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Faculty Research Center

College of Education

http://frc.coe.nau.edu/

THE BASICS OF USING SPSS OCTOBER 22, 2008

PRESENTED BY:

Robert A. Horn, Ph.D. Assistant Professor, Educational Psychology

928-523-0545 [email protected]

PRESENTATION ASSISTANT:

Amy L. Prosser, M.A. FRC Research Assistant

928-523-5314 [email protected]

PURPOSE OF WORKSHOP

The purpose of this workshop is to review the basic steps in setting up and manipulating data in SPSS. This workshop will also review examples of basic data analyses, such as descriptive statistics, correlation, t-tests, and analysis of variance. WORKSHOP TOPICS

1. Introductory Comments

2. Startup Procedures for SPSS

3. A Quick Tour of the SPSS Screens

4. Naming and Defining Variables

5. Entering Data

6. Examples of Basic Analyses

7. Examples of Modifying Data Procedures

This workshop is an update from the Refresher Training in Using SPSS training presented by William Martin, Ed.D. (Professor, Educational Psychology) on November 16, 2006. Dr. Martin

was assisted by Jen Felder and Ann Rostedt (FRC Research Assistants).

MARTIN (11/06) – HORN (10/08)

THE BASICS OF USING SPSS PAGE 2

1. INTRODUCTORY COMMENTS

SPSS operates through pull-down menus and can also be operated through syntax commands. The pull-down format can have a significant advantage, because the only major preparation required to use the program is to experiment with it and become familiar with the different options on the menus. If you are comfortable using other Windows applications, you should have no problems using SPSS. If you select a particular menu or procedure, and determine that you do not want to use it, you can typically click Cancel (or some similar escape command) to exit that menu. Most of us have learned to use SPSS through experimentation and trial-and-error rather than by referencing a manual. In this regard, you (through trial-and-error activities) may well discover preferred and even more efficient ways to do things than those that are outlined here, so be adventuresome – experiment! Regardless of your learning style, motivation, and interest, here are some general guidelines for you to keep in mind. Review these guidelines now and once again after you have become more familiar with the SPSS program.

• When you are entering data or doing new work on existing data, SAVE YOUR WORK every 3 minutes or so. A lot of work can be accomplished in a relatively short period of time – and you don’t want to loose it. The Save command is in the File menu – or you can use the Save Icon on the tool bar.

• When you choose Save As … the window that appears at the top left shows you the destination of the saved file (i.e., Look in:).

• As there are so many aspects of SPSS that cannot be fully covered in a workshop, you may find yourself at times needing Help ! So, if you have any questions while you are working with SPSS, try the Help screens. They usually provide the answer(s) as well as additional useful information.

• While in any screen (e.g., Data Editor) and most submenus, the Help option is available. Simply click on Help from the menu bar.

• In most screens and sub screens, the easiest and most direct way to access Help is by pressing the F1 Function Key on your keyboard while SPSS is open.

• Help is provided in many different ways (as pointed out in the SPSS Help).

• The Help menu in most SPSS windows provides access to the main Help system, which includes: Topics, Tutorials, Case Studies, Statistics Coach, Command Syntax Reference, etc.

• While in sub screens (e.g., Variable Type), when Help is selected, it brings you to that specific section of the Help system.

• To print copies of your data file, syntax, or output, use the Print command in the File menu – or you can use the Print Icon on the tool bar.

• If the display that is showing on your screen is not what you want, click the Window menu (far right). It will show you what files are currently opened.

• If the one you want is listed, click on the screen option that you want to be displayed.

• If it is not listed, you will need to open it. Use the Open command from the File menu – or use the Open File Icon on the tool bar.

MARTIN (11/06) – HORN (10/08)


• An important reminder is that SPSS generates three (3) different screens:

• SPSS Data Editor – which contains the raw data and variable information.

• Output (#) – SPSS Viewer – which contains the output generated by the requested data analysis.

• Syntax (#) – SPSS Syntax Editor – where syntax commands can be created/modified.

• Some computers have been set to activate a screen saver or start its power scheme (i.e., turn off monitor, turn off hard disks, system standby, and/or system hibernation). To prevent the potential loss of data, keep your screen active. The occasional moving of the mouse when not actively working will take care of this.

