Contingency Tables/Chi Square
Statistics
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 A contingency table is a table that shows the relationship between two categorical variables.
 The Chi-square statistic reflects the strength of this relationship. All else equal, the greater the chi-square statistic, the stronger the relationship. The chi square statistic is usually reported at the bottom of a contingency table.
 The probability associated with the chi-square statistic indicates the probability that you would be incorrect if you were to assert that there is a relationship between these same two variables in the population from which you drew your sample.

Contingency Tables/Chi Square
Statistics
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 If you are submitting an article for publication in a peer reviewed journal you may use a contingency table. You want to interpret it accurately.
 Before you implement a program, you want to review the literature and read about the same or similar programs. These articles may contain contingency tables. You want to be a wise consumer of information.
 If you are evaluating your own program, it is likely that you will use contingency tables. Contingency tables can not only help you discern if your program is working, but how effective it is and/or if the effectiveness varies by such factors as gender, race, etc.
 Before you make recommendations or engage in political advocacy, you want empirical evidence to substantiate your claims. Contingency tables can be used to provide this evidence.

Contingency Tables/Chi Square
Statistics
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Contingency tables and the corresponding chi-square statistic is very useful for those implementing programs, and should be used BEFORE important decisions are made.
Any responsible extension employee would use correctly implemented evaluation research to evaluate their programs and measure their objectives. Properly interpreted Chi square statistics can be an important part of that evaluation.
When applying for grants to support existing programs, it is a good idea to use statistics to validate claims that the program “works.” Chi square can be one of these statistics.
When justifying grant monies spent on programs, evaluation research should be used. Responsible administrators should use accurate statistics to provide evidence that their objectives were met.

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• BEFORE you use any Intervention, so you can determine if there is evidence that it will work and for whom it will work.
• For instance – You should be able to interpret contingency tables reported in the literature that have been used to evaluate similar programs. You want to know if there is evidence that a program like yours will work, and what factors could influence its effectiveness.
• BEFORE implementing and/or continuing any program, so you will know if it’s worth your continued time and effort.
• For instance - Is the percent of students who received an A in school greater for those who belonged to your 4-H program relative to those who did not?
• For instance - What factors are most important when attempting to increase the effectiveness of this intervention?
• For instance - What percent of those involved in intervention 1 saw the program as effective compared to the percent involved in intervention 2.

• BEFORE you report the results of your evaluation and/or make recommendations, so you can accurately explain the risks and probabilities.
• For instance – If having access to computers related to scores on standardized tests, then (1) Are scores on standardized tests related to academic achievement, and (2) How many computers must you purchase to see a significant gain in standardized test scores for this group?
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• For instance - What percent of your clients experienced increased economic stress after paying to participate in your program? Does this economic stress outweigh benefits?
For instance, Can you identify the most important factors in your program? If you were forced to curtail activities which activities could you eliminate without significantly decreasing positive outcomes for your participants?

The statistics generated by the computer for chisquare are only valid if the data meet the following qualifications:
 Both the independent and dependent variables are categorical.*
 Researchers used a random sample to collect data.
 Researchers had an adequate sample size.
Generally the sample size should be at least 100.
 The number of respondents in each cell should be at least 5. If not, you can use a Fisher’s Exact or other tests.
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Generally the number of categories is somewhere between 3 and 7.
More than 7 categories can be overwhelming and confusing to the reader.

Contingency Tables/Chi Square
Statistics
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• There are two parts of a contingency table that you must correctly interpret:
• The chi square statistic
• The numbers in the table

Contingency Tables/Chi Square
Statistics
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• The chi square statistic
• It is relatively easy to interpret a chi square statistic if you know three things
• First – all else equal, the greater the chi square number, the stronger the relationship between the dependent and independent variable
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• Second – the lower the probability associated with a chi-square statistic, the stronger the relationship between the dependent and independent variable.
• Third – If your probability is .05 or less, then you can generalize from a random sample to a population, and claim the two variables are associated in the population.

Contingency Tables/Chi Square
Statistics
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• The numbers in the contingency table
• There are a few simple rules that makes it easier to interpret a contingency table. These include:
• First – to avoid confusion, always put the independent variable on the side and the dependent variable on the top.
• Second – focus on the percent not on the frequency.
• Third – use the percent that totals to 100 percent for each independent variable (e.g., the row percent).
• Fourth – compare the percent for each category of the independent variable across the independent variables.

Contingency Table. Mother’s Racial Identification by
Probability of Receiving Bad News about their Kindergarten
Child from the Child’s Teacher.
A contingency table reported in the literature should look something like the one above.
When interpreting this table, you compare the percent of each independent category. In this case, 10% of
White mothers, compared to 44.83% of Black mothers and
35.71% of Hispanic mothers received bad news.
The probability associated with the chi square statistic of 9.205 is less than .01 indicating there is a strong relationship between whether or not the mom received bad news and her race.

