PSY 3001W Exam 3

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73 Terms

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Complex Design

Two or more IVs manipulated simultaneously in a single experiment

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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

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Null Hypothesis

The population means are equal - the observed difference is due to random error

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Research Hypothesis

The population means are not equal

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Statistical Significance

A significant result is one that has a very low probability of occurring if the population means are equal

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Indicates that there is a low probability that the difference between the obtained sample means was sue to random error

low p-value

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Probability

The likelihood of the occurrence of some event or outcome

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Alpha Level

The probability required for significance

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Sampling Distribution

Probability of obtaining possible outcomes (usually a table)

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t test

Used to examine whether two groups are significantly different from each other

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Degrees of Freedom (df)

Used to determine critical value

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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)

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Systematic Variance (Between Group Variance)

The deviation of the group means from the grand mean (mean score of all individuals in all groups)

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Error Variance (Within Group Variance)

The deviation of the individual scores in each group from their respective group means

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Confidence Interval

Most likely range of actual population values

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Type I Error

Made when we reject the null hypothesis but the null hypothesis is actually true (alpha)

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Type II Error

Made when we accept the null hypothesis although in the population the research hypothesis is true (beta)

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Power

1 - beta (Type II Error)

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Pearson r correlation

Used to describe the strength of the relationship between two variables when both variables have interval or ratio scale properties

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Use Chi-square when:

IV is nominal and DV is nominal

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Use t test when:

IV is nominal (2 groups) and DV is interval/ratio

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Use one-way analysis of variance when:

IV is nominal (3 groups) and DV is interval/ratio

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Use Pearson correlation when:

IV is interval/ratio and DV is interval/ratio

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Use Analysis of variance (factorial design/F test) when:

IV is nominal (2+ variables) and DV is interval/ratio

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Use Multiple regression when:

IV is interval/ratio (2+ variables) and DV is interval/ratio

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p-value

Probability of obtaining that value of the statistic or a more extreme value of the statistic if the null hypothesis is true

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p < .05

significant results

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p > .05

not significant results

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r= .15

small effect size

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r = .30

medium effect size

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r = .40

large effect size

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Power Analysis

Given the effect size and level of significance, determine N needed to detect effect

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Single-case experimental design (single-subject design)

Uses one subject. Seen in applied behavior analysis

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Baseline

Control period where subject's behavior is measured

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Reversal Design (ABA Design)

A (baseline period) -> B (treatment period) -> A (baseline period)

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Multiple Baseline Design

Effectiveness of treatment is demonstrated when a behavior changes only after the manipulation is introduced. Multiple circumstances.

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Across Subjects Multiple Baseline Design

Behavior of several subjects is measured over time. The manipulation was introduced at a different point in time.

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Across Behaviors Multiple Baseline Design

Several different behaviors in a single subject are measured over time.

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Across Situations Multiple Baseline Design

Same behavior is measured in different settings

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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.

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One-Group Posttest-Only Design

Lacks control or comparison group

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One-Group Pretest-Posttest Design

Measures participants before and after the manipulation

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Threats to internal validity not controlled by quasi-experiments:

History

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History

Events that occur during participation that affect behavior

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Maturation

Changes due to the passage of time that affect behavior

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Testing

Taking a test can affect subsequent testing

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Instrumentation

Changes in measurement instruments (including observers) over time

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Regression toward the mean

Extreme scores are likely to be followed by more moderate scores

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Subject Attrition (mortality)

Participants selectively drop out of the experiment

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Selection

When control and experimental groups are chosen in such a way that they are not equivalent

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Nonequivalent Control Group Design

Participants in the control and experimental groups are not equivalent

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Nonequivalent Control Group Pretest-Posttest Design

Groups are not equivalent, but we can look at changes in scores

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Propensity score matching

Matching individuals in the control and treatment groups based on propensity scores

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Interrupted time series design

Examine a series of observations before and after a treatment to look for canges in behavior

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Control series design

Interrupted time series design with a control group

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Single Case Experimental Design

NOT a case study

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Problems in all experiments

Contamination

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Contamination

Communication between participants

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Reactivity

When participants behave differently because they know they are being studied

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Developmental Research Design

used to study changes in behavior associated with age

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Cross-Sectional Method

Randomly select participants from different age groups

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Longitudinal Method

Single group followed over time and measured repeatedly

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Interaction

When effects of one variable depend on the level of another variable

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Factorial Designs

2 x 3

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Sequential Method

Mix between Cross-Sectional Method (first part of sequential method) and longitudinal method

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Main Effect

The effect each variable has by itself

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IV x PV Design

Factorial design with an experimental and non experimental variable (eg sex)

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Simple Main Effect

Examines the mean differences at each level of the independent variable

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Independent Groups (between subjects) Design

A different group of participants is assigned to each of the conditions

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Repeated Measures (within subjects) Design

The same individuals participate in all conditions

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Solomon four group design

Half of the participants are given the pretest, and the other half receive the posttest only

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Literature Review

Researcher reads a number of studies about a topic and writes a paper summarizing and evaluating the literature

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Meta-Analysis

Researcher combines actual results of studies and uses statistics to compare a given finding across multiple studies (focus on effect size)