# HOMOSEXUALITY CAUSE EXPERIMENT

## The Wrong Way

All of the experiments attempting to show or prove a genetic cause for homosexuality have so far been shown to be bad science. For some strange reason, the experimenters keep trying to use the same wrong methods, including:

### Data Types

1. Numerical - data that can be measured as mathematic real numbers

They are continuous or interval data.

Examples:

• Length
• Volume
• Mass or weight
• Temperature
• Pressure
2. Categorical - data that can not be measured as mathematic real numbers

They are discrete or non-numeric data.

Examples:

• Name
• Eye color
• Presence of a gene
• Presence of a trait
3. Count - the number of instances of a case counted as a mathematical integer

They are positive integer (whole-number) data.

Examples:

• Number of subjects
• Number below poverty level
• Number with blue eyes
• Number with the gene
• Number with the trait
1. The purpose of the study was to prove what the "scientist" already believed. All such "science" is already doomed as bad science. That is not what science is supposed to do.

The purpose of science is to find out the truth, not to prove a preconceived belief.

2. No control group was present.
3. No control subjects were included in the experiments. All of the subjects studied had the wanted trait (homosexuality).
4. The method used to obtain subjects is highly suspect. Some of them advertised for volunteers in homosexual-interest magazines. There was no independent variable in the experiment -- nothing to compare the result to.
5. The experiments were designed wrong. The gene should have been the independent variable, and homosexuality should have been the dependent variable. Both values should have been collected over a large number of randomly-selected subjects.
6. The sample sizes were too small: †

A good study using Numerical values needs at least 30 randomly picked subjects in each category. If the data under test are Numerical values, 15 in the experimental group and 15 in the control group would do.

But in these experiments, we have only the Counts of subjects in each subset of categories. A crosstable must be used when the data are Categorical values. With Categorical values, randomly selected subjects must be collected without culling until at least 40 subjects are found for each possible value of the independent variable. In addition, at least 5 subjects should be found in each bin in the crosstable (unless none at all are found). This can result in hundreds of subjects to test. None may be removed from the sample pool without tainting the results.

7. The studies used the wrong statistics: †

One study used Student's t distribution, which is used to compare two sets of experimental Numeric values, or to compare a set of Numeric values to a norm. But they didn't have two sets of experimental values, and they did not reveal any norm.

Another study used a correlation test, but WHAT did they correlate the data to? Again, they didn't have either two experimental Numeric values or any norm to compare their one set of experimental values to.

Actually, neither of these tests is the correct test, because the data are Categorical (in this case, yes-or-no data). Categorical data are unordered, so there can be no Numerical value. Either the gene is there, or the gene is absent. Either the trait is there, or the trait is absent. The Chi-Squared test is the only test that works with the Categorical data for both the independent and dependent variables placed in a crosstable. It compares the Counts of subjects in each category set to the expected Counts.

8. None of the studies collected the amount, the data types, or the randomness of data needed to calculate a correlation or an association: †

Since Categorical data are used, a correlation is impossible. Correlations require Numerical data. An association is tested for when both independent and dependent variables are Categorical.

If they actually understood statistics, they would have known that a causal link cannot be proved by statistics alone, and that a correlation is impossible with Categorical data.

9. They tried to get Numerical data by taking a Count and "converting" it to a Numerical value. †

The trouble with this is that it reduces their sample size to one sample for the remainder of any calculations they do (they wrongly kept the old sample size).

10. Many of these groups got their math wrong:

In one study, they could not correctly do long division. 4/15 is .2666 - which rounds to 27 percent, not 26 percent.

In another study, they somehow got 64 percent, but no method of analysis of the Counts collected produces that value. They did not disclose the method of analysis.

11. They dismissed several theories on what causes homosexuality on the basis of "It can't be, therefore it isn't." They refused to believe they could be true because they aren't Politically Correct.
12. They used various methods of testing:

One study did not reveal whether they tested for the presence of DNA or a protein produced by the gene.

Other studies said they tested for the DNA.

Others said they tested for a protein produced by the gene (more accurate).

13. Most studies used expected values from social sciences instead of those for Mendel's genetic laws.
14. They released the results as though they had shown a causal relationship. They had neither any proof of a causal connection, nor proof of an association (correlations are impossible with Categorical data).
15. They claimed a causal relationship with percentages such as 27% or 64%. But a causal relationship should have percentages close to 100%, given the faulty data collection methods they used.
16. Attempts to duplicate the studies were unable to reproduce the results.
 † These were probably done because the government funding the researchers got was the amount provided to pay for the bare minimum number of samples needed for researching when using Numerical data. Many more samples are needed when using Categorical data. The number of tests varies, because totally random sampling must continue until each bin in the crosstable has the minimum number of samples.