2. STARTUP PROCEDURES FOR SPSS Startup procedures can differ from computer to computer, however most times you can:

• Click the Start button (bottom left) > click on Programs > click on SPSS Inc (folder icon) > click on SPSS 16.0 (folder icon) > click on SPSS 16.0 (red SPSS icon).

• You will see a dialog box (SPSS 16.0 for Windows) that asks you What would you like to do? Click the radial button for Type in Data > click OK

• This will result in a blank data window that says Untitled – SPSS Data Editor at the top left of the screen. This is our Data Editor Screen .

3. A QUICK TOUR OF THE SPSS SCREENS Let’s take a quick tour of the screen currently shown on your computer (i.e., Untitled – SPSS Data Editor ).

• At the very top is the file title (Untitled – SPSS Data Editor ), as well as minimize, restore, and close buttons.

• The second line/row, which contains the words File , Edit , View , Data, and so on, is called the menu bar.

• Clicking on any of these words produces a pull-down menu of options from which to choose in order to accomplish certain tasks.

• You will use several of these pull-down menus while using SPSS, so take a few minutes and explore what some of the options are.

• The third line/row, containing a row of little pictures (icons), is known as the tool bar.

• These buttons provide shortcuts for tasks otherwise accomplished through the use of the pull-down menus.

• For example, the button with the printer on it does the same thing as clicking, File then clicking Print from the menu bar.

MARTIN (11/06) – HORN (10/08)


• The particular buttons appearing on the tool bar can vary widely. The functions of these buttons are not necessarily self-evident from the pictures – but you can find out what a particular button does by resting the cursor (using the mouse) on top of it but not clicking on it.

• When you rest the cursor on one of the buttons, a brief phrase appears summarizing the button’s function.

• We may use these buttons from time to time, so take a few minutes to familiarize yourself with some of the various functions that are available.

• The majority of the screen (in the Data View) is occupied by a matrix (spreadsheet) for entering data. Typically, the columns are the variables and the rows are the cases. Scroll bars (horizontal and vertical) are provided to maneuver around the screen.

• Along the left side of the spreadsheet (in grey shade) are the row numbers. Keep in mind that these numbers are not variables – they merely serve as a reminder of what row the data are located.

• If you need a number (e.g., Student I.D.) then a variable will need to be created to serve such a purpose. The row numbers do not become part of any data analyses.

• Across the top of the matrix are the column names. Currently, these are all var, which will later be changed as we name our variables.

• The generic name, var (in the Data View tab) could get confusing, since we may later forget which variable is in which column. If you were to enter data at this time, the var will change to VAR0001, VAR0002, and so on if we did not name them.

• Above the var columns is the location indicator, which shows what row (case) and column (variable) your cursor is currently in as well as any assigned value.

• At the bottom (left-hand corner) of the Data Editor screen are two tabs.

• One (Data View ) will allow you to see your data, and input the data information.

• The second (Variable View ) will allow you to define the different variables in your data set.

• Click on the Variable View tab and let’s take a quick tour of this screen.

• At the very top is the file title (Untitled – SPSS Data Editor ), as well as minimize, restore, and close buttons.

• The second line/row, which contains the words File , Edit , View , Data, and so on, is called the menu bar.

• Clicking on any of these words produces a pull-down menu of options from which to choose in order to accomplish certain tasks.

• You will use several of these pull-down menus while using SPSS, so take a few minutes and explore what some of the options are.

MARTIN (11/06) – HORN (10/08)


• The third line/row, containing a row of little pictures (icons), is the tool bar.

• These icons provide shortcuts for tasks otherwise accomplished through the use of the pull-down menus. For example, the button with the floppy-A symbol on it does the same thing as clicking, File then clicking Save from the menu bar.

• The particular buttons appearing on the tool bar can vary widely. The functions of these buttons are not necessarily self-evident from the pictures – but you can find out what a particular button does by resting the cursor (using the mouse) on top of it but not clicking on it.

• When you rest the cursor on one of the buttons, a brief phrase appears summarizing the button’s function.

• We may use these buttons from time to time, so take a few minutes to familiarize yourself with some of the various functions that are available.

