spss_cross tabs interpretation

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Introduction to SPSS POL S 354 Barreto

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Step 1: Open Dataset Click File > Open > Data

1.1: Browse for the file you want to open (LosAngeles2005.sav)

Data will open in variable view as seen here:

1.2: If your screen does not look exactly like this, make sure you are on variable view by clickingthe tab Variable View in the bottom left corner of the screen

1.3: Under the column header Name at the top left hand side of the screen, you will find the

short, abbreviated variable name for each question, or variable on the survey.

1.4: Under the column header Label at the top of the screen, you will find the questionwording for each question on the survey

1.5: Under the column header Values at the top of the screen, you will find the possible

answers to each question on the survey in the form of both numbers and text. The dataset itselfis nothing more than 0s 1s 2s 3s etc. and for each possible numeric answer you will find the

description of what that number stands for in the values section


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Step 2: Analyze the data frequency results

1.1: To get started, click on the Analyze menu header. This is where you will always click toconduct any sort of data analysis, so get familiar with this menu option

1.2: To conduct a simple frequency response of a questionClick Analyze > Descriptive Statistics > Frequencies

1.3: A new window titled Frequencies will pop up on your screen:

Select a variable/question from the list on the left-handside that you want to know the results for.

Then click the arrow in the middle (pointing right) so thatyour variable/question now appears on the right-hand

side.

Click the OK button


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Step 3: Interpreting the results frequencies

3.1: After you click OK a new window will open with your results (as seen here)

3.2: Scroll down the bold-face header, Frequency Table

3.3: Above the table you will see the question wording How vote for mayor

In the first column on the left, you will see the possible answers The second column, frequency contains the raw number results for how many people

gave each answer. The third column, percent is the percentage that each answer received out of the total The fourth column, valid percent is most likely the column we are interested in.

people who did

not answer

answers to the

question

3.4: Valid Percent For any given question on a survey, some people will not respond. This canvary from a small handful (say 3 percent) to quite a large number (say 30 percent). The fourth

column, valid percent, excludes all the people who are missing because they did not answer

the question and tells you, among those people who answered, what the percentages are.


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Step 4: Analyzing the data crosstabs

4.1: In order to look for patterns in the data, most social scientists start by dissecting thefrequency results into different groupings, called crosstabulations. An easy way to think of this

is the difference in response by men and women to each question on your survey

4.2: To conduct a crosstab, click Analyze > Descriptive Statistics > Crosstabs

4.3: A new window will appear titled Crosstabs that looks like the Frequencies window with

a few more options

The key to running a successful crosstab is understanding

the difference between rows and columns.

The row is the variable/question that you are interested in

finding out the answer to, such as How vote for mayor

The column is the variable/question that you are dividing

your sample by, such as gender, age group, income, race.

4.4: From the list on the left-hand side, select a variable/question you want to know the answer toand click the top arrow to send it into the Row(s) box

4.5: From the list on the left-hand side, select a variable/question you want to divide or groupyour responses by and click the middle arrow to send it into the Column(s) box

4.6: If you are interested in knowing how the responses breakdown by two or three differentgroups, you can select multiple variables into the Column(s) box such as age, gender, race.


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Step 4: Analyzing the data crosstabs

4.7 Once you have selected the variables you want to analyze, click on Cells at the bottom anda new window will appear. Check the box next to column in the section, Percentages

Click Cells and check the box for

column in the section, Percentages

variable(s) that you want to divide

or group the answers by you may

select more than one

variable/question that you are

interested in knowing the answer to

4.8: Click OK to run the crosstabs

Step 5: Interpreting the results crosstabs

5.1: Just as with the frequencies, after you click OK a new window with results will appear

5.2: You can ignore

the first box at the top

labeled CaseProcessing Summary

and scroll down to the

next box which

contains your results

5.3: Above the box,

you will see thevariable/question

names for your

crosstabulation check to make sure

the correct variables

were included


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sizes, you have less reliable results and you should refer to the margin of error box on the nextpage.

Step 5: Interpreting the results crosstabs

5.4: When you have selected column percentages (which we did in step 4.7) you will read a

crosstab column by column or from top to bottom

In the example above, the crosstab tells us that among Men, 35.9% voted for Hahn and 64.1%

for Villaraigosa and that among Women, 34.2% voted for Hahn and 65.8% for Villaraigosa.

Thus, we can conclude from this crosstab that no major gender gap existed in this election.

For the second crosstab (below), the results by race, we see a different pattern. Again, we read

the crosstab by starting with each column grouping and read down. Among Whites, 59.7% voted

for Villaraigosa while among Latinos 86.3% were for Villaraigosa and among Asian Americansonly 42.4% supported Villaraigosa, and finally 59.0% of Blacks voted for Villaraigosa. These

results inform us that there were clear voting differences by race and ethnicity.

5.5: Finally, at the very bottom of the crosstab box, you should note the number listed forCount. This represents the total number of people in the survey that belong to each column

group such as White, Latino, Male, Female, etc. depending on how you have grouped the results.

5.6: The count, or sample size, also tells you what the marin of error is for each group within

your data analysis. As the count gets smaller, the margin of error gets larger. With small sample


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tep 6: What is the margin of error?

centage result for any of your analysis. The percentagested on the results window, is only an ESTIMATE of what the real answer would have been if

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6.1: Be careful when you look at the perlievery single person had been interviewed. Because we only have a sample, we need to be aware

of the margin of error for our data.

6.2: Margin of error for sample sizes in survey

MoE Count MoECount MoE Count

2,500 1.96% 400 4.90% 100 9.80%

2,000 2.19% 300 5.66% 90 10.33%

1,750 2.34% 250 6.20% 80 10.96%

1,500 2.53% 200 6.93% 70 11.71%

1,250 2.77% 175 7.41% 60 12.65%1,000 3.10% 150 8.00% 50 13.86%

900 3.27% 140 8.28% 40 15.50%

800 3.46% 130 8.60% 30 17.89%

700 3.70% 120 8.95% 20 21.91%

600 4.00% 110 9.34% 10 30.99%

500 4.38% 100 9.80% 0 100.00%

.3: When you are writing up your results, making tables or charts, it is very important that you

ut a footnote for the margin of error for each of your crosstab groups so that we know if your

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ted

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findings are real for example, if you had 100 Asian Americans in your sample and 100Blacks, the margin of error on the percentage estimates would be +/- 9.8 for each group. If you

crosstab results showed that 60% of Asians voted yes on a measure and 52% of Blacks vo

yes the difference of 8 points is WITHIN the margin of error so you can not be certain that the

difference actually exists. USE EXTREME CAUTION WITH SMALL SAMPLES!

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