## The Correct Way

Here is the correct way to do the experiment. It must not be altered. Any change in this procedure will introduce bad science into the procedure:

1. The scientists must not already have beliefs about the cause of homosexuality. If this cannot be achieved, then stop. The experiment must not be done with biased scientists.

One possible alternative method is to have one or more scientist with each kind of bias. They would work as checks and balances against any one bias affecting the outcome of the science.

2. The suspected cause must be the independent variable. The following are several suggested causes:
• An inherited gene
• Psychological problems
• Worshiping created things instead of God (The cause cited in the Bible - Romans 1:25-27)

(This can actually be tested for, because it generates valid categorical data.)

3. The trait of homosexuality must be the dependent variable.
4. A RANDOM sample of the US population must be taken. The sample must be large enough to have at least 40 people with the trait of homosexuality and 40 without it (to prevent making a wrong decision), but it must be randomly chosen. The sampling procedure must not in any way actively or inadvertently select for any trait.

This means the subjects must be randomly selected with a method obeying the following rules:

• The method of attracting subjects must not mention homosexuality, any of the tested causes, or any item in the question lists.
• Subjects must not be limited to one geographical area, political belief, or religious belief.
• If, after the subjects do the questionnaires, there are fewer than 40 homosexuals or 40 heterosexuals in the sample, the random sampling procedure shall be continued to gain more subjects.

In addition, if, after the cases are placed in the crosstable bins, any bin contains a Count between 1 ands 4, the random sampling procedure shall be continued to gain more subjects.

5. Give each subject an identification number. Do not identify the subject in any other way.
6. The subjects shall not be told what the experiment is for until after the study is complete. Instead, they should be told that the study is about something else, such as an actuarial study about healthcare prices or government programs. But do not use the subject of any of the test questions as a reason.
7. Collect the information only by subject number, not any name or other method of identification.
8. Build the questionnaire so that the purpose of the study cannot be determined by a test subject from the questions asked. Ask questions about the following:
• Has the subject had homosexual cravings?
• Has the subject had psychological problems?
• Does the subject have a strong religious belief? If so, what religion?
• Has the subject devoted much of his life to something or someone (other than as part of a job) or held any of these as an object of worship? Include on the list:
1. [Y] [N] The environment (1)
2. [Y] [N] A musical group (1)
3. [Y] [N] An automobile (1)
4. [Y] [N] A celebrity (1)
5. [Y] [N] Retirement plans (1)
6. [Y] [N] Government (1)
7. [Y] [N] A sports team (1)
8. [Y] [N] Jesus Christ (5)
9. [Y] [N] Money (1)
10. [Y] [N] The book "Origin of the Rolitarim" (4)
11. [Y] [N] Sexual gratification (3)
12. [Y] [N] Computers (1)
13. [Y] [N] Gourmet foods (1)
14. [Y] [N] UFOs or space aliens (1)
15. [Y] [N] The Tombs of Darlenoc (4)
16. [Y] [N] Literary works (1)
17. [Y] [N] God (5)
18. [Y] [N] Shoes (1)
19. [Y] [N] Gems or precious stones (1)
20. [Y] [N] Any heavenly body (1)
21. [Y] [N] The word "Splignoogle" (4)
22. [Y] [N] Movies (1)
23. [Y] [N] Anti-war activism (1)
24. [Y] [N] TV shows (1)
25. [Y] [N] A personal collection (1)
26. [Y] [N] The Virgin Mary (5)
27. [Y] [N] The arts (1)
28. [Y] [N] Gambling (2)
29. [Y] [N] Entertainment (1)
30. [Y] [N] Drugs (3)
31. [Y] [N] Precious metals (1)
32. [Y] [N] Any major religion (5)
33. [Y] [N] Fashion clothing (1).
34. [Y] [N] Anything else (1)

The subject shall NOT be shown the code number in parentheses after the items.

