Between- Subjects Design Chapter 8

Review

Two types of Ex research Two basic research designs are used to obtain the groups of scores that are compared in an experiment: within-subjects design between-subjects design.

Within & Between designs Between Subjects Students Silence Music A 12   B 13 C 15 D 14 E F G H I J Within Subjects Students Silence Music A 12 15 B 13 14 C D E

Between subjects limitations

1- More subjects required To compare three different treatment conditions with 30 scores in each treatment, the between- subjects design requires 90 participants.

2-Group Difference Individual differences, may lead to group differences or assignment bias.

Example If the participants in one group are generally older ( or smarter, or taller, or faster, etc.) than the participants in the other group, then the experiment has a confounding variable.

3- Larger variance Increases variance which makes it hard to find significant differences (explained later)

Two types of confounding variables Confounding from individual differences, which is called assignment bias. Confounding from environmental variables. one group may be tested in a large room and another group in a smaller room.

Making equivalent groups Random Assignment ( Randomization) Matching Groups ( Matched Assignment) Holding Variables Constant or Restricting Range of Variability

1-Random Assignment It is relatively easy, and does not require any measurement or direct control of extraneous variables. However, random assignment is not perfect and cannot guarantee equivalent groups, especially when a small sample is used. Pure chance is not a dependable process for obtaining balanced equivalent groups.

2-Matching Groups School records are used to determine the IQs of the participants, and each student is classified as high IQ, medium IQ, or low IQ. The high- IQ participants are distributed equally between the two groups; half is assigned to one group and the other half is assigned to the second group using restricted random assignment. However, matching requires pre-testing to measure the variable( s) being controlled, It can become difficult to match several variables simultaneously.

3-Holding a variable constant For example, a researcher concerned about potential IQ differences between groups could restrict participants to those with IQs between 100 and 110. Holding a variable constant guarantees that the variable cannot confound the research, but this process limits the external validity of the research results.

INDIVIDUAL DIFFERENCES AND VARIABILITY High variability can obscure any treatment effects that may exist and therefore can undermine the likelihood of a successful study.

Restricted range 40.4 50

Wide Range 39.6 49.2

Other threats to internal validity of between-subjects designs Differential attrition (Mortality) (2 Dieting Programs) Diffusion of treatments (communication between groups) Compensatory equalization (computer lab) Compensation rivalry (John Henry) Resentful demoralization

STATISTICAL ANALYSES OF BETWEEN- SUBJECTS DESIGNS single- factor /two- group design or simply the two- group design a mean is computed for each group of participants, and then an independent- measures t-test is used to determine whether there is a significant difference between the means

Advantage It is easy to set up a two- group study, In addition, a two- group design provides the best opportunity to maximize the difference between the two treatment conditions; that is, you may select opposite extreme values for the independent variable.

Disadvantage of 2 groups The primary disadvantage of a two- group design is that it provides relatively little information. With only two groups, a researcher obtains only two real data points for comparison.

Comparing Means for More Than Two Groups a single- factor /multiple- group design may be used. For example, a re-searcher may want to compare driving performance under three telephone conditions: while talking on a cell phone, while texting on a cell phone, and without using a phone.

ANOVA For this study, the mean is computed for each group of participants, and a single- factor analysis of variance ( ANOVA for independent measures). When the ANOVA concludes that significant differences exist, some form of post hoc test or posttest is used to determine exactly which groups are significantly different from each other.

Advantage of ANOVA In addition to revealing the full functional relationship between variables, a multiple- group design also provides stronger evidence for a real cause- and- effect relationship than can be obtained from a two- group design.

Nominal and ordinal variables Because you cannot compute means for these variables, you cannot use an independent- measures t test or an ANOVA ( F test) to compare means between groups. However, it is possible to compare proportions between groups using a chi- square test for independence

Example Math test Teaching methods Passed failed Traditional 5 6 Group Work Computer Based 4 1