Unit 2: The Field of Child Psychology – Foundations of Child and Adolescent Psychology

Research Methods

Research Designs

The following is a comparison of research methods or techniques used to describe, explain, or evaluate. Each of these designs has strengths and weaknesses and is sometimes used in combination with other designs within a single study.

Observational studies involve watching and recording the actions of participants. This may take place in the natural setting, such as observing children at play at a park, or behind a one-way glass while children are at play in a laboratory playroom. The researcher may follow a checklist and record the frequency and duration of events (perhaps how many conflicts occur among 2 year olds). The researcher may be a participant or a non-participant. What would be the strengths of being a participant? What would be the weaknesses? Consider the strengths and weaknesses of not participating. In general, observational studies have the strength of allowing the researcher to see how people behave rather than relying on self-report. What people do and what they say they do are often very different. A major weakness of observational studies is that they do not allow the researcher to explain causal relationships. Yet, observational studies are useful and widely used when studying children. Children tend to change their behavior when they know they are being watched (known as theHawthorne effect ) and may not survey well.

Experiments are designed to test hypotheses (or specific statements about the relationship between variables ) in a controlled setting in efforts to explain how certain factors or events produce outcomes. A variable is anything that changes in value. Concepts are operationalized or transformed into variables in research, which means that the researcher must specify exactly what is going to be measured in the study. For example, if we are interested in studying marital satisfaction, we have to specify what marital satisfaction really means or what we are going to use as an indicator of marital satisfaction. What is something measurable that would indicate some level of marital satisfaction? Would it be the amount of time couples spend together each day? Or eye contact during a discussion about money? Or maybe a subject’s score on a marital satisfaction scale. Each of these is measurable but these may not be equally valid or accurate indicators of marital satisfaction. These are the kinds of considerations researchers must make when working through the design.

Three conditions must be met in order to establish cause and effect. Experimental designs are useful in meeting these conditions.

The independent and dependent variables must be related . In other words, when one is altered, the other changes in response. (The independent variable is something altered or introduced by the researcher. The dependent variable is the outcome or the factor affected by the introduction of the independent variable. For example, if we are looking at the impact of exercise on stress levels, the independent variable would be exercise; the dependent variable would be stress.)

The cause must come before the effect. Experiments involve measuring subjects on the dependent variable before exposing them to the independent variable (establishing a baseline). So we would measure the subjects’ level of stress before introducing exercise and then again after the exercise to see if there has been a change in stress levels. (Observational and survey research does not always allow us to look at the timing of these events which makes understanding causality problematic with these designs.)

The cause must be isolated . The researcher must ensure that no outside, perhaps unknown variables are actually causing the effect we see. The experimental design helps make this possible. In an experiment, we would make sure that our subjects’ diets were held constant throughout the exercise program. Otherwise, diet might really be creating the change in stress level rather than exercise.

A basic experimental design involves beginning with a sample (or subset of a population) and randomly assigning subjects to one of two groups: the experimental group or the control group . The experimental group is the group that is going to be exposed to an independent variable or condition the researcher is introducing as a potential cause of an event. The control group is going to be used for comparison and is going to have the same experience as the experimental group but will not be exposed to the independent variable. After exposing the experimental group to the independent variable, the two groups are measured again to see if a change has occurred. If so, we are in a better position to suggest that the independent variable caused the change in the dependent variable . The basic experimental model looks like this:

Sample Randomly Assigned to 1 of 2 Groups

Experimental Group

Measure Dependent Variable

Introduce Independent Variable

Measure Dependent Variable

Control Group

Measure Dependent Variable

Measure Dependent Variable

The major advantage of the experimental design is that of helping to establish cause and effect relationships. A disadvantage of this design is the difficulty of translating much of what concerns us about human behavior into a laboratory setting. Hopefully, this brief description of experimental design helps you appreciate both the difficulty and the rigor of conducting an experiment. (8)

Research Designs

Case studies involve exploring a single case or situation in great detail. Information may be gathered with the use of observation, interviews, testing, or other methods to uncover as much as possible about a person or situation. Case studies are helpful when investigating unusual situations such as brain trauma or children reared in isolation. And they often used by clinicians who conduct case studies as part of their normal practice when gathering information about a client or patient coming in for treatment.

Case studies can be used to explore areas about which little is known and can provide rich detail about situations or conditions. However, the findings from case studies cannot be generalized or applied to larger populations; this is because cases are not randomly selected and no control group is used for comparison.

Surveys are familiar to most people because they are so widely used. Surveys enhance accessibility to subjects because they can be conducted in person, over the phone, through the mail, or online. A survey involves asking a standard set of questions to a group of subjects. In a highly structured survey, subjects are forced to choose from a response set such as “strongly disagree, disagree, undecided, agree, strongly agree.” Sociologists, marketing researchers, political scientists, therapists, and others use surveys to gather information on many independent and dependent variables in a relatively short period of time. Surveys typically yield surface information on a wide variety of factors, but may not allow for in-depth understanding of human behavior. Surveys can be designed in a number of ways. They may include forced choicequestions and semi- structured questions in which the researcher allows the respondent to describe or give details about certain events.

