Intermediate SPSS. If you have an SPSS dataset (*.sav), you can open it in the following way:

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1 Center for Teaching, Research & Learning Research Support Group at the Social Science Research lab American University, Washington, D.C Intermediate SPSS Course Objective The goal of this course is to give students who are already familiar with SPSS a basic understanding of SPSS syntax and more advanced features in interactive mode. Learning Outcomes 1. Opening datasets that are in Excel, SPSS, and other formats 2. Creating datasets 3. Running descriptive statistics, frequencies, crosstabs, and correlations 4. Transforming variables using recode and compute 5. Splitting Files 6. Selecting Cases 7. Hypothesis testing: T-Test and Regression 8. Merging datasets 9. Exporting outputs 1. Opening a dataset If you have an SPSS dataset (*.sav), you can open it in the following way: - Select the File > Open > Data. A dialog box pops up. - Browse for your dataset and open it. Ex: World 95 for Missing Values.sav (Let s use this data for the rest of tutorial.)

2 GET FILE='C:\Program Files\SPSSInc\PASWStatistics18\Samples\English\World 95 for Missing Values.sav'. DATASET NAME DataSet1 WINDOW=FRONT. If you have a dataset which is not in SPSS data format, it is easy to open it in SPSS: - Select the File > Open > Data. A dialog box pops up. In the line that specifies Files of Type, change the file type from SPSS to all files. - Browse for your dataset and open it. Ex: demo.xls GET DATA/TYPE=XLS /FILE='C:\Program Files\SPSSInc\PASWStatistics18\Samples\English\demo.xls' /SHEET=name 'demo'/cellrange=full/readnames=on/assumedstrwidth= DATASET NAME DataSet1 WINDOW=FRONT. 2. Creating a dataset You can also create a dataset from scratch in the Data Editor: - Go to the Variable View sheet and specify your variables. - Return to the Data View sheet and enter your data, one case per line. - Save your data by selecting the File pull-down menu and using the Save option. - For more detailed instructions, please see the handout for Introduction to SPSS. 3. Running descriptive statistics and frequencies a. Descriptive statistics - Go to Analyze > Descriptive Statistics > Descriptives. - Select the variables for which you want descriptives. Ex) aids and fertility - Click on the Options button and check any boxes you want to apply such as Variance, Skewness, or Descending means. DESCRIPTIVES VARIABLES=fertility aids /STATISTICS=MEAN STDDEV MIN MAX. b. Frequencies - Go to Analyze > Descriptive Statistics > Frequencies. - Select the variables for which you want the frequencies. Ex) predominant climate and people who read - Click on the Statistics button, check any boxes you want to apply such as Mode or Mean, and then click on the Continue button.

3 - Click on the Charts button, check a graph type you want to apply such as Histogram (let s also add a normal distribution curve by checking the box) and then click on the Continue button. - Click on the Format button, check any options you want to apply such as Descending Values, and then click on the Continue button. FREQUENCIES VARIABLES=literacy climate /STATISTICS=MEAN MODE/HISTOGRAM NORMAL/FORMAT=DVALUE/ORDER=ANALYSIS. c. Cross tabulation - Go to Analyze > Descriptive Statistics > Crosstabs. - Select the variables for which you want the crosstabs. Ex) predominant religion in row and predominant climate in column. - Click on the Statistics, check options such as Chi-square, and click Continue. - Click on the Cells button, check options like Expected and Percentage > Total, and click Continue. CROSSTABS/TABLES=religion BY climate/format=avalue TABLES/STATISTICS=CHISQ /CELLS=EXPECTED TOTAL/COUNT ROUND CELL. d. Correlations - Go to Analyze > Correlate. - Select Bivariate, if you are interested only in the relationships between two variables, or Partial, if you are measuring the association between two variables but want to factor out the effect of other variables. - Select the variables for which you want the correlation. Ex) Gross domestic product and people who read CORRELATIONS /VARIABLES=gdp_cap literacy /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE 4. Transforming Variables a. Recoding Data The Recode option allows you to change the coding of variables and create discrete categories from continuous variables. - Transform > Recode into different variables. Select GDP per capita for the Numeric variable box.

