SPSS Lessons Output Handout

PSYC 210-Burnham

1.1 What is SPSS? Statistics Package for Social Scientists (SPSS) is a software tool for analyzing data. SPSS operates like a spreadsheet program, such as Excel, and data files look a lot like Excel. Unlike Excel, SPSS is designed for manipulating and analyzing data. As part of your course requirements, you will gain basic understanding of how to use SPSS. Indeed, most statistical analyses are performed with SPSS or some other software. Why do we teach you this stuff by hand, why not just use SPSS? Simply put, it’s because without conceptual knowledge of where the results of an analysis done with SPSS come from, they’re just a bunch of numbers in a computer file! Thus, we teach you what the variance of a set of data is and where it comes from by showing you how it’s calculated. This way, variance should make sense when using SPSS. If my logic doesn’t make sense, drop out of the course and preferably out of college. :-) 1.2 Where is it Available? At the University of Scranton, SPSS / SPSS is available in the Weinberg Memorial Library (WML) on the 1st floor and in group study rooms, Brennan Hall (BRN) rooms 102 and 201, McGurrin Hall (MGH) room 110, Hyland (HYL) Café and room 102 (where statistics classes are held), and Alumni Memorial Hall (AMH) rooms 214 and 202. 1 1.3 Three Types of SPSS Files There are three main files associated with SPSS (and SPSS): 1. Data Files contain data to be analyzed, and have the extension '.sav'. Data files look a lot like a Microsoft Excel spreadsheet, with columns, rows and cells. Columns represent variables, with an abbreviated name of the variable at the top of each column. Rows represent cases, or research subjects. That is, each row/case could be the data associated with an individual, or a sample. Cells and values within the file are the data. 2. Syntax Files are used to request SPSS conduct an analysis, and have the extension '.sps'. Hence, syntax files are command files that tell SPSS what to do with data. I admit that most analyses and procedures in SPSS can be obtained through the pull-down menus in the data file; but, syntax is better for reasons given later. Syntax files are similar to text editors where you insert text-based commands for SPSS to interpret and, hopefully, run your requested analyses on the data. 3. Output Files are generated in response to SPSS running an analysis on a set of data, and have the extension '.spv' (in SPSS the extension is '.spo'). Importantly, if something was written incorrectly in the syntax file, SPSS will produce a “Warning”, usually with no additional output. Most of an output file is table-format, with the exception of graphs and charts. 1.4 Data Files (Data View) There are two different 'views' of a SPSS data file: 1. Data View, where your data can be entered by hand, and where you can view the actual values of the working data file. 2. Variable View, where you can define parameters of your variables, such as how many decimals are showing, whether the variable is a string, a date, or a numeric variable, etc. You can toggle between the Data View and the Variable View by clicking on the appropriate tab at the bottom left hand corner in any data file. You can also toggle back and forth between the Data View and the Variable View by doubleclicking on any variable name. This amounts to double-clicking a column in Data View and double-clicking any row in Variable View. I will assume that you can figure out how to insert values into a data file, so I will not cover them here. 1.5 Defining and Adjusting Variables in Data Files (Variable View) If necessary, it is good to define the parameters of your variables first, so that when when you run an analysis the output of any tables and graphs will be complete and understandable. Below, I've listed each of the parameters that can be seen 1 Thanks to Dr. Barry Kuhle (University of Scranton) for compiling this list.

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SPSS Lessons Output Handout

PSYC 210-Burnham

at the top of each column in Variable View, with a brief description of what each parameter can do:

• • • • • • • • • •

NAME Refers to variables labels that you can enter, but must begin with a letter. TYPE Indicates whether a variable is numeric, a string, a date, etc. Clicking TYPE opens a dialogue box, in which you can specify the type of data contained in a variable. WIDTH Is how many numbers or letters is allowable for a value under a variable. DECIMAL The number of decimal places displayed for numeric variables. LABEL Allows you to assign a longer name to an abbreviated variable label in the data file. That is, you could 'name' a variable STAI, but 'label' the variable ‘State Trait Anxiety Inventory at time 1’. The abbreviated name appears under NAME, and the longer LABEL will appear on any tables or graphs in the output. VALUES Allows you to assign dummy-codes to variable. For example, if your data file contains the variable ‘Sex’, a 0 could refer to males and 1 could refer to females. But, 0's and 1's are arbitrary unless they are defined. This packet will show you how to assign labels using syntax. MISSING Refers to what SPSS should do with missing data entries. COLUMNS How many columns wide you want the variable name to appear in the Data View. ALIGN Allows you to have the values in each column left-justified, right-justified, or centered. MEASURE Relevant to numeric variables. Indicates the measurement scale of a variable. It allows three levels: nominal, ordinal and scale, which refers to both interval and ratio data.

