Downloading a Pew Dataset and Using SPSS to and
for Analysis: Part Two
Once you have completed recoding your variables, you will want to begin doing some basic univariate
analyses of your data. There are basically two main purposes for univariate analysis. The first is to
summarize the “central tendency” of some quality that has been explored in a survey, e.g., “the mean
income for survey respondents was $43.4.” Depending on what you are looking at, means, medians, and
modes are reported measures of central tendency. The other main purpose of univariate stats are to
show how a given quality is distributed, e.g., we could look at the standard deviation associated with our
survey’s mean income statistic to see whether the incomes most people tend to cluster around the
average income for the sample or if incomes are more broadly distributed. We could look at a statistic of
skewness to see if the distribution deviates from a bell curve (e.g. income is skewed towards…e.g. the
sharp tail points at…the wealthy in that lots of people are quite poor and comparatively few people are
rich). Since standard deviations statistics and measurements of skewness are difficult aren’t something
that most people are familiar with, we might choose to instead summarize the distribution and skew of
income in our sample by reporting means for people at each quartile, quintile, or decile.
Generating univariate statistics and charts if appropriate is something you can do from the syntax
window. If you are working with a previously saved syntax file, you will need to open it up and run the
whole thing to recreate your modified dataset (i.e., the one that has all of your recoded and computed
variables). Then, do the following:
1. Staying on your syntax page, select “Analyze” “Descriptive Statistics”  “Frequencies”.
2. Select the variable (or several if you want) that you are interested in analyzing and click the 
button.
3. In order to find out additional information on this variable, select “statistics” and then click on
“mean,” median,” or mode depending on what makes the most sense for what you want to know. If
you’ve created a “dummy variable” (coded 0-1) that identifies people who belong to a certain group,
selecting the mean option will allow you to see the percentage of respondents belonging to the
category. Click “continue” when finished in that dialogue box.
4. You may want to look at your data in a graph or chart; if so, select “charts” and your preference of
chart type, then click “continue.” If you are dealing with a variable that has nominal categories, bar
charts are usually the easiest to read. For variables that are continuous and have lots of values (age
in years or income for example), a line chart or histogram is your best option. Whatever chart you
do, you will make it more readable if you check the options to display your data in percentages
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rather than counts (e.g., if comparing men and women, you want to see percentages rather than the
number of respondents in each category).
5. Once you have finished selecting your various options, you can hit the OK button to run your
statistics. If you want to see the SPSS coding for these procedures, once again click “paste” and this
will paste the command code in the syntax file. Highlight the command code and once again select
“Run” and “Selection”…and presto…you will have your chart and frequencies. In all cases, carefully
at the results in your output file to make sure that something doesn’t look really strange as a
consequence of a variable coding mistake.
6. If for some reason, you want to produce univariate stats for just a subgroup or to see stats for two
groups, you can use the command DataSplit (checking the “compare groups” option). This
command would allow you see various statistics for just “Republicans” (as well as for the “NonRepublicans”) if you split your dataset on the dummy variable for partisanship that we created in the
example above). After your are done with your analyses using subgroups, you need to go back
through the split command to tell SPSS to resume analyzing “all cases.” An important note: If you
are comparing groups to see if there are statistically significant differences among them (e.g., trying
to see if Republicans are different than Democrats), you can’t use this procedure. To compare the
means between two groups or more, you need to use either a T-test or ANOVA procedure as
described below.
7. As you shut down SPSS, you may or may not want to save any syntax related to these procedures.
On the one hand, you might want to save your coding for these analyses somewhere so you can cut
and paste it into your syntax sheet to quickly use it for other variables you want to analyze later on
(When you will be doing pretty much the same thing with more than one variable, it will be much
quicker to cut and paste a command in the syntax file than to renegotiate a menu command and its
various dialog windows). On the other hand, if you are going to make further modifications to the
dataset based on what you found, it may make sense to not have this material cluttering up your
syntax file.
