Analysis of Variance (ANOVA)

Analysis of Variance (ANOVA)

• ANOVA compares the averages of different groups to determine if the differences are statistically significant.
• ANOVA is a statistical method used to test for differences between group means.
• Between-subjects ANOVA compares different groups of participants.
• A between-subjects factor is a variable that divides participants into different groups for comparison.
• Error variance refers to the variability in data due to random factors, not the variables under investigation.
• Eta squared measures the proportion of variance in the dependent variable that is explained by the independent variable.
• Experiment-wise error rate is the probability of making at least one Type I error when performing multiple hypothesis tests.
• Factor refers to an independent variable in an experiment that can be manipulated or observed.
• F-distribution is a probability distribution used in hypothesis testing.
• Fisher’s protected t-test is a post-hoc test used after ANOVA to determine which specific group differences are significant.
• F-ratio is a test statistic calculated in ANOVA.
• Mean square between groups measures the variability between the means of different groups.
• Mean square within groups measures the variability within each group.
• Multivariate statistics are statistical methods for analyzing data involving multiple variables simultaneously.
• One-way ANOVA tests whether the means of three or more groups are significantly different based on one independent variable.
• Post hoc comparisons are conducted after ANOVA to identify which specific group differences are significant.
• Sum of squares measures the total variability in a dataset.
• Treatment corresponds to a condition or intervention applied to different groups in an experiment.
• Treatment effect is the impact of a specific treatment on the outcome variable.
• Treatment variance is the variability in outcomes caused by the treatment in an experiment.
• Tukey’s HSD multiple comparisons test is a post-hoc test used in ANOVA to compare all possible pairs of group means.
• Univariate statistics involves methods that analyze one variable at a time.
• Within-subjects ANOVA compares the same group of participants across different conditions.

Correlation

• Correlation coefficient measures the strength and direction of the linear relationship between two variables.
• Curvilinear relationship is a relationship between two variables that follows a curved pattern.
• Linear relationship is a relationship where two variables change together at a constant rate.
• Negative linear relationship is a relationship where an increase in one variable is associated with a decrease in the other.
• Nonlinear relationship is any association between two variables that do not follow a straight line.
• Outlier is a data point that deviates significantly from other observations in a sample.
• Pearson correlation coefficient measures the strength and direction of a linear relationship between two continuous variables.
• Positive linear relationship is a relationship where an increase in one variable is associated with an increase in the other.
• Regression line is a line that best fits the data points in a scatterplot.
• Restriction of range occurs when the range of data available for analysis is limited.
• Scatterplot is a graphical representation of the relationship between two variables.
• Spearman rank-order correlation coefficient measures the strength and direction of association between two ranked variables.
• Type of relationship describes how two variables are related, such as linear, nonlinear, positive, or negative.

Regression

• Coefficient of alienation is the proportion of variance in the criterion variable that is not explained by the predictor variable.
• Coefficient of determination quantifies the proportion of variance in one variable that can be predicted from another variable.
• Criterion variable is the outcome variable that is being predicted in a regression analysis.
• Heteroscedasticity refers to the unequal scatter of data points around the regression line.
• Homoscedasticity refers to the equal scatter of data points around the regression line.
• Linear regression equation is a mathematical equation that predicts the value of one variable from another.
• Linear regression line is the line of best fit in a scatterplot representing the predicted values in a linear regression.
• Multiple correlation coefficient measures the strength of the relationship between multiple predictor variables and one criterion variable.
• Multiple regression equation is an equation that predicts the value of one variable from two or more predictor variables.
• Predicted Y score is the estimated value of the outcome variable based on the regression equation.
• Predictor variable is the variable used to make a prediction about an outcome in a study.
• Proportion of the variance accounted for indicates how much of the variability in the outcome variable is explained by the predictor variable.
• Slope represents the change in the predicted value of the dependent variable for every one-unit increase in the independent variable.
• Standard error of the estimate measures the accuracy of predictions made with a regression equation.
• Variance of the Y scores around Y’ is a measure of the dispersion of observed values around the predicted values in regression.
• Y intercept is the point where the regression line crosses the y-axis.