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Main Effect
Effect independent variable has on the dependent variable across all independent variables
Simple Main Effect
Analysis examines mean differences at each level of IV
Independent Groups Design
Participants can be apart of either control or treatment group, not both
Interaction
If there is an interaction between two IV, the effect of one IV depends on the particular level of other variable.
Factorial Designs
Designs with more than one independent variable
Manipulation Checks
Attempt to measure whether IV has the intended effect
Straightforward Manipulation
Written, verbal, visual instructions
One tailed Test
Has a predicted direction
Two tailed test
Has no predicted direction
Type 1 Error
Incorrect decision to reject the null hypothesis when it is true
Type II Error
Incorrect decision to accept the null hypothesis when it is false
ANOVA (Analysis of Variance)
Statistical procedure used to test the degree in which two or more groups vary or differ
Effect size
Extent to which 2 variables are associated
External Validity
Degree where results can be generalized
Internal Validity
Results of the experiment are because of IV, not confounding variables
Longitudinal Method
Same group @ different times
Sequential Method
Uses both longitudinal, and cross sectional
Cross sectional Method
People of different ages measured @ same point
F Test
Difference among three or more groups to evaluate results of factorial design
T test
Examine whether two groups are different
Research hypothesis
Variable under investigation are related to the population
Null hypothesis
Variable under investigation are not related to the population
Descriptive Statistics
Describe the sample (mean, std, correlation)
Inferential Statistics
Designed to determine whether results can be generalized to the population
Sampling Distribution
Based on assumption that null hypothesis is true
Interrupted Time Series Design
Examines DV over extended period of time both before and after IV is implemented
Non Equivalent Control Group Pretest-Posttest Design
Most effective Quasi experiment. Participant-measure-treatment-measure Participant-measure-no treatment-measure
Non Equivalent Control Group
Separate control group but participants in 2 conditions
Solomon Four Group Design
Solomon Four Group Design
Control Series Design
Improves interrupted time series by finding control group
Floor Effects
Task too difficult that hardly anyone can perform ell
Ceiling Effects
Tasks too easy that participants reach maximum performance
Sensitivity
Important in measuring human performance
Staged Manipulations
Necessary to create some psychological state in the participant or stimulate a situation that occurs in real life
Program Evaluation
Research on programs that are implemented to achieve positive effect
Repeated Measure Design
Same individuals participate in all conditions of the study
Participant Variables
Personal attribute such as gender, age, ethnic group
Problems with one group pretest posttest design
history maturation testing instrument decay regression toward the mean
One group pretest posttest design
used to obtain a comparison
One group posttest only design
lacks control group lack internal validity
Quasi Experimental Design
used when control features of experiment cannot be achieved No random assignment IV cannot be manipulated
Stages of Program Evaluation
Needs Assessment Program theory assessment Process Evaluation Outcome Evaluation Efficiency Assessment
Multiple Baseline Design
Effectiveness of treatment is demonstrated when behavior change occurs after manipulation is introduced
ABA design
Behavior is observed during baseline-observed during treatment-observed after experimental treatment is removed
ABAB design
Similar to ABA. Treatment is introduced a second time
Mixed Factorial Design
2 or more IV At least one repeated measure
Demand Characteristics
Any feature of an experiment that might inform a participant the purpose of the study

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