ANALYZING DATA IN SPSS: BIVARIATE ANALYSES
Most of you will want to see what, if any, statistically significant relationship exists between two
variables. What exactly do we mean by a statistically significant “relationship”? Regardless of which
specific “test of association” you choose use to generate a “coefficient of association,” you will ask SPSS
to do four things.
First, you will run some type of procedure to learn if two or more variables (that is, properties)
in your dataset are associated, which is to say tend to vary together, as if they were connected in some
way.
Second, if there appears to be some type of “covariance,” you want to know if this seeming
association is due purely to chance, which is to say that you want to know if you were to redo this
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survey 100 times in the same population at the same time, would you consistently (that is at least 95%
of the time) find that these two variables vary together.
Third, you want to know how “strong” the association is; in other words, how consistently do
the two variables co-vary.
Finally, if the variables have some internal order (e.g., the range in value from less to more), you
want to know “the direction of the relationship” by which we mean whether an increase in the value of
one the variables appears to co-vary positively or negatively with the value of the other variable.
Before you do anything below, the first quick step in bivariate analysis is to take a look at a correlation
matrix that includes all of the variables you are interested in. To do this, go to:
Analyzecorrelationbivariate correlation. Check just the option to return just the Pearson’s
correlation coefficient and the go to the left window and double-click on every variable in which you
are interested—including both dependent and independent variables. You will get quick and dirty view
of which variables are significantly correlated (i.e., those starred by SPSS or that carry a significance
statistic that is <.05). The Pearson’s measurement also gives you some idea of how strong a negative or
positive relationship is. If the coefficient is really small and insignificant, it is unlikely that the more
precise tests described below will add much information to your story.
1. To get more precise with bivariate analysis, you need to determine is what kind of variables you
will be working with. A quick review of the basic variable types:
 Categorical (aka) nominal variables are indicators where different values represent different
names or categories for something (e.g. gender, party identification, or religion). A dummy
variable is just a yes/no categorical variable that has been coded 0/1.
 Ordinal variables are indicators where different values represent a rank ordering of some sort
(e.g., a question that asks respondents if they “strongly agree, agree… strongly disagree).
 Interval variables have ordered values where any one-unit increase in the indicators value is
approximately equal to any other one-unit increase. Age is an example of this.
2. If you are looking to see if there is a relationship between two categorical/nominal variables, you
will use the crosstabs command: Analyze descriptive statistics cross-tabs. The most appropriate
statistical test for two variables of this type usually is lamda, so you will need to check this option by
opening the statistics window. The lamda test of association produces a statistic that ranges from
zero (no relationship) to one (completely associated). Under certain circumstances, lamda returns a
0 when there actually is a relationship between the two variables. If you get a zero for association,
use Cramer’s V instead. Keep in mind that the procedures for analyzing one or more nominal
variables do not provide any information about the direction of the relationship because these
variables’ coding (e.g., parties randomly assigned the numbers 1, 2, or 3) reflects names rather (e.g.,
Repub., Dem., Libertarian) rather than some underlying, ranked order.
Here’s a rough guide to use when analyzing the strength of categorical and ordinal coefficents of
association: Under .1 = very weak relationship; .1 to .2 = weak; .2-.29 moderate; anything above .4
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= strongly correlated unless we would expect the two variables to be more strongly linked.
3. If you are examining the relationship between a categorical and an ordinal variable, you still use
cross-tabs, but the appropriate measure of association is Cramer’s V. It also ranges between zero
and 1, and is interpreted like Lamda.
4. If both of your variables are ordinal, you will still use cross-tabs, but the appropriate measure of
association is usually Gamma (although researchers use several other measures interchangeably).
If your variables both happen to have the same number of ordered categories, the best test of
association is tau-b. These measures range from -1 to 0 or 0-1, depending on whether the two
variables have a negative or positive association (e.g., people who agree that schools should be
improved may also be more likely to agree that taxes should increase).
5. To test the association between a categorical and an interval variable, use crosstabs and use the
eta measurement.
