Chapter 15

Created by

Sara Alsaialy

Cards (43)

Test of Difference 
An investigation of a hypothesis that two (or more) groups differ with respect to measures on a variable
Bivariate Tests of Differences
Involve only two variables: a variable that acts like a dependent variable and a variable that acts as a classification variable
Choosing the right statistic depends on: Type of measurement, Nature of the comparison, Number of groups to be compared
Common Bivariate Tests 
Mann-Whitney U-test
Wilcoxon test
Kruskal-Wallis test
Z-test (two proportions)
Chi-square test
t-test or Z-test
One-way ANOVA
Cross-Tabulation (Contingency) Table 
A joint frequency distribution of observations on two or more variables
Chi-Square Test 
Provides a means for testing the statistical significance of a contingency table
Independent Samples t-Test 
A test for hypotheses stating that the mean scores for some interval- or ratio-scaled variable grouped based on some less-than-interval classificatory variable are not the same
Pooled Estimate of the Standard Error
An estimate of the standard error for a t-test of independent means that assumes the variances of both groups are equal
Paired-Samples t-Test 
An appropriate test for comparing the scores of two interval variables drawn from related populations
Test for Differences of Proportions 
A technique used to test the hypothesis that proportions are significantly different for two independent samples or groups
Test for Comparing Two Proportions 
Test statistic for differences in large random samples
Sp1-p2 
Pooled estimate of the standard errors of differences of proportions
One-Way Analysis of Variance (ANOVA) 
An analysis involving the investigation of the effects of one treatment variable on an interval-scaled dependent variable - a hypothesis-testing technique to determine whether statistically significant differences in means occur between two or more groups
ANOVA tests whether "grouping" observations explains variance in the dependent variable
The substantive hypothesis tested in ANOVA is: At least one group mean is not equal to another group mean
Grand Mean 
The mean of a variable over all observations
Between-Groups Variance 
The sum of differences between the group mean and the grand mean summed over all groups for a given set of observations
Within-Group Error or Variance
The sum of the differences between observed values and the group mean for a given set of observations; also known as Total Error Variance
Test 
A procedure used to determine whether there is more variability in the scores of one sample than in the scores of another sample
General Linear Model (GLM) 
A way of explaining and predicting a dependent variable based on fluctuations (variation) from its mean due to changes in independent variables
Regression Analysis 
A measure of linear association that investigates straight-line relationships between a continuous dependent variable and an independent variable that is usually continuous, but can be a categorical dummy variable
Correlation Coefficient 
A statistical measure of the covariation, or association, between two at-least interval variables
Correlation does not imply causation
Standardized Regression Coefficient (β)
Estimated coefficient of the strength of relationship between the independent and dependent variables, expressed on a standardized scale where higher absolute values indicate stronger relationships
Slope of the coefficient
Rise over run
β 
Indicative of the strength and direction of the relationship between the independent and dependent variable
α (Y intercept) 
A fixed point that is considered a constant (how much Y can exist without X)
Standardized Regression Coefficient (β)
Estimated coefficient of the strength of relationship between the independent and dependent variables, expressed on a standardized scale where higher absolute values indicate stronger relationships (range is from -1 to 1)
Raw regression estimates (b1)
Raw regression weights have the advantage of retaining the scale metric, which is also their key disadvantage. If the purpose of the regression analysis is forecasting, then raw parameter estimates must be used.
Standardized regression estimates (β1)
Standardized regression estimates have the advantage of a constant scale. Standardized regression estimates should be used when the researcher is testing explanatory hypotheses.
Multiple Regression Analysis 
An analysis of association in which the effects of two or more independent variables on a single, interval-scaled dependent variable are investigated simultaneously.
Dummy variable 
The way a dichotomous (two group) independent variable is represented in regression analysis by assigning a 0 to one group and a 1 to the other.
Regression Equation: Y = b0 + b1X1 + b2X2 + b3X3 + ... + biXi
Coefficient of multiple determination (R2): 0.845
value= 14.6; p<.05
Partial correlation 
The correlation between two variables after taking into account the fact that they are correlated with other variables too.
R2 in Multiple Regression 
The coefficient of multiple determination in multiple regression indicates the percentage of variation in Y explained by all independent variables.
Coefficients of Partial Regression (bn)
The percentage of variance in the dependent variable that is explained by a single independent variable, holding other independent variables constant.
test 
Tests statistical significance by comparing the variation explained by the regression equation to the residual error variation.
(MSR/k) / (MSE/(n-k-1)) = F