• Ask many other decoy questions to hide the purpose of the survey. Use questions about the purpose of the survey given to the subject. But all of the other questions must not contain any reference to homosexuality, sexual interests, or any items in the above list.
9. Take a DNA sample from each subject, identified only by subject number. Test the DNA for the gene (or genes) suspected of causing homosexuality.
10. Create a crosstable similar to the following one. Populate it with initial Counts (tallies) of zero (This example assumes 3 genes and two other causes were tested):
Crosstable Group 1Group 2Group 3 Group 4Group 5 Total
Gene #1Gene #2Gene #3 WorshipPsychol
YesNoYesNoYesNo YesNoYesNo
Heterosexual 00 00 00 00 00 0
Homosexual 00 00 00 00 00 0
Total 00 00 00 00 00 0
% Homosexual 00 00 00 00 00 0

#### These are not real data. This table exists only for demonstration.

Note that this is actually 5 crosstables put together, one group for each of the independent variables (top row). They are put together to prevent calculating the same value several times.

11. Use the questionnaire answers and DNA results to fill the table:

For each subject, choose one cell in the 2×2 area for each category and add one to the tally in that box:

1. Was any item with code number 4 marked YES?

If so, throw out all data from that subject and remove the subject from the pool. Collect another subject to replace the disqualified subject.

A Yes answer indicates that the subject is lying on the questionnaire to ruin the experiment. None of the items with code 4 actually exists.

2. Was any item with code number 1 or 3 marked YES?

If so, make Case A true, otherwise make Case A false.

These are cases where the person worships a created thing.

3. Was any item with code number 5 marked YES?

If so, make Case B true, otherwise make Case B false.

These are genuine religious beliefs.

4. Did the subject indicate any strong religious faith in Christianity, Judaism, or any other major religion?

If so, change Case B to true.

This includes the marked survey item on religious faith.

5. Was any item with code number 2 or 3 marked YES?

If so, make Case C true, otherwise make Case C false.

6. Did the subject indicate any psychological problem?

If so, change Case C to true.

This includes the marked survey item on psychological causes.

7. Is Case A true while Case B is false?

If so, make Case W true, otherwise make Case W false.

This is a test for worshiping created things.

8. Are Case A and Case B both true?

If so, make Case P true, otherwise make Case P false.

These are psychological indicators.

9. Is case C true?

If so, change Case P to true.

This is a psychological indicator.

10. For each of the columns for DNA tests:

Select the row on whether or not the person indicates he is homosexual.

Select the column on whether or not the gene is present.

Add 1 to the Count in the one cell where the selected row and column meet.

11. For the Worship created things column:

Select the row on whether or not the person indicates he is homosexual.

If Case W is true, select the Worship Yes column. Otherwise, select the Worship No column

Add 1 to the Count in the one cell where the selected row and column meet.

12. For the Psychol column:

Select the row on whether or not the person indicates he is homosexual.

If Case P is true, select the Psychol Yes column. Otherwise, select the Psychol No column

Add 1 to the Count in the one cell where the selected row and column meet.

Find the column sums, row sums, and the grand total.

Calculate the % Homosexual value by dividing the Homosexual Yes value in the column by the column sum.

The sample table with the data filled in now looks like this:

Crosstable Group 1Group 2Group 3 Group 4Group 5 Total
Gene #1Gene #2Gene #3 WorshipPsychol
YesNoYesNoYesNo YesNoYesNo
Heterosexual 27090 180180 3600 6 354 69291 360
Homosexual 3010 2020 040 37 3 1129 40
Total 300100 200200 36040 43 357 80320 400
% Homosexual 1010 1010 0100 861 149 10

#### These are not real data. This table exists only for demonstration.

These data were constructed solely to demonstrate five kinds of possible results, with one group showing each kind. They are not intended to show any desired result.

12. Test the table and collect more samples if necessary.

If any of the bins contain a nonzero value smaller than 5, then more samples must be taken.

In this case, since the value in one bin is 3, double the sample size. Collect 400 more samples. But do not increase the sample size if all deficient bins contain 0.

The new sample collection must have the same randomness as the collection taken above.

NEVER try to collect more for just that one bin. Doing this distorts the data, ruining the experiment.

The sample table with the new data added now looks like this:

These data were constructed solely to demonstrate five kinds of possible results, with one group showing each kind. They are not intended to show any desired result.

The color patches refer to calculations used below.

Crosstable Group 1Group 2Group 3 Group 4Group 5 Total
Gene #1Gene #2Gene #3 WorshipPsychol
YesNoYesNoYesNo YesNoYesNo
Heterosexual 540180 360360 7200     10     710 140580     720
Homosexual 6020 4040 080     74     6 2258     80
Total 600200 400400 72080     84     716 162638     800
% Homosexual 1010 1010 0100 881 149 10

#### These are not real data. This table exists only for demonstration.

13. Use the Chi-Squared distribution on the crosstable. Finds level of association between dependent and independent variables in each group.