Computer Printouts
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Chi- Square/Contingency Table
• Purpose
• Examine relationship between two categorical variables
• Determine if the dependent variable is contingent on the independent variable
• Definition of Terms
• Cells
• Squares that make up chi-square printout (sometimes referred to as table)
• Each cell contains 4 numbers. These are referred to as:
• Frequency (raw numbers or count)
• Percent
• Row Percent
• Column Percent
• Tabulates the number of times each possible combination of the values of the independent and dependent variables occur (in your sample)
• Rows (r)
• Cells that are attached horizontally are referred to as a row
• Row percents make up the numbers in the cells that percentage to 100 across these rows.

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Columns (c)
• Cells that are attached vertically are referred to as a column
• Column percents make up the numbers in the cells that percentage to 100 down these columns.
Degrees of Freedom
• A number that you would use to find the critical value of a chi square statistic using a chi square table.
• The formula you use to compute a chi square statistic is (r – 1) (c – 1) = df
Critical Value of Chi-Square Statistic
• The value of the chi-square statistic associated with a
.05 probability of making an error if you reject the null hypothesis. Your chi square statistic must be greater than this if your relationship is significant. This is not reported in the printout, but the reported value must be greater than this if you are to reject the null hypothesis.
Computed Chi-Statistic
• The computed value of the chi-square statistic.
• Generally the greater the chi square statistic, the lower the probability that you make a mistake if you reject the null hypothesis.
• Generally the greater the chi square statistic, the stronger the relationship between the independent
and dependent variable.

Chi Square
Statistic reported here
Probability reported here

Research
Question:
Is preferred ice cream flavor related to race?
* Please note….The data is fabricated and to be used only for illustration.

Research Question: Is race related to ice cream flavor preference?
One Interpretation:
65% of Blacks, compared to
35.96% of
Hispanics and
34.18% of Whites prefer Butter
Pecan. Blacks were almost twice as likely to prefer
Butter Pecan.
The relationship between race and ice cream preference is significant at the
.0001 level.
* Please note….The data is fabricated and to be used only for illustration.

Here is an example of a table
(above) that was constructed from a printout (below).
The table reports the row percent, the frequency in each cell and the chi square statistic.

Contingency Table. Mother’s Racial Identification by
Probability of Receiving Bad News about their Kindergarten
Child from the Child’s Teacher.
Chi-square results can be presented in a contingency table. In this table you report, the frequency in each cell. If the independent (causal) variable is on the side, you report the row percent. If the independent (causal) variable is on the top, you report the column percent.
When interpreting this table, you compare the percent of each independent category. In this case, 10% of
White mothers, compared to 44.83% of Black mothers and
35.71% of Hispanics mothers received bad news.

50
40
30
20
10
0
90
80
70
60
White Black Hispanic
No
Yes

90
80
70
60
50
40
30
20
10
0
Black
White
Hispanic
Yes No

100.00%
CHART 5
Middle/High School Students’ Perception of How Good
They are at Finding Resources by Whether or Not They
Made Decisions about Service Learning Projects
80.00%
60.00%
44% 43%
(p <0.0001)
40.00%
28%
24%
17%
20.00%
7%
9%
13% 11%
7%
0.00%
Strongly Disagree Undecided Agree Strongly
Disagree Agree
Contingency tables are often used to construct two-variable bar charts.
In this case, the extent to which students agree that they are good at finding resources is contingent on whether or not they made decisions about their Service Learning Project. Assuming the independent variable is on the side of the computer printout, the row percent is used to construct the bar chart. The probability is less than .0001 indicating a strong relationship between these two variables. All else equal, the less the probability, the stronger the relationship.

Interpreting Bar Charts
CHART 5
Middle/High School Students’ Perception of How Good
They are at Finding Resources by Whether or Not They
Made Decisions about Service Learning Projects
100.00%
80.00%
60.00%
40.00%
20.00%
0.00%
7%
9%
13%
24%
44% 43%
28%
17%
(p <0.0001)
11%
7%
Strongly
Disagree
Disagree Undecided Agree Strongly
Agree
Interpreting the Bar Chart
Interpret the most striking comparison for your audience. For instance, “Twenty-eight percent of those who made decisions about their Service Learning Project agreed that they were good at finding resources, compared to only seventeen percent of those who did not make decisions about their projects.”

100.00%
CHART 7
Middle/High School Students’ Perception of the Level to
Which They Agree Teachers Listen to Their Ideas by
Whether or Not They Made Decisions about Service
Learning Projects
80.00%
(p <0.0001)
60.00%
40.00%
20.00%
0.00%
9%
15%
21% 22%
35%
39%
27%
20%
8%
3%
Strongly
Disagree
Disagree Undecided Agree Strongly
Agree
In this case, the extent to which students feel teachers listen to them is contingent on whether or not they made decisions about their Service Learning Project. 8% of those who did make decisions about their Service Learning
Project felt their teachers listened to them compared to only 3% of those who did not make decisions.

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