Methodologic Challenges of Research in Older People

Qualitative Methodologies

Qualitative research has its roots in anthropology and sociology and is an umbrella term for a heterogeneous group of methodologies with different theoretical underpinnings. They aim to gain an in-depth understanding of peoples' behavior by exploring their knowledge, values, attitudes, beliefs, and fears. This allows subjects to give richer answers to real-world issues and allows the researcher to explore the full complexity of human behaviors, thus providing detailed insights that might be missed by other methods. For example, it may illuminate the reasons behind patients', carers', and clinicians' decisions about management, and therefore inform future policy developments, or help characterize important issues such as abuse or risk management that may be difficult to quantify.

Qualitative studies are hypothesis-generating rather than hypothesis-testing studies, but results can identify specific issues that need to be tested using quantitative methods or can help explain the outcomes of experimental studies. Thus, the two methods can usefully complement each other, and increasing numbers of studies are using mixed methodologies (e.g., a study trying to understand the attitudes of older adults toward enrollment into cancer clinical trials).

Samples in qualitative research tend to be small and labor-intensive, with data collected usually by direct observation or active participation in the setting of interest, or by in-depth individual interviews (unstructured or semistructured), focus groups (guided group discussions), or examination of documents or other artifacts. Other methods used in qualitative research studies include diary methods, role playing and simulation, narrative analysis, and in-depth case studies. Although potential areas of interest may be identified beforehand, there is no predetermined set of questions, and subjects are encouraged to express their views and ideas at length. Rather than formal sample size calculations, numbers of participants may be decided by analyzing interviews alongside data collection, which is stopped when no new themes are emerging (saturation). Sampling tends to be purposive rather than comprehensive or random, deliberately aiming to reflect a specific range of experience and attitudes judged to be of likely relevance to the research question. The results are analyzed by exploring the content and identifying patterns or themes, often through an iterative process allowing meaning to emerge from the data, rather than by the deductive statistical approach of quantitative methods.

Critics of qualitative analysis are concerned that it is too influenced by the views and attitudes of the researchers when they are collecting and analyzing data, thus introducing unacceptable bias and problems with generalizability and reproducibility of findings. Qualitative research can be challenging with older people but, because it can be less intrusive than more structured quantitative methodologies, it may be especially suited to those who are frail. They may be unable or unwilling to take part in lengthy interviews because of communication deficits or fatigue, and several shorter interviews may be more practical. Focus groups may work best with just four or five older participants and need a skilled facilitator to ensure a high level of participant interaction. Extra effort is needed to ensure representative samples and support those who are less confident, easily fatigued, or have cognitive or physical deficits. Participant or nonparticipant observation may be especially useful in institutional settings, but time must be given to establish trust with the researcher if residents and staff are not to feel threatened. Assurances of confidentiality and commitment from management are essential. However, once trust has been established, attrition rates tend to be low, because participation tends not to be burdensome.

Ecologic Studies

Ecologic studies use available data to characterize samples and generate hypotheses, although evidence for causality is generally weak. Data may be aggregated, such as census data and records of disease incidence by hospital, or individual, such as hospital discharge summaries or death certificates. Because the data are already available, there are advantages of speed and economy and the impact of factors operating at a population level (e.g., improved access to education, banning smoking in public places) may be difficult to measure at an individual level. However, measures may not be comparable over time or place, quality is always outside the researcher's control, and the available data may be selective. Many official statistics that are broken down by age will lump all those older than 65 years together or will only report information on adults of working age. When older people are included, they often exclude those not living in the community and those with cognitive impairment. Nevertheless, temporal data, such as the effect of daily variations in air pollution or temperature on mortality of older adults, where individual confounding factors remain constant over time, can provide robust evidence suggesting a causal effect. Ecologic data is also of value in studying the effects of early life factors on later health or disease on “life course epidemiology.”

Cross-Sectional Studies

Cross-sectional studies record information over a short period of time and are suited to report prevalence and the relationship between variables and age or dependency. They are relatively fast and simple to conduct because each subject is examined only once, and several outcomes or diseases can be studied simultaneously. For example, data from the Health and Retirement Study of 11,000 adults aged 65 years or older (representing the 34.5 million older Americans) highlighted the important finding that common geriatric conditions (e.g., cognitive impairment, falls, incontinence) were similar in prevalence to common chronic diseases in older adults, such as heart disease and diabetes, and were strongly and independently associated with dependency in activities of daily living. However, cross-sectional studies give no information about incidence or causality and are of limited value when studying rare conditions or acute illness.

Data can be presented as the mean value for each age group, or age can be used as a continuous independent variable in a regression analysis, with the outcome of interest as the dependent variable. Associations can be confounded when the variable of interest affects the survival of subjects, with selective mortality leading to a survival bias. Misinterpretation can also arise from birth cohort effects, with associations and differences not arising due to age differences but due to the era in which people were born and brought up and to changes in exposure to environmental risk factors. Sometimes such differences from one generation to the next are of particular interest, and a time series design may then be appropriate, with sequential samples of a particular age group being studied every few years. The Cognitive Function and Ageing Studies (CFAS) 1 and 11, for example, were conducted 2 decades apart using the same diagnostic methods in the same older age group living in the same geographic areas and demonstrated a cohort effect in dementia prevalence, with later born populations having a lower risk than those born earlier in the past century.

Selection of subjects needs to ensure that they are well matched at each time point, and methodologies need to be identical, to ensure that differences are solely due to temporal changes and not to selection bias.