Calculate the Chi-squared statistic. The procedure is:

1. Prepare an Expected Frequencies Table as follows:
• Copy the column sums, row sums, and grand total from the crosstable above.
• Put in each data cell the row sum for the cell's row times the column sum for the cell's column, divided by the grand total.

Here are the calculations. "Worship Created Things" was chosen to show the math with nonzero values:

From
Crosstable
Worship Total
YesNo
Heterosexual    720   *       84   /       800   =       75.6     720   *       716   /       800   =       644.4 720
Homosexual     80   *       84   /       800   =       8.4     80   *       716   /       800   =       71.6 80
Total 84716 800

Notice that the column and row sums still apply.

Here are the Expected Frequencies for the sample data above.

Expected
Frequencies
Table
Group 1Group 2Group 3 Group 4Group 5 Total
Gene #1Gene #2Gene #3 WorshipPsychol
YesNoYesNoYesNo YesNoYesNo
Heterosexual 540180 360360 64872     75.6     644.4 145.8574.2 720
Homosexual 6020 4040 728     8.4     71.6 16.263.8 80
Total 600200 400400 72080 84716 162638 800

#### These are not real data. This table exists only for demonstration.

2. Calculate the partial Chi-Squared value for each cell as follows:
• Subtract the Expected Frequencies Table value for the cell from the Crosstable Actual Frequency for that cell.
• Square the result.
• Divide the squared result by the Expected Frequencies Table value for that cell.

Here are the calculations. "Worship Created Things" was chosen to show the math with nonzero values:

Partial
Chi-Squared
Worship
YesNo
Heterosexual (    10  −     75.6) ^ 2 /     75.6 =     56.92 (    710  −     644.4) ^ 2 /     644.4 =     6.68
Homosexual (    74  −     8.4) ^ 2 /     8.4 =     512.30 (    6  −     71.6) ^ 2 /     71.6 =     60.10

The colors show where the values are obtained from in the Crosstable and the Expected Frequencies Table above and where the results are put in the Partial Chi-Squared Table below.

Here are the Partial Chi-Squared cell values for the sample data above.

Partial
Chi-Squared
Group 1Group 2Group 3 Group 4Group 5
Gene #1Gene #2Gene #3 WorshipPsychol
YesNoYesNoYesNo YesNoYesNo
Heterosexual 0.000.00 0.000.00 8.0072.00     56.92     6.68 0.230.06
Homosexual 0.000.00 0.000.00 72.00648.00     512.30     60.10 2.080.53

#### These are not real data. This table exists only for demonstration.

3. Calculate the Chi-Squared statistic for each group as follows:
• Add the Chi-Squared cell values for all of the cells in the group.

Here is the calculation. "Worship Created Things" was chosen to show the math with nonzero values:

56.92 + 6.68 + 512.30 + 60.10 = 636.00

Here are the final Chi-Squared Statistics for the sample data above.

Final
Chi-Squared
Group 1Group 2Group 3 Group 4Group 5
Gene #1Gene #2Gene #3 WorshipPsychol
Chi-Squared Statistic 0.000.00800.00636.002.90

#### These are not real data. This table exists only for demonstration.

4. An Alternate Method: Calculate Cramer's Coefficient for each group as follows:

Cramer's Coefficient converts the Chi-Squared value into a proportion or a probability.

Calculate Cramer's Coefficient from the final Chi-Squared values.

• Find the number of rows in the group in the Chi-Squared cell values table.
• Find the number of columns in the group in the Chi-Squared cell values table.
• Choose as j the smaller of the number of rows or the number of columns.
• Divide the final Chi-Squared Statistic for the group by the grand total of the crosstable.
• Divide the result by j-1.
• Take the square root of the result to get Cramer's Coefficient.

Here is the calculation. "Worship Created Things" was chosen to show the math with nonzero values:

√(636.00 / 800 / (2-1)) = 0.89

Here are the Cramer Coefficients for the sample data above.

Cramer's Coefficient Group 1Group 2Group 3 Group 4Group 5
Gene #1Gene #2Gene #3 WorshipPsychol
Cramer's Coefficient 0.000.001.000.890.06

#### These are not real data. This table exists only for demonstration.

14. Interpret the results using the Chi-Squared distribution. This procedure tests for the level of association between the dependent variable and the independent variable in each group.

