value of Dr. Lee’s secondary data research was recognized by the Gerontological Society of America who awarded her the Junior Scholar Award for Research Related to Disadvantaged Older Adults and the Emerging Scholar and Professional Organization Poster Award in 2018.

Dr. Lee is currently working on multiple other secondary analyses to broaden knowledge around social participation and depression among vulnerable aging populations.