6. Here’s a visual example of how to use cross-tabs:
When using the cross-tabs dialog window, it is customary to set up your table so that different
values of your dependent variable will be listed in the rows (drawing from the example above, let’s
say you want to see whether considering yourself to be a born-again Christian related to being a
Republican). Notice in the example below that the “cell display” options are checked for
“observed” counts and “percentage” column boxes:
Before, we look at the example, we will need to recode the “born” variable (Do you consider
yourself to be a born-again Christian) to deal with “don’t know” and/or missing responses:
RECODE born (9=SYSMIS) (2=0) (ELSE=Copy) INTO bornagain2.
VARIABLE LABELS bornagain2 'Consider yourself born again Christian'.
VALUE LABELS bornagain2 1 'Yes, born again' 0 'No'. EXECUTE.
EXECUTE.
7. Now we can use the cross-tab procedure:
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After pasting the syntax code into the syntax file and running that selection of code, here’s what we end
up with on our output screen:
First we see that Republicans in the survey were more likely say they are born-again:
Our output tells us that relationship between the two variables is statically significant (the Approx. Sig is
<.05, and in this case tells us that if we were to conduct this survey 1000 separate times, we would find
that being Republican and being Born-Again covary together and in the same way in every survey).
One of the cardinal rules of stats is that statistical significance is the not the same as substantive
importance. Here, the strength of the association between the two variables is not especially strong,
especially in light of the popular perception that most born-again Christian’s are Republican. Specifically,
the Cramer V statistic (we won’t rely on the Lamda coefficient because this is one of those
circumstances where it comes up zero), tells us that there are lots of Democrats who are born-again and
lots of Republicans who are not.
Symmetric Measures
Value
Nominal by Nominal
N of Valid Cases
Approx. Sig.
Phi
.132
.000
Cramer's V
.132
.000
2775
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How is correlation different from the other measures of association? In cases involving an interval
variable and another variable whose coding has a logical rank order, we will need to examine whether
there is a correlation between the two variables. Because of the number of responses categories
involved, we don’t want to use the cross-tabs procedure.
1. If you want to examine the direction and strength between an ordinal and an interval variable,
Spearman’s Rho is the appropriate test: AnalyzeCorrelateBivariate (check just the Spearman
option). This test’s output will return a “correlation coefficient” that ranges from -1 to 0 or 0 to 1,
depending on whether the association is positive (that is that an increase in one variable’s value
corresponds to an increase in the other) or not. SPSS also will provide a significance statistic
indicating whether the Rho correlation coefficient is statistically different from zero (pr= .05 means
that there is a 95% chance that the correlation is different from zero and that the +/- sign on the
coefficient is correct). If the sig. is >.05, we can’t be confident enough to say that there is a
relationship between the two variables that isn’t due to normally sampling variation.
2. If you want to examine the direction and strength between two interval variables, Pearson’s R is
the appropriate test: AnalyzeCorrelateBivariate (check just the Pearson’s option). It is
interpreted the same way as Spearman’s Rho.
Comparing the means of populations. Sometimes we want to see if different groups have statistically
different means for an interval or ordinal variable (e.g., Do Republicans, on average, make more or less
money than Democrats? Do they differ with respect to their average level of agreement with the
statement: you can trust government most of the time?).
1. If you are comparing means for just two groups that are coded as different values of the same
variables (for instance you want to compare men and women using a gender indicator that is coded
0,1), you will use the Independent Samples T test: AnalyzeCompare MeansInd. Samples T Test.
The interval/ordinal variable goes into the window marked “dependent variable.” If your categorical
variable happens to contain three or more subgroups (e.g., Democrats =1, Republicans=2, and
others=3), you can still compare the means for just two of them; you just need to use the “Define
groups” option indicate which two values of the variable should be compared (e.g., groups 1 and 2 if
you want to compare the means for Dems and Repubs).