• The remainder of the screen is unique to the Variable View . You will notice that the variables are now rows and the columns are now components of the variable.

• The first column (the gray shaded numbers) are (or will become) the variables in your data set.

• Next is Name, which provides a name for a variable. Below we will cover naming variables.

• Next is Type , which defines the type of variable such as text, numeric, string, scientific notation, etc.

• Next is Width , which defines the number of characters the column housing the variable will allow.

• Next is Decimals , which defines the number of decimals that will appear in the Data View window.

• Next is Label , which defines a label up to 256 characters for the variable. Labels allow us to make the variable name more meaningful. For example, if we name the variable PPT – the label may be Projective Personality Test.

• Next is Values , which defines the labels that correspond to certain numerical values (such as 1 for Male and 2 for Female).

• Next is Missing , which indicates how missing data will be dealt with.

• Next is Columns , which defines the number of spaces allocated for the variable in the Data View window.

• Next is Align , which defines how the data is to appear in the cell ( left, right, or center aligned).

• Last is Measure , which defines the scale of measurement that best characterizes the variable (scale, ordinal, or nominal). Scale includes interval and ratio data.

MARTIN (11/06) – HORN (10/08)


4. NAMING AND DEFINING VARIABLES An important part of working with data is to define the variables that you will be using. By defining, we mean everything from providing a variable name, identifying the type of data, how many decimals will be needed, to identification of the scale of measurement. The typical way we define the characteristics of a variable is by clicking on the applicable cell and specify the particular characteristics of that variable.

• Click on the Variable View tab at the bottom of the data matrix screen.

• As indicated above, the variables are now rows as we define them. We will define the variables for this set of data using the attached survey information (Appendix A – The Basics of Using SPSS Survey).

• Before we start, you should be aware of the following rules that apply to variable names:

• Each variable name must be unique; duplication is not allowed.

• In SPSS, variable names are typically kept to a minimum. However, version 16.0 of SPSS allows for extended character length (up to 64 bytes long). The general rule of thumb is – keep the variable name as short as possible – but make sure it is clear to you and to keep them manageable.

• Variable names may be composed of any combination of letters and numbers (and select symbols). The first character must be a letter or one of the special characters @, #, or $ (see below). Subsequent characters can be any combination of letters, numbers, nonpunctuation characters, and a period (.).

• A # character in the first position of a variable name defines a scratch variable. You can only create scratch variables with command syntax. You cannot specify a # as the first character of a variable in dialog boxes that create new variables.

• A $ sign in the first position indicates that the variable is a system variable. The $ sign is not allowed as the initial character of a user-defined variable.

• Variable names ending with a period should be avoided, since the period may be interpreted as a command terminator. You can only create variables that end with a period in command syntax. You cannot create variables that end with a period in dialog boxes that create new variables.

• The period, the underscore, and the characters $, #, and @ can be used within variable names. For example, A._$@#1 is a valid variable name.

• SPSS does distinguish between upper- and lowercase letters – so variable names can be defined with any mixture of uppercase and lowercase characters, and the case is preserved for display purposes.

• You cannot have (use) spaces in your variable name.

• In lieu of using a space in your variable name – use the underscore “_” symbol.

• Variable names ending with an underscore “_” should be avoided, since names may conflict with names of variables automatically created by commands and procedures.

MARTIN (11/06) – HORN (10/08)


• Because they have a unique meaning in SPSS, the following reserved variable names cannot be used: all , and , by , eq, ge, gt , le, lt , ne, not , or , to , and with .

• When long variable names need to wrap onto multiple lines in output, lines are broken at underscores, periods, and points where content changes from lower case to upper case.

• Variable names and/or identifiers for this exercise are shown in ‘Bold Arial ’ in single quotes (‘ ’), but the name should not be typed with the quotes into SPSS.

• Variable labels and/or value labels to be typed for this exercise are shown in Italic Arial, but will not be entered into SPSS in italics.

4.1. ID: We will now name and define our first variable ‘ID’. Please refer to Appendix B –

Variable View – Sample Data.

• Click in Row 1 of the Name column. The cell (field) will by outlined once you’re there.

• Type ‘ID’, then press the Tab key (you can also cursor over or click into the Type column for row 1).