1.6 Basic Structure of Output Files After you have opened a data file, performed some commands , and run an analysis, SPSS will produce an output file. The output file is what we are trying to get SPSS to provide us. It presents, in table or graph form, the descriptive and/or inferential statistics requested. As you can see in the figure below, the output contains a single table with a listing of several descriptive statistics (N, Minimum, Maximum, Mean, Standard Deviation), for two different variables (SAT_CR and SAT_M). Don't worry about the variable names right now; trust me, you'll know what they are in a bit.

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SPSS Lessons Output Handout

PSYC 210-Burnham

2.1 Requesting Frequencies ◦ Analyze ▪ Descriptive Statistics • Frequencies... Statistics Sex N

Valid Missing

Coll_Class

Coll_Maj

240

240

240

0

0

0

Frequency Table Sex Frequency Valid

Percent

Valid Percent

Cumulative Percent

Males

109

45.4

45.4

45.4

Females

131

54.6

54.6

100.0

Total

240

100.0

100.0

2.2 Requesting Frequencies and Asking for More Variables ◦ Analyze ▪ Descriptive Statistics • Frequencies... Once the pop-up window appears, click the 'Statistics' button and check off which statistics you want calculated. Statistics SAT_CR N

Valid

240

Missing Mean

0 491.14

Std. Error of Mean Median

6.838 496.00

Mode Std. Deviation Variance

402a 105.938 11222.800

Skewness

.331

Std. Error of Skewness

.157

Kurtosis

.506

Std. Error of Kurtosis

.313

Range

624

Minimum

237

Maximum

861

Sum

117874

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SPSS Lessons Output Handout

PSYC 210-Burnham

3.1 Requesting Descriptives ◦ Analyze ▪ Descriptive Statistics • Descriptives... Once the pop-up window appears, click the ''Options' button and check off which statistics you want calculated.

Descriptives Descriptive Statistics Std. N

Range

Minimum

Maximum

Sum

Statistic

Statistic

Statistic

Statistic

Statistic

Mean Statistic

Std. Error

Deviation

Variance

Statistic

Statistic

240

49.3

142.3

191.6

40559.5

168.998

.5922

9.1745

84.171

240

84.7

25.6

110.3

16248.1

67.700

.9066

14.0451

197.266

Height_cm Weight_kg 240 Valid N (listwise)

3.2 Splitting the Output into Groups ◦ Data ▪ Split File Once the pop-up window appears, click the radio button 'Organize Output by Groups'. Then, select which variable you want to separate the output by. Then, re-request the descriptive statistics as in section 3.1: ◦ Analyze ▪ Descriptive Statistics • Descriptives...

Coll_Class = Freshmen Descriptive Statistics Std. N

Range

Minimum

Maximum

Sum

Statistic

Statistic

Statistic

Statistic

Statistic

Mean Statistic

Std. Error

Deviation

Variance

Statistic

Statistic

57

31.2

153.3

184.5

9638.8

169.102

1.1159

8.4251

70.982

57

71.9

38.4

110.3

3847.0

67.491

1.9307

14.5762

212.467

Height_cm Weight_kg 57 Valid N (listwise)

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SPSS Lessons Output Handout

PSYC 210-Burnham

Coll_Class = Sophomore Descriptive Statistics Std. N

Range

Minimum

Maximum

Sum

Statistic

Statistic

Statistic

Statistic

Statistic

Mean Statistic

Std. Error

Deviation

Variance

Statistic

Statistic

65

43.8

147.8

191.6

10923.1

168.048

1.2866

10.3732

107.604

65

64.6

25.6

90.2

4244.8

65.305

1.5937

12.8486

165.087

Height_cm Weight_kg 65 Valid N (listwise)

Coll_Class = Junior Descriptive Statistics Std. N

Range

Minimum

Maximum

Sum

Statistic

Statistic

Statistic

Statistic

Statistic

Mean Statistic

Std. Error

Deviation

Variance

Statistic

Statistic

63

46.3

142.3

188.6

10659.4

169.197

1.0615

8.4254

70.987

63

63.3

37.3

100.6

4349.2

69.035

1.7392

13.8046

190.568

Height_cm Weight_kg 63 Valid N (listwise)

Coll_Class = Senior Descriptive Statistics Std. N

Range

Minimum

Maximum

Sum

Statistic

Statistic

Statistic

Statistic

Statistic

Mean Statistic

Std. Error

Deviation

Variance

Statistic

Statistic

55

34.7

154.0

188.7

9338.2

169.785

1.2657

9.3869

88.114

55

68.1

35.8

103.9

3807.1

69.220

2.0311

15.0633

226.904

Height_cm Weight_kg 55 Valid N (listwise)

3.3 Turning Off the Split File Option ◦ Data ▪ Split File Once the pop-up window appears, click the radio button 'Analyze All Cases, Do Not Create Groups.'

