ANCOVA - overview
This page offers structured overviews of one or more selected methods. Add additional methods for comparisons (max. of 3) by clicking on the dropdown button in the right-hand column. To practice with a specific method click the button at the bottom row of the table
ANCOVA | Pearson correlation | Marginal Homogeneity test / Stuart-Maxwell test |
You cannot compare more than 3 methods |
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Independent variables | Variable 1 | Independent variable | |
One or more categorical with independent groups, and one or more quantitative control variables of interval or ratio level (covariates) | One quantitative of interval or ratio level | 2 paired groups | |
Dependent variable | Variable 2 | Dependent variable | |
One quantitative of interval or ratio level | One quantitative of interval or ratio level | One categorical with $J$ independent groups ($J \geqslant 2$) | |
THIS TABLE IS YET TO BE COMPLETED | Null hypothesis | Null hypothesis | |
- | H0: $\rho = \rho_0$
Here $\rho$ is the Pearson correlation in the population, and $\rho_0$ is the Pearson correlation in the population according to the null hypothesis (usually 0). The Pearson correlation is a measure for the strength and direction of the linear relationship between two variables of at least interval measurement level. | H0: for each category $j$ of the dependent variable, $\pi_j$ for the first paired group = $\pi_j$ for the second paired group.
Here $\pi_j$ is the population proportion in category $j.$ | |
n.a. | Alternative hypothesis | Alternative hypothesis | |
- | H1 two sided: $\rho \neq \rho_0$ H1 right sided: $\rho > \rho_0$ H1 left sided: $\rho < \rho_0$ | H1: for some categories of the dependent variable, $\pi_j$ for the first paired group $\neq$ $\pi_j$ for the second paired group. | |
n.a. | Assumptions of test for correlation | Assumptions | |
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n.a. | Test statistic | Test statistic | |
- | Test statistic for testing H0: $\rho = 0$:
| Computing the test statistic is a bit complicated and involves matrix algebra. Unless you are following a technical course, you probably won't need to calculate it by hand. | |
n.a. | Sampling distribution of $t$ and of $z$ if H0 were true | Sampling distribution of the test statistic if H0 were true | |
- | Sampling distribution of $t$:
| Approximately the chi-squared distribution with $J - 1$ degrees of freedom | |
n.a. | Significant? | Significant? | |
- | $t$ Test two sided:
| If we denote the test statistic as $X^2$:
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n.a. | Approximate $C$% confidence interval for $\rho$ | n.a. | |
- | First compute the approximate $C$% confidence interval for $\rho_{Fisher}$:
Then transform back to get the approximate $C$% confidence interval for $\rho$:
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n.a. | Properties of the Pearson correlation coefficient | n.a. | |
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n.a. | Equivalent to | n.a. | |
- | OLS regression with one independent variable:
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n.a. | Example context | Example context | |
- | Is there a linear relationship between physical health and mental health? | Subjects are asked to taste three different types of mayonnaise, and to indicate which of the three types of mayonnaise they like best. They then have to drink a glass of beer, and taste and rate the three types of mayonnaise again. Does drinking a beer change which type of mayonnaise people like best? | |
n.a. | SPSS | SPSS | |
- | Analyze > Correlate > Bivariate...
| Analyze > Nonparametric Tests > Legacy Dialogs > 2 Related Samples...
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n.a. | Jamovi | n.a. | |
- | Regression > Correlation Matrix
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Practice questions | Practice questions | Practice questions | |