• In the Type column, you will notice that the default is Numeric, which will be acceptable for most variables – and is correct for this variable.

• From the dialog box [which is found by clicking on the three dots (…) in a cell], you can specify the type of variable.

• Variable types can be numeric, comma, dot, scientific notation, date, dollar, custom currency, or string.

• To get the description of the variable type options, click on Help in the Variable Type window. Descriptions of the various data types are provided.

• Press the Tab key. You should now be in the Width column.

• SPSS’s default of 8 is okay for this exercise. Clicking on the up or down arrow in the cell will change the allowable width of the variable. This can also be accomplished by simply typing the number once you tab into the column.

• However, the number associated with the decimal column will affect the lower limits of your choices – so, you may need to adjust the decimal first, then return and adjust the variable width.

• Press the Tab key. You should now be in the Decimals column.

• SPSS’s default is 2, which you may want to change – to 0 since we do not have any decimal values for this variable. This will also eliminate a lot of extra numbers that would otherwise be seen on the data editor matrix.

MARTIN (11/06) – HORN (10/08)


• Clicking on the up or down arrow in the cell will change the number of allowable decimal places for the variable. Click the down arrow until a 0 appears. This can also be accomplished by simply typing the number once you have tabbed into the column (cell).

• Press the Tab key. You should now be in the Label column.

• This is where you will type a meaningful word or set of words to describe the variable. For this particular variable – we could choose not to type in a label – as ‘ID’ is self explanatory. You may, for example, choose to type Participant Identification

• Press the Tab key. You should now be in the Values column.

• Clicking on the three dots (…) in that cell will open the Value Labels screen. You can use upper- and/or lowercase alpha characters or numbers for your labels. However, for this variable, we do not have value labels.

• Press the Tab key. You should now be in the Missing Values column.

• SPSS’ default of None will be fine for this exercise. For more information on missing values, you can click on the three dots (…), which will open the Missing Values screen. From there, click on HELP.

• Unless otherwise specified, SPSS reflects (shows) missing data on the spreadsheet with a period ( . ). You do not add a period, but leave the cell blank if any data is missing.

• Press the Tab key. You should now be in the Columns column.

• SPSS’s default of 8 is okay for this exercise. Clicking on the up or down arrow in the cell will change the width of the column presented in the data matrix. This can also be accomplished by simply typing the number once you have tabbed into the column (cell).

• Press the Tab key. You should now be in the Align column.

• SPSS’s default setting of Right is okay for this exercise. Remember, you have the option of Left, Right, or Center alignment.

• Press the Tab key. You should now be in the Measure column.

• Remember, you have the option of Scale (default), Ordinal, or Nominal here, however, be careful to choose the appropriate one, as it will potentially affect your analyses. SPSS’ default of Scale will be okay for this variable.

• Just a reminder:

• Scale = continuous data (interval/ratio) that is numeric

• Ordinal and Nominal = categorical data that is either string or numeric

• For the variable ‘ID’ (row 1), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

MARTIN (11/06) – HORN (10/08)


• Press the Tab key. You should be back at the Name column and ready to define the next variable.

4.2. ReturnDate: For the next variable from the Survey, type in ‘ReturnDate ’ as the name

of the variable. This will begin row 2 (the cell underneath ‘ID’).

• The variable type for ‘ReturnDate ’ should be a Date. To change from the default (Numeric), click on the three dots (…) in the Type cell. This will open the Variable Type screen.

• Select the radial button for Date. A format option list will appear. Select the mm/dd/yy format. Then click OK.

• You may choose to add a Label, otherwise, leave the default values for the remainder of the cells in this row.

• For the variable ‘ReturnDate ’ (row 2), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

4.3. Status1: For the next variable from the Survey, type in ‘Status1 ’ as the name of the

variable. The 1 is for the question number. This will begin row 3 (the cell underneath ‘ReturnDate ’).

• This is a grouping variable (Nominal Scale) where Group (Value) 1 = Student and Group (Value) 2 = Professional

• For the Label, type in Counseling Students and Professionals. This label provides a fuller description of the variable that can be displayed in tables and charts.

• For the Values, we will need to identify the two levels of this grouping variable.