2. If you want to compare means using categorical variable that identifies more than three groups,
you have two options. First, you can use the Anova command to examine whether every group’s
mean is different from all of the others’ means (e.g., test whether or not the mean level of support
for government among Libertarians, Democrats, and Republicans is different from each and all of
the other groups). The appropriate test for this situation is: AnalyzeCompare MeansOne-way
Anova (The “factor” variable is your categorical variable; the dependent variable is your interval
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variable). Keep in mind that Anova will return a statistically insignificant “F-statistic” if any group’s
mean is not statistically distinguishable from any one the other groups, which may well happen if
one of the groups has a small sample size and thus a large margin of error.
A second option to compare the means of several subgroups is to use the independent Sample-T
test and to just change out the groups being compared. Thus if you have a variable where you want
to compare the means of subgroups 1,2,3. You would run three separate T-tests with different
“define groups” settings: 1 and 2, 2 and 3, 1 and 3. Although slightly more work, this is the
preferred procedure when comparing multiple means.
ANALYZING DATA IN SPSS: MULTIVARIATE ANALYSIS
Many of you will not be satisfied with using your research project to explore how just one or two
variables impacts a given outcome. One way to look at associations while adding an additional control
variable or two is to use the “layers” options in the cross-tabs procedure (e.g., you could compare men
and women with respect to the existence, strength, and significance of a relationship between being a
born again Christian and being Republican). While this is nice for charts, most social scientists take
things a step further.
Have you ever wondered why most studies reported in political science, natural science, and even
medical journals use “regression” models even though most Americans have no idea what these
procedures entail or how to interpret their output? Most social science research today uses somewhat
more sophisticated methods than crosstabs for two reasons. First we often want to consider how
changes in each one of many different independent variables will an outcome in which we are
interested. Frankly, life is complicated, and controlling for more variables makes it clearer how our
variables of interest shape the outcome we are studying. So, rather than looking at how race impacts
voting, how gender impacts voting, how education impacts voting, etc., social scientists instead
frequently want to know how differences in race, gender, and education all interact to impact voting.
Second “regression” analytical techniques also allow us to answer the question of whether there is a
relationship between an independent variable of special interest and our dependent variable/s, once
other relevant factors are taken into account.
As an example of how we can use regression models to answer a question that would be impossible to
get at with bivariate methods, consider a study that wants to know if Mexican Americans vote at the
same rate as other Americans. Let’s assume that previous research has suggested that 1) Mexican
Americans vote in presidential elections at lower rates when we just compare mean rates of turnout, 2)
that Mexican Americans, on average, have lower socioeconomic indicators, and 3) that people with
lower socioeconomic indicators vote at lower rates. Thus our research thus might want to see whether
Mexicans still vote at lower rate than other Americans once we “take into account,” that is “control” for
various indicators of socio-economics that impact voting. In other words, we want to know whether
there is something about being a Mexican American leads to lower voting rather than other qualities
that disproportionately apply to this group. To answer this question, we can use a statistical program
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like SPSS to work out all of the permutations necessary to compare the voter turnout of Mexicans and
other Americans across varying levels of income, educational attainment, occupational status, etc. Our
results might allow us to say something like, “while the odds that the typical Mexican-American will vote
in a presidential election is approximately half those of other Americans, once the effects on voting of
the socio-economic differences between the two groups are taken into account, the odds that a nonMexican will vote in a presidential election is only 20 percent higher than the odds for a MexicanAmerican.”
Why most of you will want to use the same specific type of regression: Logistic regression. With the
type of datasets you will be working with, the most straight-forward way to analyze the impact of
several variables on a phenomenon is to use or to make a dependent variable that is dichotomous
(more specifically, to use a variable constructed in a yes/no format). This type variable allows the use of
logistic regression: Analyze-> Regression->Binary logistic to explore how variations in a given
dependent variable increase or decrease the odds that our outcome of interest will occur. If you are
incredibly fortunate and your dependent variable of interest is an interval variable with a wide range of
values, you will be able to use the most easy-to-interpret type of regression: ordinary least squares. If,
however, you have a dependent variable that takes the form of a count (e.g., how many times have you
voted in the last year?) or an ordinal ranking (e.g., do you support the torture of suspected terrorists
“often,” “some of the time,” “rarely,” or “never”?) the interpretation of the appropriate regression
model is sufficiently complicated that we will ask you to convert your dependent variable into a
dichotomous variable so that you can use logistic regression.