The procedure is:

1. Decide the level of significance to be used for the tests:
• The usual choice is a 5%, (or .05) error level, which gives a 95% confidence.
• For more confidence, choose a 1%, or .01 error level, which gives a 99% confidence.
2. Find the number of degrees of freedom for each group in the table:
• Find the number of rows r in the group in the Chi-Squared table.
• Find the number of columns c in the group in the Chi-Squared table.
• Find the number of degrees of freedom by multiplying:
• d.f. = (r-1)(c-1)
3. Look up the Chi-Squared value for the selected level of significance and the degrees of freedom of the group. Use any of the following methods:
• Look up the Chi-Squared distribution in a table in a statistics book. The table is organized by level of significance and degrees of freedom.
• Use the Excel function CHISQ.INV.RT to get the value.

The value retrieved for .05 significance and 1 degree of freedom is 3.84.

Finish the Chi-Squared table with the significance level, and whether or not the Chi-Squared value exceeds it.

Chi-Squared Group 1Group 2Group 3 Group 4Group 5
Gene #1Gene #2Gene #3 WorshipPsychol
Chi-Squared Value 0.000.00800.00636.002.90
Chi-Squared distribution value 3.843.843.843.843.84
Is group significant? NONOYESYESNO

#### These are not real data. This table exists only for demonstration.

Alternately, use Cramer's Coefficient:

1. Use this table to interpret the strength of an association:
Cramer's Coefficient RangeMeaning
1.0 .. 0.8Strong Association
0.8 .. 0.5Fair Association
0.5 .. 0.25Weak Association
0.25 .. 0.0No Association
2. Here are the Cramer Coefficients and Associations for the sample data above.
Cramer's Coefficient Group 1Group 2Group 3 Group 4Group 5
Gene #1Gene #2Gene #3 WorshipPsychol
Association Strength 0.000.001.000.890.06
Case Association Level NoneNoneStrongStrongNone

## Recognizing the Cases

In the trial exercise above, 5 different cases of recognizing association are shown:

#### Group 1 Gene #1 - An Unassociated Dominant Gene

In this case, Chi-Squared and Cramer's Coefficient show no association.

Note that the gene is active in 75% of the cases, both homosexual and heterosexual.

Suspect another cause, because the gene is not associated.

Variations:

Unassociated Recessive Gene:
Note that the gene is active in 25% of the cases, both homosexual and heterosexual.

Types of tests:
If the test is for the product of the gene, the numbers above are correct.
If the test is for the gene code, the test numbers will be 75%, whether dominant or recessive. This kind of testing should be avoided if the gene is recessive,

#### Group 2 Gene #2 - An Unassociated Environmental Cause

In this case, Chi-Squared and Cramer's Coefficient show no association.

If both of the dependent values show the same percentage of the gene, but the percentages are not 75% - 25% or 25% - 75%, suspect an unassociated environmental value, a nonrandomly distributed gene, or a biased sample.

If both of the dependent values show the same percentage of 50% of the gene, an unassociated sex-chromosome gene might be the cause of the numbers.

#### Group 3 Gene #3 - Perfectly Negatively Associated Gene

In this case, Chi-Squared and Cramer's Coefficient show a strong association.

Since the presence of the gene is perfectly associated with heterosexuality, and the absence of the gene is perfectly associated with homosexuality, the gene probably prevents homosexuality. But without a causal analysis, this is only a negative association. Pursue more on this factor.

Variations:

A positive association would have the nonzero Counts in the Homosexual Yes and Heterosexual No cells.

#### Group 4 Worshiping Created Things - Strongly Positively Associated Factor

In this case, Chi-Squared and Cramer's Coefficient show a strong association.

Since the calculated Chi-Squared greatly exceeds the distribution table value, the association is very strong, but a few cases do not follow the association. Pursue more on this factor.

#### Group 5 Psychological - Not Significantly Associated Factor

In this case, Chi-Squared and Cramer's Coefficient show no association.

Since the calculated Chi-Squared value is less than the distribution table value, the association is weak, if not nonexistent. This factor is not interesting enough to pursue.

#### The above are not real data. This table exists only for demonstration.

The above data were constructed solely to demonstrate these five kinds of possible results, with one group showing each kind. They are not intended to represent any truth in this controversy.

