Statistics and Research Methods Flashcards

1
Q

Campbell’s validity typology

A

Internal validity, external validity, statistical conclusion validity, construct validity

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2
Q

Internal validity

A

Extent to which association between variables is causal in nature. Valid causal inference requires 1) statistical association, 2) temporal precedance and 3) nonspuriouslness

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3
Q

Case-control designs

A

Compares a group of participants with a certain characteristic (case group) to a group without that trait (control group).

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4
Q

Cohort designs

A

Intact group is followed over time to examine changes in outcome of interest. Design is longitudinal or prospective.

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5
Q

Cross-sequential design

A

When multiple cohorts differ in age or some other salient developmental marker at the study’s inception

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6
Q

Quasi-Experimental Studies

A

Experiments without random assignment. Subset of units is typically exposed to an intervention. Experimental designs include interrupted time series design and regression discontinuity design.

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7
Q

Single-Case Experiments

A

Common features include intensive assessment before, during, and after intervention. Prolonged baseline assessment provides information about the pattern of changes in the outcome in the absence of the intervention.

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8
Q

ABAB Designs

A

single-case design that alternates the baseline A phase (intervention absent) with an intervention B phase (intervention present). The outcome of interest is assessed on multiple occasions within each phase.

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9
Q

Multiple baseline designs

A

Replication of an effect is sought over multiple baselines, which can reflect different behaviors, settings, and/or children (just to name a few).

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10
Q

Hypothetical counterfactual

A

In RTC, what would have happened to those not in treatment condition. What you learn from control condition.

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11
Q

Efficacy trials

A

Intervention’s effects are examined under ideal circumstances, particularly with respect to treatment implementation

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12
Q

Effectiveness trials

A

Intervention’s effects are examined under real-world conditions.

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13
Q

Intent-to-treat analyses

A

Designed to analyze outcome data from randomized experiments involving participant attrition. Researchers analyze outcome data from participants as a function of their original group assignment, regardless of their level of exposure to treatment.

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14
Q

Levels of evidence

A

Mudford, McNeill, Walton, and Phillips (2012). As knowledge in an area accumulates, discussions of ordering evidence sources along a continuum from low to high become more prominent.

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15
Q

Threat to construct validity

A

Therapeutic attention (i.e. common factors), confounding clinicians with treatment, dynamics of a group for group therapy trials.

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16
Q

Threats to internal validity

A

Maturation, history, statistical regression, attrition, testing effect, instrumentation, selection and interactions with selection.

17
Q

Parametric statistics

A

Assumption of normal distribution, data measured on interval or ratio scale

18
Q

Type I error

A

true null hypothesis is incorrectly rejected (i.e., results are declared statistically significant even though the null hypothesis is true).

19
Q

Type II error

A

False null hypothesis is not rejected (i.e., results are not declared statistically significant even though the null hypothesis is false).

20
Q

NNT (Number needed to treat)

A

Measure of clinical significance. For binary outcome, reflects rate of success in treatment versus control group. When NNT is larger, effect is smaller.

21
Q

One sample t-test

A

Used to test the difference between a single sample mean and an hypothesized population mean. Used instead of the one-sample z test if the population SD is unknown (a more typical scenario).

22
Q

Student’s t-test

A

Independent samples t-test

23
Q

Paired (related) samples t-test

A

each “participant” contributes a pair of data points to the analysis, which are assumed to be dependent (i.e., correlated).

24
Q

Omnibus tests of significance

A

In models for which there are three or more levels of a factor (e.g., low, medium, and high levels of stress), the test of the factor’s main effect. Doesn’t show whether 2 levels are significantly different

25
Q

Manova

A

More than 1 dependent variable