• Click the three dots (…) in that cell – this will open the Value Labels screen. (You can use upper- and/or lowercase alpha characters or numbers for labels.).

• Click in the Value: box (if you are not already there), then type 1

• Click in the Label: box, then type Student, then click Add

• Click back into the Value: box (you may need to backspace to clear out the 1), then type 2

• Click back into the Label: box (you may need to backspace to clear out Student), then type Professional

• Click Add , then click OK

• For the Measure, you will want to change this to Nominal

• You may choose to change the Decimals value to 0, otherwise, leave the default values for the remainder of the cells in this row.

• For the variable ‘Status1 ’ (row 3), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

MARTIN (11/06) – HORN (10/08)


4.4. Gender2: For the next variable from the Survey, type in ‘Gender2 ’ as the name of the variable. The 2 is for the question number.

• This is a grouping variable (Nominal Scale) where Group (Value) 1 = Male and Group (Value) 2 = Female

• For the Label, type in Participant Sex

• For the Values, we will need to identify the two levels of this grouping variable using the procedures you did for ‘Status1 ’

• 1 = Male and 2 = Female



• For the variable ‘Gender2 ’ (row 4), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

This might be a good place to Save your work.

Click File , and then click Save As

Choose where to save the file (e.g., My Documents, Desktop, Folder, or Portable Disk)

A suggested name would be SPSS Sample Data 4.5. Age3: For the next variable (a continuous variable) from the Survey, type in ‘Age3 ’ as

the name of the variable.

• You may choose to add a Label or change the Decimals value to 0, otherwise, leave the default values for the remainder of the cells in this row.

• For the variable ‘Age3 ’ (row 5), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

4.6. Ethnicity4: For the fourth question from the survey, we are going to create two

variables, one Nominal and a second String variable for the open ended responses that were generated from Please Specify (on the Survey, Appendix A).

• For the Nominal variable, please name this variable ‘Ethnicity4 ’

• For the Label, type in Participant Ethnicity

• For the Values, we will need to identify the six levels of this grouping variable using the procedures you did for ‘Status1 ’ and ‘Gender2 ’

• 1 = African American

• 2 = Asian American

MARTIN (11/06) – HORN (10/08)


• 3 = European American

• 4 = Hispanic American

• 5 = Native American

• 6 = Other



• For the variable ‘Ethnicity4 ’ (row 6), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

4.7. EthnicOther: For the String variable, please name this variable ‘EthnicOther ’

• For the Type, change this to String

• For the Width, you will want to change this to 25 (to account for possible responses)

• For the Label, type in Other Ethnicity

• Leave the default values for the remainder of the cells in this row

• For the variable ‘EthnicOther ’ (row 7), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

This might be a good place to Save your work

Click File , and then click Save or click the Save icon on the Tool Bar 4.8. Confidence5: For the next variable from the Survey, type in ‘Confidence5 ’ as the

name.

• For the Label, type in Confidence as a Counselor

• For the Values, we will need to identify the five levels of this grouping variable using the procedures you did earlier.

• 1 = Strongly Disagree

• 2 = Disagree

• 3 = Somewhat Disagree

• 4 = Agree

• 5 = Strongly Agree

• For the Measure, you will want to change this to Ordinal

MARTIN (11/06) – HORN (10/08)



• For the variable ‘Confidence5 ’ (row 8), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

4.9. COSE6: For our last variable from the Survey, type in ‘COSE6’ as the name.

• For the Label, type in Counselor Self-Estimate Inventory Scale Score

• Leave the default values for the remainder of the cells in this row.

• For the variable ‘COSE6’ (row 9), your cells should have the same information as the printed copy of the attached Appendix B – Variable View – Sample Data.

Save your work 5. ENTERING DATA Click on the Data View tab at the bottom left of the window to get back to the spreadsheet that we will use for entering data.

• Refer to the following handout (Appendix C – Data View Sample Data), which shows all of the data that you will now enter.

• To save time (but to still provide practice), notice that only a few sample data are entered in ‘ReturnDate ’ and ‘EthnicOther ’

• The Enter key moves you down the spreadsheet and the Tab key moves you across the spreadsheet. Note that you can also navigate around the spreadsheet using the arrow keys or simply clicking into the cell of interest.

