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Complex Design
Two or more IVs manipulated simultaneously in a single experiment
Inferential Statistics
Used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples
Null Hypothesis
The population means are equal - the observed difference is due to random error
(IV had no effect)
Research Hypothesis
The population means are not equal
(IV had an effect)
Statistical Significance
A significant result is one that has a very low probability of occurring if the population means are equal
Indicates that there is a low probability that the difference between the obtained sample means was sue to random error
Probability
The likelihood of the occurrence of some event or outcome
Alpha Level
The probability required for significance
Sampling Distribution
Probability of obtaining possible outcomes (usually a table)
t test
Used to examine whether two groups are significantly different from each other
t = group difference/within-group variability
s
standard deviation
s^2
variance
Degrees of Freedom (df)
Used to determine critical value
n1 + n2 - 2
F test / Analysis of variance
Used to ask whether there is a difference among three or more groups, or to evaluate the results of factorial designs (2+ IVs)
Systematic Variance (Between Group Variance)
The deviation of the group means from the grand mean (mean score of all individuals in all groups)
Error Variance (Within Group Variance)
The deviation of the individual scores in each group from their respective group means
Confidence Interval
Most likely range of actual population values
Type I Error
Made when we reject the null hypothesis but the null hypothesis is actually true (alpha)
Type II Error
Made when we accept the null hypothesis although in the population the research hypothesis is true (beta)
Power
1 - beta (Type II Error)
Pearson r correlation
Used to describe the strength of the relationship between two variables when both variables have interval or ratio scale properties
Use Chi-square when:
IV is nominal and DV is nominal
Use t test when:
IV is nominal (2 groups) and DV is interval/ratio
Use one-way analysis of variance when:
IV is nominal (3 groups) and DV is interval/ratio
Use Pearson correlation when:
IV is interval/ratio and DV is interval/ratio
Use Analysis of variance (factorial design/F test) when:
IV is nominal (2+ variables) and DV is interval/ratio
Use Multiple regression when:
IV is interval/ratio (2+ variables) and DV is interval/ratio
p-value
Probability of obtaining that value of the statistic or a more extreme value of the statistic if the null hypothesis is true
p < .05 = significant
p > .05 = not significant
Effect Size
r = .15, small effect size
r = .30, medium effect size
r = .40, large effect size
Power Analysis
Given the effect size and level of significance, determine N needed to detect effect
Single-case experimental design (single-subject design)
Uses one subject. Seen in applied behavior analysis
Baseline
Control period where subject's behavior is measured
Reversal Design (ABA Design)
A (baseline period) -> B (treatment period) -> A (baseline period)
Multiple Baseline Design
Effectiveness of treatment is demonstrated when a behavior changes only after the manipulation is introduced. Multiple circumstances.
Across Subjects Multiple Baseline Design
Behavior of several subjects is measured over time. The manipulation was introduced at a different point in time.
Across Behaviors Multiple Baseline Design
Several different behaviors in a single subject are measured over time.
Across Situations Multiple Baseline Design
Same behavior is measured in different settings
Quasi-experimental design
Allows us to examine the impact of an independent variable on ta dependent variable, but causal inference is much more difficult because of the lack of random assignment and others.
One-Group Posttest-Only Design
Lacks control or comparison group
Participants -> IV (treatment group only) -> DV
One-Group Pretest-Posttest Design
Measures participants before and after the manipulation
Participants -> DV Pretest -> IV (treatment group only) -> DV Posttest
Threats to internal validity not controlled by quasi-experiments:
History
Maturation
Testing
Regression toward the mean
Subject attrition (mortality)
Selection
History
Events that occur during participation that affect behavior
Maturation
Changes due to the passage of time that affect behavior
Testing
Taking a test can affect subsequent testing
Instrumentation
Changes in measurement instruments (including observers) over time
Regression toward the mean
Extreme scores are likely to be followed by more moderate scores
Subject Attrition (mortality)
Participants selectively drop out of the experiment
Selection
When control and experimental groups are chosen in such a way that they are not equivalent
Nonequivalent Control Group Design
Participants in the control and experimental groups are not equivalent
(selection)
Participants -> IV (treatment and no treatment groups) -> DV
Nonequivalent Control Group Pretest-Posttest Design
Groups are not equivalent, but we can look at changes in scores
Participants -> DV Pretest -> IV (treatment and no treatment groups) ->DV Posttest
Propensity score matching
Matching individuals in the control and treatment groups based on propensity scores
Interrupted time series design
Examine a series of observations before and after a treatment to look for canges in behavior
Control series design
Interrupted time series design with a control group
Single Case Experimental Design
NOT a case study
Behavior recorded during baseline period, researcher manipulates IV, behavior is recorded again
Problems in all experiments
Contamination
Experimenter expectancy effect/observer bias
Reactivity
Contamination
Communication between participants
Reactivity
When participants behave differently because they know they are being studied
Developmental Research Design
used to study changes in behavior associated with age
Note 
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