Here's how to compute your tables with SPSS for class assignments. There are other ways to do it, but this will give you guidance on one good way to do it.

Crosstabs

Put the independent variable (Gender) in the Columns, and put the dependent variable ("Afraid to walk alone") in the Rows.
In the "cells" subcommand, tell SPSS that you want Observed Counts and Column Percentages.
In the "statistics" subcommand, tell SPSS that you want Chi-square; then -
- If your data are ordinal and/or dichotomous (2-category, including dummy variables), as they are here, tell SPSS that you want Gamma or another ordinal statistic. This will be most common.
- If your data are nominal, choose Phi/Cramer's V or another nominal statistic. You won't use this as much.

**V6XR Afraid to walk alone at night (R) * V2R Gender (R) Crosstabulation**
			V2R Gender (R)		Total
			1 Female	2 Male	Total
V6XR Afraid to walk alone at night (R)	1 No	Count	80	119	199
	1 No	% within V2R Gender (R)	36.7%	63.3%	49.0%
	2 Yes	Count	138	69	207
	2 Yes	% within V2R Gender (R)	63.3%	36.7%	51.0%
Total		Count	218	188	406
Total		% within V2R Gender (R)	100.0%	100.0%	100.0%

When we set things up this way, we can say that 63.3% of women are afraid to walk alone at night, compared to 36.7% of men. That is, women are much more afraid of this than men (26.6 percentage points more so).

**Chi-Square Tests**
	Value	df	Asymp. Sig. (2-sided)	Exact Sig. (2-sided)	Exact Sig. (1-sided)
Pearson Chi-Square	28.583(b)	1	.000
Continuity Correction(a)	27.528	1	.000
Likelihood Ratio	28.920	1	.000
Fisher's Exact Test				.000	.000
Linear-by-Linear Association	28.512	1	.000
N of Valid Cases	406
a Computed only for a 2x2 table
b 0 cells (.0%) have expected count less than 5. The minimum expected count is 92.15.

The Chi-square tests simply measure whether anything is "going on" in the table. You'll learn more about Chi-square in your statistics classes. All we need to look at here are the significance ("Sig.") measures. If they are very low, as they are here (.000), we say that Chi-square is significant.

The usual "cut points" for significance levels are as follows:

Less than .01 - Highly significant
Between .01 and .05 - Moderately significant
Between .05 and .10 - Somewhat significant
Above .10 - Not significant

This is true for any statistic you use (not just Chi-square).

**Symmetric Measures**
		Value	Asymp. Std. Error(a)	Approx. T(b)	Approx. Sig.
Ordinal by Ordinal	Gamma	-.497	.078	-5.540	.000
N of Valid Cases		406
a Not assuming the null hypothesis.
b Using the asymptotic standard error assuming the null hypothesis.

The Gamma statistic shows whether there is a relationship between two ordinal variables, as is the case here. Gamma here is -.497 ("Value"), which is quite strong, and it is highly significant (.000) ("Approx Sig."). Thus, for a strong relationship, the Value is high (absolute value), and the Significance is low. Significance levels work the same here as they do for Chi-square. Gamma ranges between +1.0 and -1.0. Zero means no relationship, and the higher the absolute value of gamma (the closer to +1.0 or -1.0), the stronger the relationship.

The sign (+/-) simply shows which way the correlation goes: positive means that the main diagonal of the table is dominant (upper left cell to lower right), and negative means that the that the off-diagonal of the table is dominant (upper right cell to lower left). Since gamma is negative here, it means that women are more afraid to walk alone at night (you'll see this in the lower left cell of the table).

The strength of gamma, in absolute values, is roughly as follows:

Less than .1 - Quite weak (probably insignificant)
About .1-.2 - Slight relationship
About .2-.3 - Moderate relationship
About .4-.5 - Strong relationship
.5-.6 and higher - Very strong relationship

These rules of thumb don't necessarily apply to other statistics.

Crosstabs

**V3R Trust people (R) * V2R Gender (R) Crosstabulation**
			V2R Gender (R)		Total
			1 Female	2 Male	Total
V3R Trust people (R)	1 Can't be too careful	Count	134	90	224
	1 Can't be too careful	% within V2R Gender (R)	62.0%	47.9%	55.4%
	2 Other, depends (Volunteered)	Count	17	29	46
	2 Other, depends (Volunteered)	% within V2R Gender (R)	7.9%	15.4%	11.4%
	3 Most people can be trusted	Count	65	69	134
	3 Most people can be trusted	% within V2R Gender (R)	30.1%	36.7%	33.2%
Total		Count	216	188	404
Total		% within V2R Gender (R)	100.0%	100.0%	100.0%

In this table, notice that the dependent variable ("Trust people") has three categories, and that they are in order (the variable is ordinal). When rows and columns are set up this way, we can say that 62.0% of women say you can't be too careful, compared to 47.9% of men. That is, women are somewhat more distrusting than men (14.1 percentage points more so).

**Chi-Square Tests**
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	10.000(a)	2	.007
Likelihood Ratio	10.044	2	.007
Linear-by-Linear Association	5.173	1	.023
N of Valid Cases	404
a 0 cells (.0%) have expected count less than 5. The minimum expected count is 21.41.

The Chi-square here is highly significant (though not as much so as in the previous table). Thus, there is some relationship between the variables.

**Symmetric Measures**
		Value	Asymp. Std. Error(a)	Approx. T(b)	Approx. Sig.
Ordinal by Ordinal	Gamma	.214	.086	2.436	.015
N of Valid Cases		404
a Not assuming the null hypothesis.
b Using the asymptotic standard error assuming the null hypothesis.

Gamma is moderate at .214 and is significant at .015. We can conclude that women are somewhat less trusting of other people than are men. Because gamma is positive, we should look in the main diagonal of the table (upper left to lower right cells) to characterize the relationship.