• If you make a mistake, click on the cell and delete the error and retype the correct information.

• As data entry is a common error, please check your entered data after each column is entered and then Save your work.

• Enter the data – as shown in Appendix C – Data View – Sample Data. 6. EXAMPLES OF BASIC ANALYSES 6.1. Frequency Analysis: (Good for ungrouped data.)

• From the Menu Bar

• Click Analyze > Descriptive Statistics > Frequencies

• From the box on the left, click on (highlight) ‘ReturnDate ’ and then click it over (using the arrow to the right) to the space under Variable(s):

MARTIN (11/06) – HORN (10/08)


• Repeat this for ‘Status1 ’ and ‘Gender2 ’

• All three variables should be in the box under Variable(s):

• Click on the button to the right called Charts

• Select the radial button for Bar Charts

• Select the radial button for Percentages

• Click on Continue

• This will return you to the Frequencies screen, now click OK

• This will produce frequency tables and bar charts for the three variables. 6.2. Creating Standardized z Scores:


• Click Analyze > Descriptive Statistics > Descriptives

• From the box on the left, click on (highlight) ‘COSE6’ and then click it over (using the arrow to the right) to the space under Variable(s):

• Select Save standardized values as variables

• Click OK

• This will produce a new variable (standardized z-scores) in the data set ‘ZCOSE6’ 6.3. Explore Analysis: (Good for grouped data and univariate data screening.)


• Click Analyze > Descriptive Statistics > Explore

• From the box on the left, click on (highlight) ‘COSE6’ and then click it over (using the arrow to the right) to the space under Dependent List:

• Click on ‘Status1 ’ and then click it into the Factor List:

• Under Display, keep the default of Both

• Click on the button to the right called Plots

• Under Descriptive, select Histogram

• Select Normality plots with tests

• This will produce the Tests of Normality (e.g., Shapiro-Wilks)

• Click on Continue

• This will return you to the Explore screen, now click OK

• This will produce output rich in descriptive information and univariate data screening results.

MARTIN (11/06) – HORN (10/08)


6.4. Independent-Sample t: (Good for differences between two independent groups.)


• Click Analyze > Compare Means > Independent-Samples T Test

• Click on ‘COSE6’ and then click it into the Test Variable(s): box (this is the dependent variable)

• You can click over several variables under Test Variable(s): and it will run a separate independent-samples t test on each dependent variable.

• Click on ‘Status1 ’ and then click it into the Grouping Variable: box (this is the independent variable)

• Click on Define Groups

• Type the number 1 (which represents Students) beside Group 1:

• Type the number 2 (Professionals) beside Group 2:

• Click Continue

• Click OK

• This will produce output with group statistics, Levene’s test of equality of variance, and t test equality of means (group differences).

6.5. One-way ANOVA: (Good for differences between two or more independent groups.)


• Click Analyze > Compare Means > One-Way ANOVA

• Click on ‘COSE6’ and then click it into the Dependent List: box (this is the dependent variable)

• You can click over several variables under Dependent List: and it will run a separate one-way analysis of variance test on each dependent variable.

• Click on ‘Ethnicity4 ’ and then click it into the Factor: box (this is the independent variable)

• Click on Post Hoc

• Choose at least one option from Equal Variances Assumed (e.g., Tukey)

• Choose at least one option from Equal Variances Not Assumed (e.g., Games-Howell)

• Click Continue

• Click on Options

• Under Statistics

• Select Descriptives, Homogeneity of variance test, Welch (or you may select Brown-Forsythe), and Means plot

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• Click Continue

• Click OK

• This will produce output to describe the groups, test the assumption of homogeneity of variance, determine group differences, and examine pairwise differences.

6.6. Factorial ANOVA: (2 × 2 ANOVA)


• Click Analyze > General Linear Model > Univariate

• Click on ‘COSE6’ and then click it into the Dependent Variable: box (this is the dependent variable)

• Click on ‘Status1 ’ and then click it into the Fixed Factor(s): box (this is the first independent variable)

• Click on ‘Gender2 ’ and then click it into the Fixed Factor(s): box (this is the second independent variable)

• Click on Plots

• Click on ‘Status1 ’ and click it into the Horizontal Axis: box

• Click on ‘Gender2 ’ and click it into the Separate Lines: box

• Click Add

• Click Continue


• Under Display

• Select Descriptive statistics, Estimates of effect size, and Homogeneity tests

• Click Continue

• Click OK

• This will produce output to describe the groups, test the assumption of homogeneity of variance, determine group differences, and examine main effects and possible interaction.

