Qualitative Research/Quantitative Research
In gerontology, qualitative research refers to a variety of approaches to studying the everyday lives and social settings of mature adults. In studying the aging process or phenomena that help us to gain a deeper understanding of aging, qualitative research examines words, narratives, and experiences. It is exploratory by design and focused on gaining a richer understanding of motivations, opinions, perspectives, and underlying reasons. Qualitative gerontologists employ a range of different methods including in-depth interviews, document analysis, participant observation, case studies, and focus groups.
Quantitative research analyzes the relationships between and among variables using numerical measurement (e.g., quantity, frequency). Quantitative data in social gerontology often come from surveys or questionnaires. Responses are then summarized into numerical values that are subsequently used in statistical analysis. Quantitative methods include statistical methods, econometric techniques, and mathematical models to analyze data. Some examples of quantitative techniques include ordinary least-squares regression, generalized linear models (e.g., logistic regression), structural equation modeling, and multilevel modeling.
Researchers decide whether to employ qualitative or quantitative methods based on the questions they seek to address and the theoretical perspectives that inform their research questions. Qualitative research tends to address questions regarding “how” or “why” a phenomenon is experienced, as opposed to addressing questions regarding “how many,” “what is the difference between or relationship among various factors,” and “what is the impact or effect of x on y,” as is commonly the case in quantitative research. Examples of qualitative methods often applied in gerontology include interviews, focus groups, participant observation, and document analysis. Quantitative methods can include survey data analysis, experimental analysis involving econometric modeling, and multiple regression techniques applied to survey data or experimental data.
The main types of quantitative research designs include descriptive, correlational, quasi-experimental, and experimental. A descriptive design tends to involve the collection of observational or administrative data with the goal of describing the current status of a variable or phenomenon. A correlational design applies statistical analyses to examine the relationship between different variables without commenting on causation. A quasi-experimental design (also referred to as causal-comparative design) attempts to establish a causal relationship between groups, but because the researcher does not assign groups or manipulate the outcome variable, it can also be considered a form of natural experiment. In experimental designs, participants are randomly assigned to either a treatment or control group and econometric modeling is applied to the data to produce a causal relationship among variables.
Decisions about which type of research method a gerontologist applies ultimately stem from their methodological perspective or the worldview and lens through which a researcher applies to the research question being examined (van den Hoonaard 2012). The methodological approach one takes includes a set of commonly held beliefs about how research questions ought to be understood and addressed. Three of the most common research paradigms include positivism, constructivism, and pragmatism (Creswell 2017). Positivism suggests that there is a single reality, which can be measured, and quantitative methods are most typically used to measure this reality. Constructivism suggests that there is no single reality, that reality must be interpreted, and that qualitative methods are typically employed under this paradigm. Pragmatism suggests that reality is constantly being interpreted and debated; thus, the best method to use is the one that solves the problem; therefore qualitative, quantitative, or mixed methods may be used among pragmatist researchers.
Qualitative research follows from a constructivist or interpretivist perspective. It allows the people who are being studied to define what is central and important in their experience.
By design, qualitative research is emergent and tends to follow an inductive approach whereby researchers begin with the social world and then develop a theory that is consistent with the evidence that they observe (Savin-Baden and Major 2013; Maxwell 2013). In contrast, quantitative research applies deductive reasoning so that a conclusion is reached reductively. Quantitative research stems from the positivist perspective, which posits three key principals: adherence to a realist perspective, trust in causal knowledge, and reliance on deductive reasoning. In other words, quantitative researchers assume there is a singular truth about social life and aging or that there is a “reality” to be discovered, believe that interactions involve objective causes and effects, and employ a form of reasoning that applies a process of inference to derive conclusions from general premises. Quantitative research allows us to describe the extent to which a phenomenon is experienced so that we can understand population age trends, how many people are affected by a particular disease, or the impact of a policy change on certain groups of people.
