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40 Cards in this Set
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Dependent means/groups

Association or link in the research design between the sets of scores. Usually occurs in one of three conditions  repeated measures, linked selection, or matching.


Repeated Measures

Repeated measures designs collect data on subjects using the same measure on at least two occasions. This often occurs before and after a treatment or when the same research subjects are exposed to two different experimental conditions.


Linked Selection

When selection is with the intent to analyze the data together (e.g. parentchild, partners)


Subject matching

When you want to control for something (e.g. socioeconomic differences in research subjects). You match on the variable and the scores on the dependent variables are then treated as a pair in the statistical test.


Statistics for dependent means

McNemar Test (two samples or times of measurement), Wilcoxon t Test (two samples), Dependent Means t Test (two samples), Friedman ANOVA for Ranks (three or more samples), Simple Repeated Measures ANOVA (three or more samples), and Mixed Factorial ANOVA (at least one factor is linked/correlated).


Independent means/groups

No subject overlap across group (can the person be in both groups? e.g. gender)


Statistics for Independent Means

chisquare test of independence (two or more groups), MannWhitney U Test (two groups), Independent Means t test (two groups), OneWay BetweenGroups ANOVA (three or more groups), and Factorial ANOVA (two or more independent variables).


Nominal Data

McNemar – two sample/dependent
ChiSquares – all others 

Nominal – one sample
Dependent variable 
Chi goodness of fit


Nominal –one sample
DV & IV 
Chi test of independence


Nominal – two samples
Independent Groups 
Chi test of independence


Nominal – two samples
Dependent Groups 
McNemar


Ordinal data one sample
DV & IV 
Spearman’s r’s


Ordinal Data – multiple samples
Independent Samples Two samples 
MannWhitney U


Ordinal Data – multiple samples
Independent Samples 3+ samples 
Kruskal Wallis H


Ordinal Data – multiple samples
Dependent samples Two samples 
Wilcoxon T


Ordinal Data – multiple samples
Dependent samples 3+ samples 
Friedman Anova by Ranks


Scale Data

Includes approximate interval, interval and ratio
group differences or associations between independent and dependent variables 

Scale data – one sample
Dependent variable Known population sd 
Z test


Scale data – one sample
Dependent variable Known population mean 
Onesample ttest


Scale data – one sample
(1) IV & DV 
Pearsons r
Bivariate regression 

Scale data – one sample
Mulitple IVs & DVs 
Multiple Regression


Scale Data Multiple Samples
Independent Samples Two Samples 
Independent ttest


Scale Data Multiple Samples
Independent Samples Three Samples 
Oneway ANOVA
Factorial ANOVA 

Scale Data Multiple Samples
Dependent Samples Two Samples 
Dependent ttest


Scale Data Multiple Samples
Dependent Samples Three Samples 
Repeated Measures ANOVA
Mixed Factorial ANOVA 

Parametric Assumptions
KolmogorovSmirnov Test 
used to determine how likely it is that a sample came from a population that is normally distributed.


Parametric Assumptions
Levene test 
used to test the assumption of equal variances


Parametric Assumptions

A) Interval, ration, approximate interval scale
B) random sampling from a defined population C) Characteristic is normally distributed in the population D) Population variance is equal (if two or more groups/variables) 

Violated parametric test assumptions

A) transform data
B) Use nonparametric statistic 

Parameter

A value, usually numeric, characteristic of a population
Mu – mean Sigma – standard deviation Sigma square – variance 

Parametric tests assume:

A) populations from which samples are drawn have specific characteristics
B) samples are drawn under certain conditions. 

Nonparametric tests assume:

A) sampling (random)
B) independence or dependence of samples (varies by test) but make no assumptions about the population. 

onesample tests

Z test; t test; Pearson and Spearman correlations; chisquare goodnessoffit


Two sample tests

("samples" for any statistic that examines differences between groups t test for dependent means)
t test for independent means; oneway ANOVA; Friedman ANOVA; chisquare test of independence 

Type I Error

Rejecting the null when it is true
Alpha Usually with onetailed test 

Type II Error

Rejecting the alternative hypothesis (fails to reject the null hypothesis) when the alternative hypothesis is true.
Beta 

Power of test

1 minus beta
Opposite of type 2 error increased by increase sample size, alpha, effect magnitude, decreasing random error, use parametric and onetailed tests. 1B (inverse of Beta) 

Increasing Power

A) Increasing alpha, decreases beta and increases power
B) sample size C) effect size D) directional –one tailed are more powerful 

Effect size

The degree of distance between the null hypothesis and alternative hypothesis distributions. The larger the effect size, the easier it is to detect a true difference between the two population means. The effect size is the only factor that influences power that is not under the investigator's control. We estimate the effect size (anticipated difference between our null hypothesis and alternative hypothesis) from the literature.
