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Quantitative Analysis: Inferential Statistics - the statistical procedures…
Quantitative Analysis: Inferential Statistics - the statistical procedures that are used to reach conclusions about associations between variables; designed to test hypotheses.
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General Linear Model (GLM) - a system of equations that can be used to represent linear (straight line) patterns of relationships in observed data.
Two-Variable Linear Model - simplest type of GLM that examines the relationship between one independent variable (the cause of predictor) and one dependent variable (effect or outcome).
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Covariates (Control Variables) - variables that are not of theoretical interest but may have some impact on the dependent variable so that the residual effects of the independent variables of interest are detected more precisely.
Dummy Variables - predictor variables that may even be nominal variables; can assume one of only two possible values: 0 or 1 (e.g., gender - male or female).
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Analysis of Variance (ANOVA) - when there is a dummy predictor variable and comparing the effects of the two levels (0 & 1) of the dummy variable on the outcome variable; Analysis of Covariance (ANCOVA) - while controlling for the effects of more than one covariate; Multivariate Regression - when multiple outcome variables are modeled as being predicted by the same set of predictor variables; Multivariate ANOVA (MANOVA) - when doing ANOVA or ANCOVA with multiple outcome variables; Structural Equation Modeling - when modeling the outcome in one regression equation as a predictor in another equation in an interrelated system of regression equations.
Significance Level - the maximum level of risk that we are willing to accept as the price of our inference from the sample to the population.
Model Specification - process of determining which variables to include and exclude from a model; most important problem in GLM.
Two-Group Comparison (T-Test) - examines whether the means of two groups are statistically different from each other (non-directional or two-tailed test), or whether one group has a statistically larger (or smaller) mean than the other (directional or one-tailed test).
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Alternative Hypothesis - at least one of the population means is different from another (there is an effect of at least one treatment).