In the example dataset we will be working with in class, we being our work by recoding the question
related to torture: Do you think the use of torture against suspected terrorists in order to gain
important information can often be justified, sometimes be justified, rarely be justified, or never be
justified? Instead, we will ask: Who supports the regular use of torture on terrorism suspects? By
supports, we mean who told Pew’s surveyors that torture is either sometimes or often justified (=1) and
who didn’t (all other responses = 0).
After recoding several of independent variables we think may be relevant to explain levels of support,
we can use logistic regression see how the various factors impact the likelihood that a given person will
support the widespread use of torture on terrorism suspects. Specifically, in the model whose results
are pasted below, we use logistic regression to ask how:





being a born-again Christian,
being more wealthy (as measured by income ranging across 9 increasing brackets),
having more education (as measured across 7 increasing levels),
being male, and
being Republican
impact the likelihood that a respondent will say that the use of torture at least “sometimes” justified.
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Here’s the output that we would get back from SPSS:
At the very end of the output screen (SPSS reports a lot of output before it gets to the important part,
so you should ignore that part of the output), we see the table “the Model Summary.” Here, we learn in
this case that our variables collectively don’t explain very much about what makes people likely to
support terrorism at least sometimes. In fact, the normal way to read these two versions of a “pseudo Rsquared statistic” is to say that this regression model “explains” about 4 percent of variance in whether
or not someone will support torture. Put another way, even after we know your gender, income,
political party, educational attainment and whether or not you consider yourself to be a born-again
Christian, we still know very little about whether or not you are likely to support the torture of
suspected terrorists.
Model Summary
Step
Cox & Snell R
Nagelkerke R
Square
Square
-2 Log likelihood
1
615.626
a
.035
.048
a. Estimation terminated at iteration number 3 because
parameter estimates changed by less than .001.
Despite the model’s “poor fit” (e.g., it model doesn’t include most of the factors that impact support for
torture), we can still get quite a lot of information out of the procedure. Specifically, we determine the
influence of any one of the variables, by itself, once we hold the influence of the other variables
constant:
Variables in the Equation
B
Step
1a
S.E.
Wald
df
Sig.
Exp(B)
BornAgain2
.165
.195
.718
1
.397
1.179
income
.036
.049
.526
1
.468
1.036
educ
-.082
.070
1.385
1
.239
.921
Male
.251
.194
1.670
1
.196
1.286
Republican
.720
.215
11.213
1
.001
2.055
Constant
.013
.343
.001
1
.969
1.013
a. Variable(s) entered on step 1: BornAgain2, income, educ, Male, Republican.
The only statistics that you want to pay attention to in this part of the output are in the significance and
Exp(B) columns. Here’s what the results say:
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
Of all the variables in the model, being a Republican is the only indicator that we say with
certainty has an influence on supporting torture because the other variables are not statistically
significant (i.e., it is the only one where the significance is less than .05).

Holding the other variables constant, the model “predicts” that being a Republican increases the
odds of believing that the torture of terrorism suspects is justified at least sometimes by a little
over 200 percent.

If the education variable were significant—which it is not— we would interpret the results in the
following way: “When all other variables are held constant, every one unit increase in
educational attainment on Pew’s seven-level index corresponds to an 8 percent reduction in the
odds that a respondent believes that the torture of terrorism suspects is justified at least
sometimes.”
CONCLUDING COMMENTS:
We know that we have given you a lot of information in this document (keep in mind that you will
probably only use a fraction of the methods covered, depending on what kinds of variables you use in
your project). This isn’t supposed to make sense with no further explanation. Instead we are looking
forward to working with each of you individually, and hope this material will get you off to a solid start.
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