Correlational Research

                                        Experimental Research

                                                Independent Variables

                                                Dependent Variables

                                                Random Assignment to Conditions

                                        Experimental vs. Statistical Control

                                        Quasi-experiments

                                                Quasi-independent Variables

                                                No Random Assignment to Conditions

                                        Other Designs Relevant to Aging

                                                Cross sectional

                                                Longitudinal

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    The goal of this chapter is to give you a rudimentary knowledge of research methods. This chapter is not designed to be a definitive source on research methodology but instead explains many concepts and designs that you may encounter as you read the material for the course.

Correlational Research

    A correlation is a relationship between two variables. For example, we could look at the relationship between age and health and ask whether health improves or declines with age. We could also look at the relationship between age and cognitive decline and see if these two variables are related. Below is another example that shows a relationship between two variables:
 
 
 

    This example shows that there is a relationship between watching violence on TV and aggressive behavior. However, when we look at correlations we must be aware of three issues:

                    Correlations does not equal causation

    We cannot infer a causal relationship. We cannot say that watching violent TV programs causes people to be more aggressive. All we can say is that there is a relationship between these two variables.

                    Correlations are bi-directional

    The reason we cannot infer causation is that correlations are bi-directional (as represented by the two arrow heads between TV violence and aggression). It is not clear whether watching TV causes aggression or whether aggressive people simply like to watch violent programs.

                    Third-variable or Alternative Explanations

    As can be seen in the above diagram, a relationship between two variables does not rule out the possibility that other variables may be important in determining TV watching and aggression. Other variables such as family background, parenting styles, socio-economic-status, the person's personality, or peers may be affecting this relationship.
 

Experimental Research

    Research that uses an experimental design has an advantage over correlational designs in that an experimental design can control for or rule out third variable explanations. How this is done is by random assignment to conditions. Random assignment means that every participant in a study has an equal chance of being in any of the conditions in the study. For example, a researcher might be interested in how engaging in mental activities, such as puzzle solving, might improve memory. The researcher might want to compare a group that engages in puzzle solving (the experimental group) to a group that does not solve puzzles (the control group) and see if memory improves. The independent variable is the variable being manipulated by the experimenter (i.e., solving puzzles or not) and the dependent variable is the variable being measured (i.e., an increase in memory). How random assignment to conditions rules out third variable explanations is that everyone has an equal chance regardless of say, health or interest in puzzle solving (potential third variable explanations) to be in either condition. If we did not randomly assign participants to conditions we might get more people in the control condition in poorer health than those in the experimental condition. Then, if we find better memory for the experimental condition than the control condition we are not sure whether it was the prior activity (puzzle solving) or the participant's health that affected the outcome. Random assignment to conditions is considered to be the defining factor of an experiment. Without random assignment you cannot rule out third variable explanations and you do not have an experiment! You have something else, more than likely a correlational finding.
 

Experimental vs. Statistical Control

    Random assignment to conditions allows us to gain experimental control over our variables. However, there are instances where we cannot randomly assign people to conditions either because of ethical or logistical reasons. For example one cannot ethically assign someone to smoke cigarettes for 25 years to determine whether smoking causes cancer nor can we change someone's gender, age, or ethnicity. Gender, age, and ethnicity are considered quasi-independent variables (see below) that occur naturally. Controlling for these potential third-variable explanations is attained by using statistical, rather than experimental techiques. The third variable might present a potential confound, but statistical procedures (e.g., multiple regression analyses) are used to attempt to statistically eliminate this confound. The use of statistical control is also known as partialing, controlling for, residualizing, holding constant, and covarying. Thus, if we were interested in the relationship between puzzle solving and memory we could also control for the person's age. This means that once we statistically account for any effects of age on memory we can then see if puzzle solving in and of itself affects memory.
 

Quasi-experiments

    A large amount of research on aging uses quasi-experimental designs because they include quasi-independent variables such as age, personality variables (e.g., self-esteem, obsessive-compulsive), ethnicity, or gender to name a few. We cannot control participant's exposure to or randomly assign them to these variables. As a result, there can be no random assignment of participants to these particular conditions. The use of quasi-experiments requires then that we statistically control for third variable explanations.

The table below summarizes the distinctions between experimental and quasi-experimental designs.

