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SPSS 2: Data analysisBy Wendiann Sethi
Spring 2011
The second stages of using SPSS is data analysis. We will review descriptive statistics and then move onto other methods of data exploration using crosstabulations, inferences on the mean, regression and ANOVA. Students are encouraged to bring data that they are analyzing in class or projects to discuss what methods would be best to use.
Course description:
Descriptive statistics Identifying outliers Missing data – by variable, by respondent Manipulating data –
◦ reversing the scale◦ Collapsing a continuous variable into groups
Choosing the right statistic Data Analysis:
◦ Cross tabulations◦ Correlations and regression◦ Tests about mean and proportions◦ ANOVA
Multiple Responses
Course objectives
Measures of central tendency Measures of spread
Analyze> Descriptive Statistics
What do you use for each type of variable and why?
Descriptive Statistics
Look at the mean, median, st.dev and skewness to determine if there might be outliers
Could also use a box plot to see
Outliers
Two potential problems with missing data:1. Large amount of missing data – number of
valid cases decreases – drops the statistical power
2. Nonrandom missing data – either related to respondent characteristics and/or to respondent attitudes – may create a bias
Missing Data revisited
Missing Data Analysis
Examine missing data
By variableBy
respondentBy analysis
If no problem found, go directly to your analysisIf a problem is found:
Delete the cases with missing dataTry to estimate the value of the missing data
Use Analyze > Descriptive Statistics > Frequencies
Look at the frequency tables to see how much missing
If the amount is more than 5%, there is too much. Need analyze further.
Amount of missing data by variable
1. Use transform>count2. Create NMISS in the target variable3. Pick a set of variables that have more
missing data4. Click on define values5. Click on system- or user-missing6. Click add7. Click continue and then ok8. Use the frequency table to show you the
results of NMISS
Missing data by respondent
Use Analyze>descriptive statistics>crosstabs
Look to see if there is a correlation between NMISS (row) and another variable (column)
Use column percents to view the % of missing for the value of the variable
Missing data patterns
Proceed anyway Estimate (impute) the missing data with
substituting the mean or median value
What to do about the missing data?
Recoding Calculating When to create a new variable versus
creating a new one.
Manipulating data
What do you want to explore?
What do you need in your data to do that exploration?
Choosing the right statistic
Analyze>Descriptive Statistics>Crosstab
Good for categorical data to see the relationship between two or more variables
Statistics: correlation, Chi Square, association
Cells: Percentages – row or column
Cluster bar charts
Crosstabulation
Finding the relationship between two scale or ordinal variables.
Analyze > Correlate > bivariate
Analyze > regression > linear
Correlation and regression
Aim: find out whether a relationship exists and determining its magnitude and direction
Two correlation coefficients:◦ Pearson product moment correlation coefficient –r- interval
or ratio scale variables◦ Spearman rank order correlation coefficient –rho- ordered
or ranked data Assumptions:
◦ Related pairs of scores◦ Relationship of the variables is linear◦ Variables are measured at least at the ordinal level◦ Homoscedasticity – variability of y variable should remain
constant at all values of x variable
Correlation
Aim: Use after finding there is a correlation to find an appropriate Linear model to predict the results of the DV based on one or more IV’s
Assumptions:◦ Related pairs of scores◦ Relationship of the variables is linear◦ Variables are measured at least at the ordinal level◦ Homoscedasticity – variability of y variable should remain constant
at all values of x variable Procedure: Linear Regression
◦ One IV to one DV◦ ANALYZE>REGRESSION>LINEAR◦ After placing the appropriate DV and IV, click STATISTICS◦ Click CONTINUE and then OK to run the analysis
Regression
Comparing the means of a scale (or ordinal) when grouped by a category
Analyze > Compare means◦ Means – simplest form DV – scale to be compared given the IV –
categories◦ One-sample t-test : test the mean of the variable against a set
value.◦ Independent samples t-test: looking at the difference of two
means of the variable given a grouping variable (two-groups only)
◦ Paired-samples t-test: looking at the difference of the means when there is paired data (pre-test vs post-test)
◦ One-way ANOVA: comparing the means of dependent variables (scale or ordinal) given a factor (one IV-category)
Comparing means
Aim: Testing the differences between the means of two independent samples or groups
Requirements:◦ Only one independent (grouping) variable IV (ex. Gender)◦ Only two levels for that IV (ex. Male or Female)◦ Only one dependent variable (DV)
Assumptions:◦ Sampling distribution of the difference between the means is normally
distributed◦ Homogeneity of variances – Tested by Levene’s Test for Equality of
Variances Procedure:
◦ ANALYZE>COMPARE MEANS>INDEPENDENT SAMPLES T-TEST◦ Test variable – DV◦ Grouping variable – IV◦ DEFINE GROUPS (need to remember your coding of the IV)◦ Can also divide a range by using a cut point
T-test for independent groups
Aim:used in repeated measures or correlated groups designs, each subject is tested twice on the same variable, also matched pairs
Requirements:◦ Looking at two sets of data – (ex. pre-test vs. post-test)◦ Two sets of data must be obtained from the same subjects
or from two matched groups of subjects Assumptions:
◦ Sampling distribution of the means is normally distributed◦ Sampling distribution of the difference scores should be
normally distributed Procedure:
◦ ANALYZE>COMPARE MEANS>PAIRED SAMPLES T-TEST
Paired Samples T-test
Aim: looks at the means from several independent groups, extension of the independent sample t-test
Requirements:◦ Only one IV◦ More than two levels for that IV◦ Only one DV
Assumptions:◦ The populations that the sample are drawn are normally
distributed◦ Homogeneity of variances◦ Observations are all independent of one another
Procedure:ANALYZE>COMPARE MEANS>One-Way ANOVA Dependent List – DV Factor – IV
One-way Analysis of Variance
How to deal with questions were the participant can choose several choices.
ANALYZE>MULTIPLE RESPONSE◦ Define sets◦ Frequencies◦ Crosstabs
Example data: survey_sample.sav◦ Eth1, 2, 3 – multiple response method◦ News 1, 2, 3 – multiple dichotomy method
Multiple responses
