# ANCOVA: overview

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ANCOVA
Chi-squared test for the relationship between two categorical variables
Independent variablesIndependent /column variable
One or more categorical with independent groups, and one or more quantitative control variables of interval or ratio level (covariates)One categorical with $I$ independent groups ($I \geqslant 2$)
Dependent variableDependent /row variable
One quantitative of interval or ratio levelOne categorical with $J$ independent groups ($J \geqslant 2$)
THIS TABLE IS YET TO BE COMPLETEDNull hypothesis
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• There is no association between the row and column variable
More precise statement:
• If there are $I$ independent random samples of size $n_i$ from each of $I$ populations, defined by the independent variable:
The distribution of the dependent variable is the same in each of the $I$ populations
• If there is one random sample of size $N$ from the total population:
The row and column variables are independent
n.a.Alternative hypothesis
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• There is an association between the row and column variable
More precise statement:
• If there are $I$ independent random samples of size $n_i$ from each of $I$ populations, defined by the independent variable:
The distribution of the dependent variable is not the same in all of the $I$ populations
• If there is one random sample of size $N$ from the total population:
The row and column variables are dependent
n.a.Assumptions
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• Sample size is large enough for $X^2$ to be approximately chi-squared distributed under the null hypothesis. Rule of thumb:
• 2 $\times$ 2 table: all four expected cell counts are 5 or more
• Larger than 2 $\times$ 2 tables: average of the expected cell counts is 5 or more, smallest expected cell count is 1 or more
• There are $I$ independent simple random samples from each of $I$ populations defined by the independent variable, or there is one simple random sample from the total population
n.a.Test statistic
-$X^2 = \sum{\frac{(\mbox{observed cell count} - \mbox{expected cell count})^2}{\mbox{expected cell count}}}$
where for each cell, the expected cell count = $\dfrac{\mbox{row total} \times \mbox{column total}}{\mbox{total sample size}}$, the observed cell count is the observed sample count in that same cell, and the sum is over all $I \times J$ cells
n.a.Sampling distribution of $X^2$ if H0 were true
-Approximately a chi-squared distribution with $(I - 1) \times (J - 1)$ degrees of freedom
n.a.Significant?
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• Check if $X^2$ observed in sample is equal to or larger than critical value $X^{2*}$ or
• Find $p$ value corresponding to observed $X^2$ and check if it is equal to or smaller than $\alpha$
n.a.Example context
-Is there an association between economic class and gender? Is the distribution of economic class different between men and women?
n.a.SPSS
-Analyze > Descriptive Statistics > Crosstabs...
• Put one of your two categorical variables in the box below Row(s), and the other categorical variable in the box below Column(s)
• Click the Statistics... button, and click on the square in front of Chi-square
• Continue and click OK
n.a.Jamovi
-Frequencies > Independent Samples - $\chi^2$ test of association
• Put one of your two categorical variables in the box below Rows, and the other categorical variable in the box below Columns
Practice questionsPractice questions