Qualitative and quantitative methods each have their strengths and limitations. Qualitative research methods are well suited to capture the complexity of social interactions and behaviors. These methods often probe how individuals understand and give meaning to their lives by using their own words to share their beliefs, perceptions, and ideas. Qualitative research gives voice to the subjective experiences of individuals. Qualitative research is often rich and detailed and is particularly suitable for sensitive topics that require the researcher to build trust and rapport with their respondents over time (Patton 1990). However, qualitative research is often critiqued for having the “small sample size problem.” Yet, unlike quantitative research, qualitative research does not aim to be generalizable to the larger population. Qualitative research may also be prone to the unintentional subjectivity or bias of the researcher (e.g., choosing to ask questions in a way that will elicit a response that supports their hypotheses). To enhance the trustworthiness or validity of qualitative methods, researchers often aim to establish credibility, transferability, or dependability and confirmability (Guba and Lincoln 1985).
One strength of quantitative research is that a great deal of information can be gathered on a wide range of issues. Data can be quantified, standardized, and measured. An additional strength of quantitative data is that researchers can take data from a sample and attempt to generalize the results to an underlying general population. Quantitative studies ought to be replicable by other researchers, which helps strengthen the case for certain research findings. Quantitative research is not without its limitations, however. It is often composed of very structured survey questions that may provide respondents limited response choices (e.g., Likert scale items ranging from “strongly agree” to “strongly disagree”). As a result, the “rich” description of individuals’ subjective experiences or perceptions of their social world are not captured in quantitative research. Responses to survey questions are often forced into “tick boxes” and may not be suitable for sensitive topics. To enhance the trustworthiness or validity of quantitative methods, researchers often aim to establish internal validity, external validity or generalizability, reliability, and objectivity.
While the goals of quantitative research include prediction, generalizability, and causality, qualitative research aims to understand participants’ perceptions and the context surrounding certain phenomena. In qualitative research prediction is a less commonly applied term. However, when applied, prediction tends to be based on subjective judgment and to refer to the perspectives of experts or key informants, for example, among qualitative researchers applying the Delphi method. In quantitative research, prediction is generally obtained by applying statistical methods to cross-sectional, longitudinal, or time series data. Forecasting is a more specific form of prediction whereby estimates are produced based on past or present quantitative data. Examples include Poisson process model-based forecasting and exponential smoothing models.
Key Research Findings
There are many applications of qualitative research studies within gerontology. Three of the most commonly applied qualitative approaches include ethnography, phenomenology, and grounded theory. Ethnography originated in anthropology with the aim of describing a culture and the meaning of human behaviors within a specific cultural context. Phenomenology aims to identify and describe something as it is experienced by affected participants. Grounded theory aims to inductively develop a theory that is based on the researcher’s data collection and analyses.
Qualitative research allows scholars studying various aspects of aging to learn about the experiences of people experiencing everyday phenomena, as well as the voices of marginalized people, and experiences that are specific to older adulthood. As such, the sampling techniques applied in qualitative research typically involve purposeful sampling rather than random sampling techniques. There are a range of purposive sampling techniques including maximum variation, information-rich cases, extreme or deviant cases, confirming cases, or politically important cases. Network sampling and snowball sampling are also important ways that participants are identified through their connections or other participants. The exploratory nature of qualitative research means that researchers often identify the characteristics of relevant participants upfront but do not prespecify their study population (Morse 2015).
Qualitative research has come to be recognized as particularly relevant to researchers seeking to understand complex problems, particularly within the context of utilizing healthcare services or understanding complex communication issues within healthcare systems (Cobb and Forbes 2002). Through repeated interactions with participants via in-depth interviews or participant observation and document review, qualitative researches can evaluate social programs targeted for older adults, procedures for caring for aging patients within hospitals, and the efficacy of healthcare policies that can eventually inform public policy.
Within social gerontology, qualitative research has shed light and generated important insights on a range of other subject matters including grandparenthood, intergenerational relationships, widowhood, retirement, dementia, navigating healthcare systems, active aging, independence, caregiving, technology use, and sexual relationships. Narrative gerontology is qualitative approach that encourages researchers to listen to people’s stories and to engage with participants in ways that permit them to share substantive issues that affect the lives of mature adults (de Medeiros 2013). Examples of applications of narrative gerontology include the study of retirement and how individuals whose personal and work identities are intertwined may struggle with the transition (Silver 2018). Ethnogerontology is a field of study that has emerged within gerontology to study social inequality in aging and the racial, ethnic, and cultural processes of aging that has generated numerous applications for qualitative research. Increasingly, organizations and academic institutions are creating qualitative data repositories, such as the Maxwell School at Syracuse University, the University of Michigan, and QualiBank from the UK Data Service.