6.7. Bivariate Correlation Matrix:


• Click Analyze > Correlate > Bivariate

• Click on ‘Age3 ’ and then click it into the Variables: box

• Click on ‘Confidence5 ’ and then click it into the Variables: box

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• Click on ‘COSE6’ and then click it into the Variables: box


• Under Statistics

• Select Means and standard deviations

• Click Continue

• If your variables are continuous (like ours), we would keep the default of Pearson. If your data are ordinal (ranked), we would select Spearman.

• Click OK

• This will produce output to describe the groups and provide a correlation matrix to examine the relationship (and significance) among the set of variables.

6.8. Simple Scatterplot:


• Click Graphs > Legacy Dialog > Scatter/Dot

• Click on Simple Scatter (this should be the default)

• Click Define

• Click on ‘Age3 ’ and then click it into the Y Axis: box

• Click on ‘COSE6’ and then click it into the X Axis: box

• You can add titles and footnotes to your graph by clicking on Titles and adding the applicable information.

• Click OK

• This will produce the scatterplot showing the relationship between the two variables (‘Age3 ’ and ‘COSE6’).

7. EXAMPLES OF MODIFYING DATA PROCEDURES 7.1. Inserting a Variable:

• We are going to add a new variable called ‘Work ’ (Work Experience).

• There are a few ways to add a new variable.

• From the Data View screen, click on the top (in the gray shaded variable name) of the column called ‘Age3 ’ to highlight it.

• With the column highlighted, right click on the variable name ‘Age3 ’ and click on Insert Variable

• Or, with the column highlighted, click on the Insert Variable icon from the Tool Bar (it looks like a spreadsheet with a red arrow pointing down into it).

MARTIN (11/06) – HORN (10/08)


• Or, with the column highlighted, click on Edit from the Menu Bar, and click on Insert Variable

• Click on the Variable View tab and name and define the variable using the guidelines outlined in this workshop.

7.2. Inserting a Case:

• We are going to insert a new case into the data file.

• There are a few ways to insert a new case.

• From the Data View screen, click on number at row 22 (the gray shaded 22 on the left most column). This will highlight the row.

• With the row highlighted, right click on the row number (22) and click on Insert Cases

• Or, with the row highlighted, click on the Insert Cases icon from the Tool Bar (it looks like a spreadsheet with a red arrow pointing into it from the left).

• Or, with the column highlighted, click on Edit from the Menu Bar, and click on Insert Cases

• You can now add the information for this case from the Data View tab. 7.3. Sorting Cases in Ascending (or Descending) Ord er by a Variable:

• There are a few ways to sort cases by a variable.

• From the Data View screen, click on number at column (in the gray shaded variable name) of the column called ‘Gender2 ’ to highlight it.

• With the column highlighted, right click on the variable name ‘Gender2 ’ and click on Sort Ascending (from least to greatest) or Sort Descending (from greatest to least).

• Or, with the column highlighted, click on Data from the Menu Bar, and click on Sort Cases

• Select ‘Gender2 ’ from the list of variables on the left and click (move) it over to the Sort by: box.

• Under Sort Order

• Select the radial button for Ascending or Descending

• Click OK 7.4. Selecting Cases for One Group within a Categor ical Variable:


• Click Data > Select Cases

• Click on ‘Gender2 ’

MARTIN (11/06) – HORN (10/08)


• Select the radial button for If condition is satisfied

• Click on the If button (directly below If condition is satisfied)

• Click on ‘Gender2 ’ and then click it into the box to the right of the blue arrow

• Then type an equal sign ( = ) or select the equal sign from the keypad

• Then type the number 1 (which represents Males) or select the number 1 from the keypad

• This should look like Gender2 = 1

• Click Continue

• Click OK

• From the Data View, notice that the rows are crossed out if they are a 2 (which represents Females)