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SPSS Lessons Output Handout

PSYC 210-Burnham

4.1 Requesting Correlations ◦ Analyze ▪ Correlate • Bivariate If you want to request the descriptive statistics for each variable, and the cross produces and covariances between variables: click the 'Options' button, and then check off the appropriate boxes.

Correlations Descriptive Statistics Mean

Std. Deviation

N

SAT_CR

491.14

105.938

240

SAT_M

516.79

127.100

240

SAT_V

496.69

66.772

240

Correlations SAT_CR SAT_CR

Pearson Correlation

SAT_M 1

-.062

.481**

.340

.000

2682249.183

-199155.775

813393.625

11222.800

-833.288

3403.321

240

240

240

-.062

1

-.048

Sig. (2-tailed) Sum of Squares and Cross-

SAT_V

products Covariance N SAT_M

Pearson Correlation Sig. (2-tailed)

.340

Sum of Squares and Cross-

.461

-199155.775

3860928.162

-96992.938

-833.288

16154.511

-405.828

240

240

240

.481**

-.048

1

.000

.461

813393.625

-96992.938

1065571.563

3403.321

-405.828

4458.458

240

240

240

products Covariance N SAT_V

Pearson Correlation Sig. (2-tailed) Sum of Squares and Crossproducts Covariance N

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SPSS Lessons Output Handout

PSYC 210-Burnham

5.1 Requesting Independent Groups t-Test • Analyze ◦ Compare Means ▪ Independent Samples T Test... Place the dependent variable to be assessed in the 'Test Variables' panel (you can select more than one). Place the independent variable in the 'Grouping Variable' panel, and be sure to identify which levels are being compared.

T-Test Group Statistics Tutor_Group Post_GREv

Post_GREa

N

Mean

Std. Deviation

Std. Error Mean

Control Group (no tutoring)

80

419.25

62.334

6.969

Individual Tutoring

80

442.88

68.993

7.714

Control Group (no tutoring)

80

4.17

.355

.040

Individual Tutoring

80

4.22

.456

.051

Independent Samples Test Levene's Test for Equality of Variances

t-test for Equality of Means 95% Confidence

F

Sig.

t

df

Sig. (2-

Mean

Std. Error

Interval of the

tailed)

Difference

Difference

Difference Lower

Equal variances Post_GREv

.978

.324

Upper

-2.273

158

.024

-23.625

10.396

-44.157

-3.093

-2.273

156.400

.024

-23.625

10.396

-44.159

-3.091

-.774

158

.440

-.050

.065

-.178

.078

-.774

149.096

.440

-.050

.065

-.178

.078

assumed Equal variances not assumed Equal variances

Post_GREa

4.221

.042

assumed Equal variances not assumed

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SPSS Lessons Output Handout

PSYC 210-Burnham

5.2 Requesting Correlated Samples t-Test • Analyze ◦ Compare Means ▪ Correlated Samples T Test... Click the two variables that are to be compared, and then click the arrow button in the middle.

T-Test Paired Samples Statistics Mean Pair 1

Pair 2

N

Std. Deviation

Std. Error Mean

Pre_STAIt

50.27

240

11.923

.770

Post_STAIt

50.09

240

11.944

.771

Pre_GREq

568.71

240

76.484

4.937

Post_GREq

591.13

240

79.685

5.144

Paired Samples Correlations N

Correlation

Sig.

Pair 1

Pre_STAIt & Post_STAIt

240

.987

.000

Pair 2

Pre_GREq & Post_GREq

240

.934

.000

Paired Samples Test Paired Differences

Mean

t

Std.

Std. Error

95% Confidence Interval of

Deviation

Mean

the Difference Lower

Pair 1

Pre_STAIt -

df

Sig. (2-tailed)

Upper

.175

1.939

.125

-.072

.422

1.398

239

.163

-22.417

28.461

1.837

-26.036

-18.798

-12.202

239

.000

Post_STAIt Pair 2

Pre_GREq Post_GREq

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