15610674
| Frage | Antworten |
| Correlational Research Designs | Describes linear relationships among measured variables Hypo: There is a relationship between variable x and variable y. |
| Organizing the Data Visually Scatterplot | Uses a standard coordinate system where The horizontal axis -> scores on the predictor variable (x-axis) The vertical axis -> scores on the outcome variable (y-axis) Data is plotted for each individual at the intersection of his or her scores on the two variables |
| Organizing the Data Visually Regression line | straight line of “best fit” |
| Linear relationship | Relationship is easily approximated with a regression line |
| Nonlinear relationships | Relationship cannot be determined with a regression line Independent and Curvilinear |
| Patterns of Relationships Between Two Variables Independent | When there is no relationship at all between the two variables |
| Patterns of Relationships Between Two Variables Curvilinear relationships | Relationships that change in direction |
| Pearson product-moment correlation coefficient | Summarizes the association between two quantitative variables: strength direction Frequently designated by the letter r Values range from r = -1.00 to r = +1.00 |
| The Pearson Correlation Coefficient | The direction of the relationship is indicated by the sign of the correlation coefficient Positive values of r indicate positive linear relationships Negative values of r indicate negative linear relationships The strength of the linear relationship: Indexed by the absolute value distance of the correlation coefficient from zero A significant r indicates there is a linear association between the variables. |
| Correlation Matrix | A table showing the correlations of many variables with each other |
| The Chi-Square Statistic | Relationship between two nominal variables Technically known as the chi-square test of independence Calculated by constructing a contingency table, which displays the number of individuals in each of the combinations of the two nominal variables |
| Multiple regression | A statistical analysis procedure for: 2+ predictor variable (2 or more IVs) single outcome variable (1 DV) Based on Pearson correlation coefficients both between each predictor variable and the outcome variable and among the predictor variables themselves |
| Correlation and Causality | Cannot be used to draw conclusions about the causal relationships among the measured variables Although the researcher may believe the predictor variable is causing the outcome variable: Cannot determine causality Can help predict relationships |
| Strengths/Weaknesses of Correlational Designs Strengths | When predictor variables cannot be manipulated Study behavior as it occurs in everyday life To predict an outcome variable but don’t need to determine cause |
| Strengths/Weaknesses of Correlational Designs Weaknesses | Cannot determine causal relationship |
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