Let’s take a closer look at the components of an experiment. Conducting an experiment requires choosing at least one dependent variable, the behavior under study (e.g., physiological stress—heart rate and cortisol—and aggressive thoughts) and one independent variable, the factor proposed to change the behavior under study (e.g., type of video game). The independent variable is manipulated or varied systematically by the researcher during the experiment (e.g., a child plays with a violent or a nonviolent video game). The dependent variable is expected to change as a result of varying the independent variable, and how it changes is thought to depend on how the independent variable is manipulated (e.g., physiological stress and aggressive thoughts vary in response to the type of video game).
In an experiment, the independent variable is administered to one or more experimental groups, or test groups. The control group is treated just like the experimental group except that it is not exposed to the independent variable. For example, in an experiment investigating whether particular types of music influence mood, the experimental group would experience a change in music (e.g., from “easy listening” to rock), whereas the control group would hear only one type of music (e.g., “easy listening”). Random assignment, whereby each participant has an equal chance of being assigned to the experimental or control group, is essential for ensuring that the groups are as equal as possible in all preexisting characteristics (e.g., age, ethnicity, and gender). Random assignment makes it less likely that any observed differences in the outcomes of the experimental and control groups are due to preexisting differences between the groups. After the independent variable is manipulated, if the experimental and control groups differ on the dependent variable, it is concluded that the independent variable caused the change in the dependent variable. That is, a cause-and-effect relationship has been demonstrated.
As another example, consider a study designed to examine whether massage therapy improves outcomes in preterm infants (infants who were born well before their due date) (Abdallah, Badr, & Hawwari, 2013). Infants housed in a neonatal unit were assigned to a massage group (independent variable), who were touched and their arms and legs moved for 10-minute periods once each day, or to a control group, which received no massage. Other than the massage/no-massage periods, the two groups of infants were cared for in the same way. Infants who were massaged scored lower on the measure of infant pain and discomfort (including indicators such as heart rate, oxygen saturation, and facial responses) at discharge (dependent variable). The researchers concluded that massage therapy reduces pain responses in preterm infants.
Developmental scientists conduct studies that use both correlational and experimental research. Studying development, however, requires that scientists pay close attention to age and how people change over time, which requires the use of specialized research designs, as described in the following sections.
Developmental Research Designs
Does personality change over the lifespan? Do children outgrow shyness? Are infants’ bonds with their parents associated with their adult relationships? These challenging questions require that developmental scientists examine relationships among variables over time. The following sections discuss the designs that researchers use to learn about human development. As you learn about each design, consider how we might employ it to answer a question about development. For example, how does alcohol use among adolescents change from 6th grade through 12th grade?
Cross-Sectional Research Design
A cross-sectional research study compares groups of people of different ages at a single point in time. For example, to examine how alcohol use changes from 6th through 12th grade, a scientist might visit a school system in 2020 and administer a survey about alcohol use to students ages 12, 14, 16, and 18. By analyzing the survey, the scientist can describe age differences in alcohol use and identify how 12-year-olds differ from 18-year-olds. However, the results do not tell us whether the observed age differences in alcohol use reflect age-related or developmental change. In other words, we don’t know whether the 12-year-olds will show the same patterns of alcohol use as the current 18-year-olds when they are 18, six years from now.
Cross-sectional research permits age comparisons, but participants differ not only in age but also in cohort, limiting the conclusions researchers can draw about development. Recall that a cohort is a group of people of the same age who are exposed to similar historical events and cultural and societal influences. The 12-year-olds and the 18-year-olds are different ages, but they are also in different cohorts, so the two groups may differ in reported alcohol use because of development (age-related changes) or cohort (group-related changes). For example, perhaps the 12-year-olds received a new early prevention program at school that was not available to the 18-year-olds when they were 12. The difference in alcohol use between 12-year-olds and 18-year-olds might then be related to the prevention program, not to age. Cross-sectional research is an important source of information about age differences, but it cannot provide information about age change.
Longitudinal Research Design
A longitudinal research study follows the same group of participants over many points in time. Returning to the previous example, to examine how alcohol use changes from 12 to 18 years of age, a developmental scientist using longitudinal research might administer a survey on alcohol use to 12-year-olds and then follow up 2 years later when they are 14, again when they are 16, and finally when they are 18. If a researcher began this study in 2020, the last round of data collection would not occur until 2026.
Longitudinal research provides information about age change because it follows people over time, enabling scientists to describe how the 12-year-olds’ alcohol use changed as they progressed through adolescence. However, longitudinal research studies only one cohort, calling into question whether findings indicate developmental change or whether they are an artifact of the cohort under study. Was the group of 12-year-olds that the scientist chose to follow for 6 years somehow different from the cohorts or groups of students who came before or after? Because only one cohort is assessed, it is not possible to determine whether the observed changes are age-related changes or changes that are unique to the cohort examined.
Sequential Research Designs
A sequential research design combines the best features of cross-sectional and longitudinal research by assessing multiple cohorts over time, enabling scientists to make comparisons that disentangle the effects of cohort and age (see Table 1.7). Consider the alcohol use study once more. A sequential design would begin in 2020 with a survey to students ages 12, 14, 16, and 18. Two years later, in 2022, the initial sample is surveyed again; the 12-year-olds are now 14, the 14-year-olds are now 16, and the 16-year-olds are now 18. The 18-year-olds are now 20 and are not assessed because they have aged out of the study. Now a new group of 12-year-olds is surveyed. Two years later, in 2024, the participants are surveyed again, and so on.
A sequential design combines cross-sectional and longitudinal designs, permitting the researcher to study multiple cohorts over time.
Table 1.7
Source: Adapted from Kim & Böckenholt (2000) Psychological Methods, Vol 5(3), Sep 2000, 380-400.
Table 1.8