## A Few Caveats

A few pitfalls to avoid in studies of this type:

• No statistical study can be strong enough to prove a causal relationship. Additional non-statistical science (e.g. looking for a chemical process) is needed for that. This is a common mistake often made by people who have not taken courses in physical or biological sciences, but have taken some courses in political or behavioral science. Those latter sciences have a lower standard of scientific rigor.
• The ONLY way that statistics can be used to prove a causal relationship is in an active case, where the independent variable can be varied in real time while collecting both the independent variable and the dependent variable. If the dependent variable follows every change in the independent variable, a causal connection can be proved statistically. But note that, since genes can't be varied over time in one individual, this method can't be used in this experiment.
• Why is this called an association, rather than a correlation? A correlation requires multiple Numerical values, one for each subject. But all we have here are logical (yes-no) values. They are Categorical (descriptive) values, not Numerical values. An association it is basically similar to a correlation, but has less weight because of the discrete nature of the data.
• Be careful not to cut corners to save money. This is one of the reasons the existing studies on this topic were so poor. They didn't have enough money to do a proper study. But nothing excuses the other instances of bad science in those studies.
• Be especially careful not to bias the randomness of the sample. Do not let any of these errors bias the sample:
1. Filling in a crosstable deficiency with a nonrandom sample
2. Sample filtering
3. Collecting data only in inner cities
4. Advertising for subjects in biased publications
5. Using voluntary responses to get subjects
6. Non-response of subjects
7. Telephone survey (biased by people not having local landline phones)
8. Seasonal bias (some subjects available at only certain times of the year)

Although bias cannot be totally eliminated, efforts to prevent bias or compensate for it can reduce the bias.

The page author would be interested in reading any study conducted the correct way. Go to his home page to find contact information.

## An Alternative Table Layout

In this case, one table is made for each possible cause. It is well suited to being used with Excel.

### Fill the Crosstable and prepare the Expected Frequencies Portion:

The sample table below is the same data as that contained in the sample table with new data above

These data were constructed solely to demonstrate possible results. They are not intended to show any desired result.

The color patches refer to the sources of the values used in the calculations.

Group 4
Worship
Crosstable Expected
Values
Partial
Chi Squared
Cramer's
Coeff
YesNoTotal YesNoTotal YesNoStat Stat
Heterosexual
10

710

720
720   *       84   /       800
=       75.6
720   *       716   /       800
=       644.4

720

Homosexual
74

6

80
80   *       84   /       800
=       8.4
80   *       716   /       800
=       71.6

80

Total   84   716   800 84 716 800

### Calculate the Partial Chi-Squared Portion:

Group 4
Worship
Crosstable Expected
Values
Partial
Chi Squared
Cramer's
Coeff
YesNoTotal YesNoTotal YesNoStat Stat
Heterosexual
10

710

720

75.6

644.4

720
(    10  −     75.6) ^ 2 /     75.6
=     56.92
(    710  −     644.4) ^ 2 /     644.4
=     6.68

Homosexual
74

6

80

8.4

71.6

80
(    74  −     8.4) ^ 2 /     8.4
=     512.30
(    6  −     71.6) ^ 2 /     71.6
=     60.10
Total   84   716   800 84 716 800

### Calculate the Chi-Squared Statistic:

Group 4
Worship
Crosstable Expected
Values
Partial
Chi Squared
Cramer's
Coeff
YesNoTotal YesNoTotal YesNoStat Stat
Heterosexual
10

710

720

75.6

644.4

720

56.92

6.68
56.92 +     6.68 +     512.30 +     60.10
=     636.00

Homosexual
74

6

80

8.4

71.6

80

512.30

60.10
Total   84   716   800 84 716 800

### Calculate Cramer's Coefficient:

Group 4
Worship
Crosstable Expected
Values
Partial
Chi Squared
Cramer's
Coeff
YesNoTotal YesNoTotal YesNoStat Stat
Heterosexual
10

710

720

75.6

644.4

720

56.92

6.68

636.00
√(    636.00 /     800 / (2-1)) = 0.89
Homosexual
74

6

80

8.4

71.6

80

512.30

60.10
Total   84   716   800 84 716 800

### Test for an Association:

Group 4
Worship
Crosstable Expected
Values
Partial
Chi Squared
Cramer's
Coeff
YesNoTotal YesNoTotal YesNoStat Stat
Heterosexual
10

710

720

75.6

644.4

720

56.92

6.68

636.00
> 3.84

0.89
Homosexual
74

6

80

8.4

71.6

80

512.30

60.10
Total   84   716   800 84 716 800     Assoc Strong