Table 1

Experimental and Quasi-experimental Designs
 

Experiments	Quasi-experiments
Independent variables are manipulated by the experimenter	Uses quasi-independent variables
Random assignment to conditions	No random assignment to conditions
Gets at cause and effect	Correlational--Does not get at cause and effect because alternative or third variable explanations are not controlled for
Experimental Control	Statistical Control

 

Other Designs Relevant to Aging

Cross sectional Designs

    A cross sectional design is one that compares different groups of people. These comparisons could be based on age, gender, ethnicity, or any variable that would place people into different groups. A cross sectional design can also look at different cohorts (i.e., people roughly born at the same time) with the goal of comparing these cohorts on some dependent variable. Below are the results of two studies that use a cross sectional design:

Figure 1

The Relationship Between Participant Age and Intelligence

    This is a summary of a number of studies that looked at differences in intelligence as measured by IQ tests between younger and older adults. As can be seen, younger adults seem to perform better on IQ tests than do older adults.
 

Figure 2

A Comparison of Just World Beliefs Between Younger and Older Adults
 

    Both findings are intriguing but we must be careful in how we interpret these data. For example, do the findings mean that younger adults are smarter than older adults? Or do older adults believe that the world is a just place and younger adults do not? In order to interpret these findings, we must first be aware of some points related to cross sectional designs.

    One point is that cross sectional designs cannot tell us about changes as we get older. All cross sectional designs tell us is that there is a difference between the groups. We cannot infer that intelligence declines with age or that a belief in a just world increases with age using a cross sectional design. A second point is that other variables may be affecting the performance of the two groups. For example, younger adults have had more experience with the educational system than have older adults. Younger adults may have had more practice taking tests than have older adults. Thus, with cross sectional designs, alternative explanations for the findings cannot be ruled out (unless we statistically control for these alternatives). A third point, is that difference between the groups may reflect cohort differences. People born at the same time share similar experiences and possibly similar attitudes. Also, different cohorts are affected by historical events in different ways. An older person who was draft eligible during the Vietnam War may perceive politics, for instance differently than a younger person living at a time when military service is voluntary. Baby boomers would possibly differ from Generation X-ers on many values and beliefs. Our example of differences in just world beliefs may reflect cohort differences.
 
 

Longitudinal Designs

    The advantage of a longitudinal design over a cross sectional design is that it can determine if a particular variable actually changes with age. This design is not without its disadvantages, however. For example, we can not be sure whether a change is due to getting older or to important historical events at different points in time. These period effects can be quite subtle and complex. For example, if you sample older workers over time about their attitudes toward retirement one must also consider that changes in retirement laws may affect the participants responses. Period effects may be particularly evident if you assess the participants' attitude toward retirement before mandatory retirement was introduced, then during the time when mandatory retirement was law, and later when mandatory retirement laws were eliminated. Another example is if we wished to determine if belief in a just world changes as people get older. That is, do people as they get older, come to believe more strongly that the world is a just place and that everyone gets what they deserve? We can use a longitudinal design and follow people for a period of 60 or 70 years and periodically measure their just world beliefs. However, we must also consider any important historical events that may have occurred over that period of time that may influence belief in a just world.

    Other issues to consider in using longitudinal designs is that the instruments that are used may become outdated. Items on an anxiety scale used in the 1950s may have significantly changed in the 1990s or become outdated. Therefore, the instruments may not be consistent over time. Another issue is attrition. Participants in a study, particularly if it is ongoing for around 50 years or so, may do a number of things. They may get bored or sick and drop out of the study, or they may die. Attrition creates problems for generalizing the findings. The survivors, indeed, may be a different group from those that were originally in the study.
 

Table 2

Comparing Cross Sectional and Longitudinal Designs
 

Design	Definition	Examples	Issues
Cross Sectional	Two or more groups from a different cohort are studied	Grandparenting Styles in 50 vs. 70 year olds Intelligence between 22 and 85 year olds Difference in a belief in a just world in younger compared to older adults	Cohort Effects Can only talk about Age Differences Other variables may be affecting the outcome
Longitudinal	A group is studied at several points in time	Intelligence over a 60 year period	Gets at age changes But also: Period Effects Changes in instruments Attrition are problematic to this design

         Chasteen, A. L., & Madey, S. F. (2003). Belief in a just world and the perceived injustice of dying young or old. Omega: Journal of Death and Dying, 47, 313-326.

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