2016-17 Workshop Series May 17, 2017 Brandon Aragon Understanding SPSS 2016-17 Workshop Series May 17, 2017 Brandon Aragon

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What is SPSS? Statistical analysis software Descriptive statistics and frequencies T-Tests, ANOVA, correlations, etc. Tabulated reports, graphs, and charts. Can also be used for data management

SPSS or Excel? Some benefits of using SPSS over Excel include: Quick and easy access to descriptives Variety of charts and graphs Flexible pivot tables Easy to create subsets Easy value labeling User friendly with output that is easy to understand

Opening Excel Files Open SPSS Open a dataset Save File Opening Window Recent Files Open a dataset File -> Open -> Data File Type: SPSS Statistics (*.sav) Excel (*.xls, *.xlsx, *.xlsm) Read Variable names from the first row of data Save File When you open the excel file in SPSS, the data will appear in the main SPSS window which includes a data editor.

Data View Data View Each row is a different case Each column is a different variable Can drag and move variables (unlike Excel) Variables can be used to group participants (ex. “program”) Change to Variable View Click Variable View tab Double-click variable Used for entering data. Each row represents data from one entity (ex. participant, business etc.), while each column represents a different variable.

Variable View: Describing your Data Edit Variables Each row is a variable Name Edit variable names No special characters (spaces, $, /, etc.) First character has to be a letter Type (String = alphanumeric, Numeric = numbers only) Width Change GPA to 2 decimal places No decimals for ID Label: Full Name from Excel Name = will appear at the top of the corresponding column for each variable; you cannot use spaces or symbols that have other uses in SPSS. Type = Numeric means that the variable contains numbers and is assumed by SPSS; String consist of stings of letters (ex. Name). Width = Default is 8, but more can be used for very precise calculations. Decimals = Default is 2. Label = Can be longer with no restrictions on names.

Variable View: Describing your Data (cont’d) Value Labels Gives a label to simple values Gender: 1 = Male, 2 = Female Missing Values Force SPSS to exclude a value EX: GPA = 99, Class Level missing value Align Similar to Excel Measure Nominal – Gender Ordinal – Level Scale – GPA Can copy/paste Labels, Values, and Missing Change to Data View Click Data View tab Double-click variable number Values = for assigning numbers to groups of people. Missing = Assigning numbers to missing data. Columns = How many characters are displayed in the column of data view. Align = Like excel Measure = Define the level at which the variable is measured.

Search (case/variable) Data View Click GPA -> Ctrl+F Variable View Click Name/Label -> Ctrl+F

Frequencies and Descriptives Analyze -> Descriptive Statistics -> Frequencies Class level, GPA, Gender, Program Statistics and Charts buttons Interpret (Frequency, Percent, Missing ClassLevel) Descriptives Analyze -> Descriptive Statistics -> Descriptives GPA, Gender Interpret (N, Minimum, Maximum, Mean, Gender) Remove Missing value from GPA Right click variable -> Descriptives Interpret (Different way to get information, different mean because of Missing)

Program Exercise Rename the “Program” variable so that it represents a program that is relevant to your area of expertise. Enter a number for each case (values) 1 = Participant, 2 = non-Participant Add variable values Check the characteristics of the variable to make sure they are appropriate.

Select/Split Cases Data -> Split Files Data -> Select Cases Move program into the box, check ‘Compare groups’ Obtain the Descriptives of GPA Data -> Select Cases If condition is satisfied Gender = 1 AND ClassLevel = ‘Seniors‘ Filter out unselected cases Copy Selected cases to a new dataset Delete unselected cases Descriptives of GPA

Crosstabs Analyze -> Descriptive Statistics -> Crosstabs Select and drag row/column/layer variables Statistics Cells – Percentages, Expected Values Create separate tables to analyze: Class Level by Program Participants Gender by Program Participants

Computing New Variables/Recoding Transform -> Compute Variable Target Variable – New Variable Compute Honors if GPA >= 3.5 Recode Missing Value Tranform -> Recode into Same Var. Old & New Values Old Value = System-missing New Value = 0 Click Add

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