Chapter 10 Experimental Research Gay, Mills, and Airasian 10th Edition Educational Research Chapter 10 Experimental Research Gay, Mills, and Airasian 10th Edition

Topics Discussed in this Chapter Defining characteristics of experimental research Conducting experimental research Manipulation and control Threats to validity Internal validity External validity Group designs

Defining Characteristics Research designed to investigate cause and effect relationships through the direct manipulation of an independent variable and control of extraneous variables Independent variable – the variable being manipulated Dependent variable – the variable in which the effect of the manipulation of the independent variable is observed Researcher manipulation and control – choice of treatments, choice of a research design, use of specific procedures, etc.

Experimental Process Six steps to conducting experimental research Selection and definition of the problem Statement of a hypothesis indicating a causal relationship between variables Selection of participants and instruments Random selection of a sample of subjects from a larger population Random assignment of members of the sample to each group Selection of valid and reliable instruments

Experimental Process Six steps to conducting experimental research (cont.) Selection of a research plan Three types of comparisons Comparison of two different approaches Comparison of new and existing approaches Comparison of different amounts of a single approach Execution of the research plan Two concerns Sufficient exposure to the treatment They need to be substantively different treatments

Experimental Process Six steps to conducting experimental research (cont.) Analysis of data Formulation of conclusions

Manipulation and Control The researcher’s decisions related to what constitutes the independent variable Active and assigned variables Active variables are those the researcher actively manipulates Choice of an instructional strategy A particular counseling approach Assigned variables are those that cannot be manipulated by the researcher but are of interest Gender Race

Manipulation and Control The researcher’s efforts to remove the influence of any extraneous variables that might have an effect on the dependent variable The goal is to be assured the only differences between groups is that related to the independent variable Participant variables – characteristics of the subjects Pre-existing achievement levels Differences in attitudes Environmental variables – characteristics of the context Learning materials Differences in the time available for treatment between groups

Experimental Validity Internal validity – the degree to which the results are attributable to the independent variable and not some other rival explanation External validity – the extent to which the results of a study can be generalized Population validity – generalizations related to other groups of people Ecological validity – generalizations related to other settings, times, contexts, etc.

Experimental Validity Relative importance of internal and external validity Internal and external validity are related reciprocally Controlling internal validity decreases external validity Controlling external validity decreases internal validity First demonstrate an effect in a highly controlled environment (i.e., prioritize internal validity) Second replicate the study in a more realistic, natural setting (i.e., prioritize external validity)

Threats to Internal Validity History:an event occurs not related to IV Maturation: Ss change over time Testing: Exposure to pretest might improve scores on posttest Instrumentation: Reliability, Validity, and not using the same test Statistical regression: Regression to the mean Differential selection of participants: Groups might be different outside of IV Mortality: Ss drop out of the study Selection-maturation interaction, etc.: Groups grow at different rates not due to the IV

Threats to External Validity Pre-test treatment interaction: Taking the pretest impacts the treatment itself. Multiple treatment interference: More than one treatment/ experiment performed (old impacts new) Selection treatment interaction: Who is in your sample impacts the results. Specificity of variables: Not specific enough in the following areas to replicate the study or know if generalizable Participants Operational definition of the treatment Operational definition of the dependent variable Specific times Specific circumstances Treatment diffusion: Two groups talk to one another and share treatment information so that they are not in effect one group.

Threats to External Validity Experimenter effects: Something about the experimenter changes the outcome of the DV. Reactive arrangements: Something about the Ss changes the outcome of the DV. Some examples of this are: reaction to the environment, reaction to the attention from the researcher, placebo effect, and novelty effect.

Controlling for Extraneous Variables Extraneous variables must be controlled to be able to attribute the effect to the treatment Group equivalency must be assured Four major means to achieve control Randomization Selection – controls for representation Assignment – controls for group equivalency

Controlling for Extraneous Variables Matching Identifying pairs of subjects “matched” on specific characteristics of interest Randomly assigning subjects from each pair to different groups Difficulty with subjects for whom no match exists Comparing homogeneous groups Restricting subjects to those with similar characteristics Restricting subjects results in problems related to generalization

Controlling for Extraneous Variables Using subjects as their own controls Multiple treatments across time Problem with carry-over effects Analysis of covariance (ANCOVA) Statistically adjusting the posttest scores for the subjects in each group for pretest differences that existed at the beginning of the study Creates statistically equivalent groups

Controlling for Extraneous Variables Other ways to control extraneous variables Holding variables constant Using only males rather than males and females Selecting teachers with only similar levels of experience Selecting only one grade level Stipulating the specific length of a treatment

Group Designs Two major classes of group designs Single-variable designs – one independent variable Factorial designs – two or more independent variables Three types of experimental designs Pre-experimental designs Experimental designs Quasi-experimental designs

Pre-Experimental Designs Three types (X or X1=treatment, 0=test, X2= control) One-shot case study X O One-group pretest-posttest design O X O Static group comparison X1 O X2 O Threats to internal validity – see Table 10.1

True Experimental Designs Three types (r=random assignment, x=treatment, 0=test) Pretest-posttest control group design R O X O R O O Posttest only control group design R X O R O

True Experimental Designs Three types (cont.) Solomon four-group comparison R O X O R O O R X O R O Threats to internal validity – see Figure 10.1

Quasi-Experimental Designs Groups may be randomly assigned, not Ss Three types Non-equivalent control group design O X O O O Time series design O O O O X O O O O Counterbalanced design O X1 O X2 O X3 O O X3 O X1 O X2 O O X2 O X3 O X1 O Threats to internal validity – see Figure 10.2

Factorial Designs Two independent variables and one dependent variable The effect of teaching strategy and gender on students’ achievement The effect of a particular counseling technique and the clients’ ethnicity on the success of the treatment The effect of a specific coaching approach and children in three age groups on the ability to perform certain physical tasks

Factorial Designs Interaction The degree to which changes in the dependent variable are different depending on the levels of each of the independent variables A particular instructional strategy is more effective for males than females A particular counseling technique is more effective when the ethnicity of the counselor and client are similar

Factorial Designs Interaction Visually explained by a graph of performance of all levels of both independent variables Parallel lines indicate no interaction Non-parallel lines indicate an interaction See Figure 10.5 in your text

Factorial Designs Interaction Visual presentation of a significant interaction

Factorial Designs Interaction Visual presentation of a non-significant interaction