There are several opportunities for innovative and rigorous quantitative research on aging. This is driven, in large part, by the proliferation of several large, nationally representative data sets that follow participants over time. Access to these data sets provides an unprecedented opportunity for researchers. These data sets typically include a wide array of measures and large samples of data that are difficult and expensive for any one researcher or research team to obtain. Though not an exhaustive list, we describe some of the main data sets in social gerontological research here that feature data collected at three or more time points (usually referred to as waves). All of these data sets are publicly accessible, which means they can be accessed by researchers that are not part of the study’s original research team.
The most widely used survey for quantitative aging research is the Health and Retirement Study (HRS). The goal of this study is to track individuals age 51 years old and over until later stages or death in the United States. The HRS includes data on a comprehensive list of topics, including current employment and employment history, retirement, disability, occupation, industry, job and work environment characteristics, earnings and other income, pensions, other retirement plans, housing assets, estate planning, subjective probabilities, demographic characteristics, health conditions and health status, health insurance, life insurance, and internet usage in later life. The HRS is particularly known as an excellent data source for studying patterns of aging and retirement over time (see Shultz and Wang 2011).
The Wisconsin Longitudinal Study (WLS) is another well-known data set in quantitative research in social gerontology. In this study, participants were recruited in 1957 from the Wisconsin area of the United States during their senior year of high school. Data continue to be collected on these individuals. In 2011, the study participants were approximately 72 years old. Since this study has followed people over the course of 60 years, it is an excellent data source for studying life-course processes, especially the links between early-life conditions and later life outcomes. Though it has several uses, the WLS has been used to examine how childhood socioeconomic status affects later life cognition (Greenfield and Moorman 2018). In 2016, the WLS also included genetic data from respondents, which can be merged with social and health data of respondents and provides insight into how genetic factors may shape the aging process and influence outcomes in later life.
The Americans’ Changing Lives (ACL) Survey is another nationally representative, longitudinal data set ripe for application in social gerontology. This study was launched in 1986 and recruited a sample of adults aged 25 and older. The latest wave of data was collected in 2011. Like the WLS, the ACL is also particularly well-suited for studying how earlier periods of the life course shape later life. For instance, using this data, House et al. (2005) have examined whether socioeconomic disparities over the life course predict mortality in later life.
Nationally representative, longitudinal panel data also come from the National Social Life, Health, and Aging Project (NSHAP), collected and housed at the National Opinion and Research Council (NORC) at the University of Chicago. The NSHAP features three waves, beginning in 2005–2006 with 5-year gaps between waves. While the NSHAP continues several measures that overlap with the HRS, WLS, and ACL, the NSHAP is widely known for its measures on the sexual health of older adults (Lindau and Gavrilova 2010; Waite et al. 2009). Another unique feature of the NSHAP is the collection of what is known as “biomarker data” (e.g., blood samples) to obtain objective health information, such as measures of chronic inflammation through C-reactive protein (CRP) and interleukin 6 (IL-6) (Nowakowski 2014; Williams and McDade 2009).
While the United States has produced several longitudinal studies that are of great utility in social gerontology, there are also several comparably large and representative data sets in Asia, such as the Korean Longitudinal Study of Ageing (KLoSA) (see this volume), and other regions of the world, and the English Longitudinal Study of Aging (ELSA) (see this volume) and the Survey of Health, Ageing, and Retirement in Europe (SHARE) (in this volume). The ELSA began in 2002 and conducts interviews biannually. It currently has seven waves of data available (see Stafford et al. 2011; Xavier et al. 2014). SHARE surveys respondents from 11 European countries and also interviews participants biannually, since it was launched in 2004. It has five waves completed to date. By Wave 5, 15 countries had participated (see Verropoulou and Tsimbos 2017). Both ELSA and SHARE are modelled after the Health and Retirement Study design in the United States.