One of the most difficult aspects of designing a good survey is wording questions in an unbiased way and asking the right questions so that respondents can give a clear response rather that choosing “undecided” each time. Knowing that 30% of respondents are undecided is of little use. So a lot of time and effort should be placed on the construction of survey items. One of the benefits of having forced choice items is that each response is coded so that the results can be quickly entered and analyzed using statistical software. Analysis takes much longer when respondents give lengthy responses that must be analyzed in a different way. Surveys are useful in examining stated values, attitudes, opinions, and reporting on practices. However, they are based on self- report or what people say they do rather than on observation and this can limit accuracy.

Secondary/Content Analysis

Secondary/Content analysis involves analyzing information that has already been collected or examining documents or media to uncover attitudes, practices, or preferences. There are a number of data sets available to those who wish to conduct this type of research. For example, the U. S. Census Data is available and widely used to look at trends and changes taking place in the United States. There are a number of other agencies, such as NORC (National Opinion Research Center) at the University of Chicago and The Henry J. Kaiser Family Foundation that collect data on family life, sexuality, and many other areas of interest in human development. The researcher conducting secondary analysis does not have to recruit subjects but does need to know the quality of the information collected in the original study.

Content Analysis

Content analysis involves looking at media such as old texts, pictures, commercials, lyrics or other materials to explore patterns or themes in culture. An example of content analysis is the classic history of childhood by Aries (1962) called “Centuries of Childhood”/ or the analysis of television commercials for sexual or violent content. Passages in text or programs that air can be randomly selected for analysis as well. Again, one advantage of analyzing work such as this is that the researcher does not have to go through the time and expense of finding respondents, but the researcher cannot know how accurately the media reflects the actions and sentiments of the population. (8)

Developmental Research Designs

Research is critical for any academic field. However, specific research methods must be adapted to the field of study. Because we are interested in how children develop across time, our research methods must allow us to assess change throughout development. We can do this through a cross-sectional design longitudinal design , or sequential design . These designs are referred to as developmental designs. (1)

Cross-Sectional Design

A cross-sectional design samples people of different ages to assess how individuals of different age groups differ on some variable of interest. For example, a researcher interested in the relationship between age and self-esteem may select groups of four-year-olds, eight-year-olds, twelve-year-olds, and sixteen-year-olds and then measure their self-esteem. That way, the researcher can have a snap shot of how self-esteem may change across childhood and adolescence. However, there are shortcomings to this approach. One significant issue is the lack of ability to know if the groups were initially different in self-esteem, regardless of their age. For example, maybe the researcher just happened to sample a group of four-year-olds with really healthy self-esteem and a group of sixteen-year-olds with really unhealthy self-esteem. If this happened, a researcher may make the mistake of thinking that self-esteem declines between the ages of four and sixteen, without realizing that the two groups were just innately different. The next approach tries to address this concern.

Longitudinal Design

A longitudinal design is another approach for developmental research. With this design, you start with a group of individuals all of the same age. Then, you follow them across time to see how they change on some dimension of interest. Paralleling the example above, a researcher using this approach would start with a group of four-year-olds and assess their self-esteem. The researcher would then check back in with the individuals at the ages of eight, twelve, and sixteen to see how their self-esteem has changed. This approach allows us to see how self-esteem changes within an individual during development. Unfortunately, there are once again shortcomings to this approach. One noteworthy issue with this approach is the danger of attrition. Attrition is a fancy word that researchers use for the loss of participants in a study. When you do a longitudinal study, you are asking for a significant time commitment from participants. For the example above, individuals would need to be available for follow-up assessments for 12 years. People lose interest, move, or, unfortunately, die. To try to address for the shortcomings of cross-sectional and longitudinal designs, researchers developed the next approach.

Sequential Approach

A sequential approach is a combination of both cross-sectional and longitudinal research. Like the longitudinal approach, you would be assessing individuals at four different times. However, at each time period, you add in a new group of people. This approach can be difficult to explain, so take a moment to look following information.

Time 1

Group 1 : 4 years

Time 2

Group 1 : 8 years

Group 2 : 4 years

Time 3

Group 1 : 12 years

Group 2 : 8 years

Group 3 : 4 years

Time 4

Group 1 : 16 years

Group 2 : 12 years

Group 3 : 8 years

Group 4 : 4 years

If you look at the columns for times two, three, and four, then you have a cross-sectional design. However, if you look at the rows, you have a longitudinal design. What is new, however, is the diagonal. Here we are able to see how the time period may be influencing self-esteem in a given age group. Maybe something changed in the educational system between time one and time four that really changed four-year-old children’s self-esteem. You couldn’t know that with just a cross-sectional or longitudinal design. (1)

When considering the best research design to use in their research, scientists think about their main research question and the best way to come up with an answer. A table of advantages and disadvantages for each of the research designs is provided here to help you as you consider what sorts of studies would be best conducted using each of these different approaches. (9)

Cross-Sectional

Advantages

  • Examines changes between participants of different ages at the same point in time
  • Provide information on age-related change

Disadvantages

  • Cannot examine change over time
  • Cannot examine cohort effects

Longitudinal

Advantages

  • Examines changes within individuals over time
  • Provides a developmental analysis

Disadvantages

  • Expensive
  • Takes a long time
  • Participant attrition
  • Possibility of practice effects
  • Cannot examine cohort effects

Sequential

Advantages

  • Examines changes within individuals over time
  • Examine changes between participants of different ages at the same point in time
  • Can be used to examine cohort effects

Disadvantages

  • May be expensive
  • Possibility of practice effects (9)