4 - Click Old and New Values. Select Range: LOWEST through box. Enter In the New values, enter 1. Click Add. Select Range: value through HIGHEST box. Enter In the new values, enter 0. Click Add. Click Continue. In Output Variable, give the name as lowincome and the label as country has per capita income less than 10,000. Click Change. Click OK. RECODE gdp_cap (Lowest thru 10000=1) (10001 thru Highest=0) INTO lowincome. VARIABLE LABELS lowincome 'country has per capita income less than 10,000'. EXECUTE. b. Computing Data The Compute option allows you to arithmetically combine or alter variables and place the resulting value under a new variable name. Transform > Compute variable. - Give a new variable name, for example, lngdp under Target Variable. - In the Function Group box, select Arithmetic by double clicking on it. - In the Functions and Special Variables box, select Ln by double clicking on it. - Highlight Gross Domestic Product and put it in the expression box by clicking the arrow. - Click OK COMPUTE lngdp=ln(gdp_cap). EXECUTE. 5. Splitting Files The Split File option allows you to compare subgroups or organize output by subgroups. Let s organize output by subgroups. - Data > Split file > Compare groups. Select the button by Organize output by groups. - Highlight a variable you want, for example, country has per capita income less than or any categorical variable. Click OK. - Then do some analysis (for example, run a correlation between literacy and aids rate). The output will show you the results of each group side by side. - - COMPUTE lngdp=ln(gdp_cap). EXECUTE. SPLIT FILE OFF. SORT CASES BY lowincome. SPLIT FILE SEPARATE BY lowincome. COMPUTE lngdp=ln(gdp_cap). EXECUTE. SPLIT FILE OFF. SORT CASES BY lowincome. SPLIT FILE SEPARATE BY lowincome. CORRELATIONS /VARIABLES=literacy aids /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE.

5 - To turn this off, go to Data > Split file > Compare groups and click Analyze all cases, do not create groups. Then select OK. SPLIT FILE OFF. USE ALL. 6. Selecting Cases The Select cases option allows you to analyze only one of the subgroups of your interest. - Data > Select cases. Check If condition is satisfied. Click on the If button. - Highlight a variable you want, for example, country has per capita income less than or any categorical variable, and put it in the expression box by clicking the arrow. Then specify a subgroup by writing an expression you want. Ex) lowinc=1 - Then do some analysis (for example, run a correlation between literacy and aids rate). The output will show only the results of the selected group. - To turn off the selection, go to Data > Select cases. Check All cases. Click OK. COMPUTE filter_$=(lowincome=1). VARIABLE LABEL filter_$ 'lowincome=1 (FILTER)'. VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. FORMAT filter_$ (f1.0). FILTER BY filter_$. EXECUTE. CORRELATIONS /VARIABLES=literacy aids /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE. FILTER OFF. USE ALL. EXECUTE. 7. Hypothesis Testing a. T-test There are three types of t-tests: 1. One-Sample T test- to compare a single sample with a population value, 2. Independent-Samples T test- to compare two groups scores on the same variable, and 3. Paired-Sample T test- to compare the means of two variables within a single group. To compare the means of two variables within a single group: - Go to Analyze > Compare means > Paired-sample t-test. Pick average female life expectancy and average male life expectancy for variable 1 and 2. Put the variables in the box by clicking the arrow. Click OK. T-TEST PAIRS=lifeexpf WITH lifeexpm (PAIRED) /CRITERIA=CI(.9500) /MISSING=ANALYSIS. b. Regression Analysis

6 - To run a linear regression (OLS): go to Analyze > Regression > Linear. Select your dependent variable (females who read) and your independent variables (average female life expectancy, infant mortality, birth rate, Gross domestic product) from the list on the left, then click OK. REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT lit_fema /METHOD=ENTER lifeexpf babymort birth_rt aids_rt gdp_cap. 8. Merging Datasets You can merge data from two files in two different ways: 1. Merge the active dataset with another open dataset or SPSS Statistics data file containing the same variables but different cases. 2. Merge the active dataset with another open dataset or SPSS Statistics data file containing the same cases but different variables. To Merge Files go to Data>Merge Files > Select Add Cases or Add Variables i. Add Cases merges the active dataset with a second dataset or external SPSS Statistics data file that contains the same variables (columns) but different cases (rows). ii. Add Variables merges the active dataset with another open dataset or external SPSS Statistics data file that contains the same cases (rows) but different variables (columns). For example, you might want to merge a data file that contains pre-test results with one that contains post-test results. NB: Cases must be sorted in the same order in both datasets. To Sort Cases go to Data> Sort Cases>Select one or more sorting variables. The data file is sorted based on the values of the sorting variables. If you select multiple sorting variables, cases are sorted by values of each variable within categories of the prior variable on the Sort list. - If one or more key variables are used to match cases, the two datasets must be sorted by ascending order of the key variable(s). - -Variable names in the second data file that duplicate variable names in the active dataset are excluded by default because Add Variables assumes that these variables contain duplicate information. Using command syntax entails first sorting and saving files to be merged and then matching. Below are the commands for merging: GET FILE="dataset_A1.sav". SORT CASES BY varname. SAVE OUTFILE="dataset_A2.sav". GET FILE="dataset_B1.sav". SORT CASES BY varname. SAVE OUTFILE="dataset_B2.sav".

7 MATCH FILES FILE=" dataset_a2.sav " /FILE=" dataset_b2.sav " /BY varname. LIST. Using command syntax, you can merge up to 50 datasets and/or data files. 9. Exporting Outputs - To export outputs, go to File > Export. A dialog box pops up. Specify the type of output you want to export from the Export drop-down menu. Specify your file destination and name in the File Name box ( G:\ouput ). Specify your file type from the File Type drop-down menu. You can export the outputs in Html, Text, Excel, Word, and PowerPoint format. Then click OK.