There are several common statistical methods used in social gerontological research to analyze quantitative data. We briefly describe a few here. Ordinary least squares (OLS) regression models are undoubtedly the most heavily employed tool for understanding the relationship between variables. OLS offers a way of estimating conditional change, such as “how much does change in one measure associate with a change in a related measure” (e.g., a 10-year increase in age is associated with a one-unit decrease in self-rated health). While useful for its flexibility and simplicity in interpretation, OLS is not a suitable method for uncovering causal effects. In addition, OLS models cannot capture additional complexities of the aging process, such as historical or political contexts and the Great Recession. To address the latter concern, fixed effects models are often employed. These are an extension of OLS regression and have much utility in social gerontology research. These methods can be used when data are comprised of repeated observations from a given unit, which happens when we are estimating effects over a longer period of time (e.g., as researchers do using many of the longitudinal data sets described above). The advantages of fixed effects models are that these provide more accurate coefficients by estimating within-person unit variation. Essentially, fixed effects models allow the researcher to control for all time-invariant characteristics of the individual that could influence the outcome (e.g., prior life history, race, gender), which are essential in many of the questions of interest to social gerontologists.
Structural equation modeling (SEM) is another important analytic tool for the analysis of longitudinal data in social gerontology. One of its unique characteristics includes the option to estimate reciprocal relationships between variables over time (e.g., the relationship between social relationships ➔ health and health ➔ social relationships). Structural equation modeling, briefly, also allows researchers to correct for the unreliability of measurement in, for instance, a scale of depression. This is done by incorporating a latent variable (i.e., unobserved variable) encapsulating several symptoms of depression to create an underlying construct. This is beneficial to adjust for measurement error in each item of a scale. Finally, structural equation models allow for relatively easy estimation of direct and indirect or “mediating” effects of explanatory variables on the outcome of interest. Growth curve models are a statistical technique used within the SEM framework that is commonly used in social gerontology and life course research. These models are useful when there is a focus on how outcomes change over time. Growth curve models allow the researcher to fit trajectories that are unique to every individual under consideration based on the set of observed repeated measures (e.g., health at ages 50, 60, 70, etc.). This technique generates a compilation of individual-specific trajectories that become the unit of analysis. In turn, this allows the researcher to estimate what the “average” trajectory is and to ascertain how individual trajectories differ from each other. In addition, growth curve models allow the researcher to predict the differences in trajectories as a function of individual characteristics (e.g., race, gender).
Multilevel modeling is also frequently used in social gerontology to examine the effects of individual-level and aggregate or contextual variables on outcomes of interest. These models are especially useful in studies examining the effects of environmental characteristics (e.g., neighborhoods) on outcomes of interest. Multilevel research has become increasingly popular in social gerontology because several large, nationally representative samples link respondents to their census tracts through geographical codes. In so doing, researchers have access to a number of different neighborhood-level variables, such as the percent within an older adult’s census tract that live below the poverty line or the race and age composition of a neighborhood (e.g., see Moorman et al. 2016). Access to data of this sort has broadened the range of research questions that social gerontologists can address. In some studies, the research question focuses on whether environmental characteristics have effects on the dependent variable above and beyond individual-level characteristics (e.g., Cagney et al. 2014).
Future Directions of Research
There are several promising directions for future research in social gerontology in qualitative (Phoenix 2018) and quantitative research. Recognizing the importance of social connectedness in later life, an increasing number of scholars have become particularly interested in the structural properties of social networks and the role of aging in shaping them. Over the past few years, several large, publicly available data sets have been developed to facilitate the measurement and analysis of key features of older adults’ social networks. These include, among others, the National Social Life, Health, and Aging Project (NSHAP) and the Survey of Health, Ageing, and Retirement in Europe (SHARE). This provides an opportunity to assess how the close social connections of older adults change over time and how this may be linked to access to social support, health, and other indicators of well-being (see Cornwell and Laumann 2015; Upenieks et al. 2018).
