an investigation of a hypothesis that 2 or more groups differ with respect to measures of a variable (behavior, characteristics, beliefs)

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Choosing the right statistic depends on:

Type of measurement

Nature of the comparison

Number of groups to be compared

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x-squared test

testing statistical significance of contingency table

compare the observed frequencies with expected frequencies

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Testing Hypotheses

examine statistical significance of observed contingency table

examine whether the difference between observed and expected values are consistent with hypothesized prediction

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Paired samples t-test

compares the scores of 2 interval variables drawn from related populations

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z-test

comparing 2 proportions

Test hypothesis that proportions are significantly different 2 independent samples

requires sample size>30

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Analysis of Variance (ANOVA)

investigating the effects of one treatment variable on an interval scaled dependent variable

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Between group variances

sum of the differences between the group mean and the grand mean summed over all groups for a given set of observations

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Within-group error

sum of the differences between the observed values and the group mean

also called total error variance

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F-test

used to determine whether there is more variability in the scores of one sample than in the scores of another

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General linear model

a way of explaining and predicting a dependent variable based on fluctuations (variation) from its mean due to changes in independent variables

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Multiple regression analysis

An analysis of association where the effects of 2 or more independent variables on a single, interval scaled dependent variable investigated simultaneously

Click Card to flip

Parameter estimate choices

Raw regression estimates (b1): used if the purpose of the regression analysis is forecasting

standardized regression estimates (B1): advantage of a constant scale, used when researcher is trying to explain some outcomei

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Test of differences

an investigation of a hypothesis that 2 or more groups differ with respect to measures of a variable (behavior, characteristics, beliefs)

Choosing the right statistic depends on:

Type of measurement

Nature of the comparison

Number of groups to be compared

x-squared test

testing statistical significance of contingency table

compare the observed frequencies with expected frequencies

Testing Hypotheses

examine statistical significance of observed contingency table

examine whether the difference between observed and expected values are consistent with hypothesized prediction

Generated by
Koofers.com

Paired samples t-test

compares the scores of 2 interval variables drawn from related populations

z-test

comparing 2 proportions

Test hypothesis that proportions are significantly different 2 independent samples

requires sample size>30

Analysis of Variance (ANOVA)

investigating the effects of one treatment variable on an interval scaled dependent variable

Between group variances

sum of the differences between the group mean and the grand mean summed over all groups for a given set of observations

Generated by
Koofers.com

Within-group error

sum of the differences between the observed values and the group mean

also called total error variance

F-test

used to determine whether there is more variability in the scores of one sample than in the scores of another

General linear model

a way of explaining and predicting a dependent variable based on fluctuations (variation) from its mean due to changes in independent variables

Multiple regression analysis

An analysis of association where the effects of 2 or more independent variables on a single, interval scaled dependent variable investigated simultaneously

Generated by
Koofers.com

Parameter estimate choices

Raw regression estimates (b1): used if the purpose of the regression analysis is forecasting

standardized regression estimates (B1): advantage of a constant scale, used when researcher is trying to explain some outcomei

Generated by
Koofers.com

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Test of differences

an investigation of a hypothesis that 2 or more groups differ with respect to measures of a variable (behavior, characteristics, beliefs)

Choosing the right statistic depends on:

Type of measurement

Nature of the comparison

Number of groups to be compared

x-squared test

testing statistical significance of contingency table

compare the observed frequencies with expected frequencies

Testing Hypotheses

examine statistical significance of observed contingency table

examine whether the difference between observed and expected values are consistent with hypothesized prediction

Paired samples t-test

compares the scores of 2 interval variables drawn from related populations

z-test

comparing 2 proportions

Test hypothesis that proportions are significantly different 2 independent samples

requires sample size>30

Analysis of Variance (ANOVA)

investigating the effects of one treatment variable on an interval scaled dependent variable

Between group variances

sum of the differences between the group mean and the grand mean summed over all groups for a given set of observations

Within-group error

sum of the differences between the observed values and the group mean

also called total error variance

F-test

used to determine whether there is more variability in the scores of one sample than in the scores of another

General linear model

a way of explaining and predicting a dependent variable based on fluctuations (variation) from its mean due to changes in independent variables

Multiple regression analysis

An analysis of association where the effects of 2 or more independent variables on a single, interval scaled dependent variable investigated simultaneously

Parameter estimate choices

Raw regression estimates (b1): used if the purpose of the regression analysis is forecasting

standardized regression estimates (B1): advantage of a constant scale, used when researcher is trying to explain some outcomei

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