2.  Descriptive Methods ◦ Observation ◦ Survey Research  Experimental Methods ◦ Independent Groups Designs ◦ Repeated Measures Designs ◦ Complex Designs  Applied Research ◦ Single-Case Designs and Small-n Research ◦ Quasi-Experimental Designs and Program Evaluation

3.  OVERVIEW  WHY RESEARCHERS USE REPEATED MEASURES DESIGNS  THE ROLE OF PRACTICE EFFECTS IN REPEATED MEASURES DESIGNS ◦ Defining Practice Effects ◦ Balancing Practice Effects in the Complete Design ◦ Balancing Practice Effects in the Incomplete Design  DATA ANALYSIS OF REPEATED MEASURES DESIGNS ◦ Describing the Results ◦ Confirming What the Results Reveal  THE PROBLEM OF DIFFERENTIAL TRANSFER Repeated Measure Designs

4.  Repeated Measure Designs ◦ Within-subjects designs  Subjects are repeatedly tested  Each subject in all conditions  Subjects as their own controls  practice and fatigue effects ◦ Improvement with practice ◦ Worse with fatigue/reduced motivation  No elimination but balancing ◦ Averaged across the conditions

5.  Conduct experiment with few participants ◦ Special populations (individuals with brain injuries)  Conduct experiment more efficiently  Increase sensitivity ◦ Ability to detect the effect ◦ Minimize error variation  Study changes in behavior over time

6.  Same individuals in each condition: ◦ No confounding on individual differences variables  Practice effects: Change because of repeated testing (not because of the independent variable)  Practice effects = threat to internal validity ◦ If different conditions are presented in the same order to all participants  Two types of RMD (complete and incomplete) ◦ Differ in the ways to control for practice effects.

7.  Block randomization ◦ Random order of all condition on each presentation ◦ Blocks = No of administrations of each condition (Tr/Cd) ◦ Blocks Size = Number of conditions ◦ Balancing = Avg presentation of each condition shall be equal ◦ Avg Pc = Σ (cd number) / Blocks  ABBA counterbalancing ◦ Random sequence followed by opposite sequence ◦ Suitable for small number of conditions and trials ◦ Balancing = 2 trials  Non-linear practice effects or Anticipation effects ◦ Block randomization >> ABBA

8.  Balanced across subjects  Each condition in each ordinal position ◦ Orders = N! ◦ N=Conditions ◦ Participants = Any Multiple of all Possible Orders  <=4 conditions : Use all possible orders  Random Assignment  Methods for orders selection ◦ Latin Square and Random starting order with rotation ◦ Orders = Any Multiple of Conditions

9.  Latin Square ◦ each condition at each ordinal positions once ◦ each condition precedes and follows each other condition exactly once  Random starting order with rotation ◦ Begin with a random order ◦ Rotate sequence systematically

10.  Errors and outliers (data scanning)  A summary score (e.g., mean, median) ◦ Each participant (incomplete design) ◦ Each participant in each conditions (complete design)  Descriptive statistics ◦ Performance across all participants ◦ For each condition of IV

11.  Probability testing  Same as in random group design ◦ Null hypothesis testing ◦ Confidence interval  Error variation ◦ More sensitive ◦ Not cause of individual differences ◦ Difference in ways the conditions effect the results

12.  Persistence of effects of one condition  Influence performance in subsequent conditions  Common with instructional variables ◦ Threat to internal validity of RMD  Identification of DT ◦ Same variables in RMD and RGD  Use RGD