As a second direction of future research, the last several decades have witnessed a proliferation of life course research (Mayer 2009). The life course framework is commonly employed to understand how socioeconomic inequalities generate disparities in health (Ben-Shlomo and Kuh 2002; Lynch and Smith 2005). Social gerontology has been at the forefront of research on the life course perspective. But there are still several questions that could be answered by the continued integration of social gerontology and the life course perspective. For instance, do early conditions of childhood, adolescence, and early adulthood directly impact the middle and later parts of the life course, including in the retirement years and old age, net of intervening mechanisms such as educational attainment, work and career trajectories, and family formation? Coming to a deeper understanding of the link between social gerontology and the life course perspective is important because of its potential to illuminate mechanisms that produce age-specific variation of health, well-being, and social connectedness across key predictors such as social class, race, gender, education, income, and other variables of interest.
Another promising avenue for future research involves the integration of qualitative and quantitative methods. Mixed methods research involves the collection, analysis, and integration of qualitative and quantitative data in a way that either merges the data, has one method build off of the other, embeds one within the other, or is developed around a mixed methodological or theoretical orientation that underlies all aspects of the study.
The use of mixed methods research is growing in popularity in social gerontology. For example, this approach was utilized by Barg et al. 2006 in a study that examined the loneliness and depression levels of older adults through the use of a quantitative survey and asked older adult to describe what loneliness and depression is. In another example, Elliot and colleagues (2014) measured the association between neighborhood cohesion and well-being both qualitatively and quantitatively. These authors found, for instance, that while tighter-knit neighborhoods were associated with greater well-being, the qualitative component of the survey allowed the researchers to propose some mechanisms as to why this might be the case. This part of the analysis revealed that while good relationships with neighbors are salubrious for health, conflict-ridden relationships with those in one’s neighborhood were found to be extremely stressful for older adults.
When might a mixed methods approach be a viable option in social gerontology? Clarke (2009) argues that an analysis might be particularly conducive to a mixed methods approach when the methods include elements that can be readily integrated (e.g., a scaled survey quantitative questionnaire and a structured qualitative interview) or when the methods examine different aspects of the same phenomenon. For instance, qualitative research can often be used to uncover mechanisms that underlie a quantitative finding, as in the Elliot et al. (2014) study mentioned above. Quantitative researchers often have to speculate about the mechanisms that may be at play, as they are limited by the variables that have been observed in the particular data. Qualitative research can help identify respondent’s perceptions and can enrich the quantitative results by providing an individual’s own perspective on what might be influencing the dependent variable.
Despite its benefits, there are also several challenges facing mixed methods research in social gerontology. First, there may be increased costs to collecting both types of data (e.g., to survey a large sample of individuals through a battery of questions and conduct interviews with a large number of them). Second, mixed methods are logistically complex in nature. Such methods often require extensive methodological training and interdisciplinary collaboration among teams of researchers. Perhaps a bigger challenge facing mixed methods research is making sure that the quantitative and qualitative findings are integrated. Conclusions drawn from each of the two strains need to be integrated to provide a fuller understanding of the research question under consideration. In other words, the contribution of a mixed methods study has to encompass more than a mere statement that two different methodologies were combined. Theoretical insight needs to be gleaned from the integration of both methods that speak to the initial question or phenomenon under study.
Qualitative research is an umbrella term used to describe a range of research methodologies that seek to build an understanding of phenomena. As such, qualitative research examines behaviors, words, or images and draws from data collection techniques, such as interviews, observations, or focus groups. Qualitative research often applies inductive analytic techniques, whereby the researcher builds hypotheses or theories from the data. In contrast, quantitative research seeks explanation or causation by examining numbers with the goal of being precise and objective. Quantitative research uses data collection tools like surveys or questionnaires and often applies deductive analytic techniques or statistical procedures to measure, describe, or convey the relationship between different factors. One of the key distinctions between qualitative and quantitative research lies in the different goals each strive to achieve. Whereas the goal of quantitative research can include prediction, generalizability, or even causality, the goal of qualitative research is to learn the ways that participants perceive specific phenomena and to understand participants’ perspectives within their social context. Though they are distinct, the two research methods can be used together in mixed methods research when it is appropriate to address research questions that can only be answered by generating, analyzing, and integrating quantitative and qualitative approaches.
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