• Go back and remove the selection criteria

• Click Data > Select Cases

• Select the radial button for All cases (or click Reset)

• Click OK 7.5. Square-Root Transformation of a Variable:


• Click Transform > Compute Variables

• Under Target Variable

• Type ‘SqrtCOSE6 ’

• Under Function group:

• Click All

• Scroll down the list under Function and Special Variables: until you find Sqrt (then click on it)

• Click on the up arrow just to the left of Functions and Special Variables (this will place the SQRT in the box under Numeric Expression:

• You will notice a highlight question mark in parentheses (?) – do not make any changes to this…

• Click on ‘COSE6’ and then click it into the box under Numeric Expression:

• This will replace the highlighted question mark

• This should look like SQRT(COSE6)

MARTIN (11/06) – HORN (10/08)


• Click OK

• The new variable ‘SqrtCOSE6 ’ will show up on the spreadsheet (Data View) as the last column and the last row in Variable View

As suggested by Tabachnick and Fidell (2007) and Howell (2007), the following guidelines (including SPSS compute commands) should be used when transforming data.

If your data distribution is… Use this transformation method.

Moderately positive skewness Square-Root

NEWX = SQRT(X)

Substantially positive skewness Logarithmic (Log 10)

NEWX = LG10(X)

Substantially positive skewness Logarithmic (Log 10)

(with zero values) NEWX = LG10(X + C)

Moderately negative skewness Square-Root

NEWX = SQRT(K – X)

Substantially negative skewnes Logarithmic (Log 10)

NEWX = LG10(K – X) C = a constant added to each score so that the smallest score is 1. K = a constant from which each score is subtracted so that the smallest score is 1; usually equal to the largest score + 1.

References

Howell, D. C. (2007). Statistical methods for psychology (6th ed.). Belmont, CA: Thomson Wadsworth.

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Allyn and Bacon.

MARTIN (11/06) – HORN (10/08)


Appendix A

The Basics of Using SPSS Survey

ID:

Return Date of Survey:

1. Please check the one category below that best describes you:

____ Counseling Student

____ Counseling Professional

2. Are you?

____ Male

____ Female

3. How old are you?

4. Which one of the following ethno cultural groups best describes you?

____ African American

____ Asian American

____ European American

____ Hispanic American

____ Native American

____ Other Please Specify:

5. To what extent do you agree that you are confident as a counselor?

____ Strongly Disagree

____ Disagree

____ Somewhat Agree

____ Agree

____ Strongly Agree

6. Counseling Self-Estimate Inventory (COSE) Scale Score:

Appendix B – Variable View – Sample Data

Appendix C – Data View – Sample Data

MARTIN (11/06) – HORN (10/08)


Appendix C – Data View – Sample Data

ID ReturnDate Status1 Gender2 Age3 Ethnicity4 EthnicOther Confidence COSE

1 10/18/08 1 2 23 6 Pakistanian 3 27.00

5 10/20/08 1 2 25 1 4 17.00

10 10/21/08 2 1 51 2 2 38.00

11 2 2 28 3 5 26.00

12 2 2 49 2 5 40.50

13 2 1 58 3 5 33.00

15 2 2 50 4 3 38.00

21 2 1 60 4 4 40.00

22 2 1 53 3 2 36.00

30 1 2 33 6 Canadian 1 31.00

32 1 2 26 3 3 26.00

33 1 1 23 1 5 28.50

34 1 1 26 3 4 21.00

35 1 2 26 5 4 25.00

44 1 2 46 4 3 24.00

57 2 2 45 3 4 30.00

67 2 1 35 1 3 32.00

72 1 1 27 5 4 18.00

73 1 2 41 5 5 20.00

77 1 2 25 3 4 34.00

80 1 2 30 6 Samoan 3 24.00

81 1 1 37 3 2 26.00

90 1 1 34 4 2 28.00

97 1 2 34 4 3 26.50

107 2 2 33 5 4 22.00

109 2 1 32 5 2 28.00

116 2 1 42 5 3 31.00

123 2 1 51 2 4 19.00

137 1 2 33 3 3 26.00

139 1 1 31 1 2 34.00