GETTING STARTED To get started in SPSS, the first thing you ll need to do is open a data file. Open our sample data file insurance_claims.sav There are two ways to look at your data in SPSS: DATA VIEW shows the actual data (i.e., responses to your survey) VARIABLE VIEW shows names and properties of all of your variables: o This is where you can type in your survey questions and the value for each response (i.e., 1 = strongly agree, 2 = agree, etc.) o You can change variable type (ordinal, nominal, scale) There are different types of data - make sure you know which type of data you are using, as different data types are suitable or different analyses. Nominal level data - categorized using names or labels; qualitative analyses only Ordinal level data - data arranged in order, differences between entries are not meaningful; qualitative and quantitative analyses Scale level data - data arranged in order, differences between entries can be calculated; quantitative analyses You ll notice that as soon as you open the data set, another window labeled Output will open as well. This is where your charts and graphs will open, and you ll be able to track your progress throughout your analysis. SPSS is also capable of importing data from other programs. To import from Excel click File > Open > Data and select Open File of Type (EXCEL) from the drop down. Select the Excel file and SPSS will display a dialog box asking for the range of data you wish to import. If the Excel file contains multiple worksheets you must select the one you wish to work with. If you wish to import all of the data from the worksheet then leave the range field blank. 1
PART 1: VARIABLES Goal: Understanding how to view and use the variable view Creating a new variable 1. Navigate to Variable View in your data editor. 2. Scroll down to the bottom, and click in the first empty cell in the Name column to begin entering a new variable. 3. Name your variable whatever you d like. You ll notice that as soon as you name the variable, many of the other variable fields for the new variable will auto-populate. 4. Click through the variable fields to see what each of them do. 2
Transforming a Variable 1. Select the Transform menu 2. Select Compute Variable 3. Name the target variable lincome 4. From the Function group menu select Arithmetic and then Ln 5. Press the up arrow to move it to the Numeric Expression box 6. Select income for the input variable, and press the arrow to add it to the equation. 7. Press okay 3
Combining Different Variables Suppose you want to classify your data by car size. To do this you want to divide your data into four categories. Small, Midsize, Large, and Oversize 1. To categorize the data select the Transform menu and click Recode into Different Variables 2. Select Income from the variable list and name the output variable bracket then click Change 3. Click Old and New Values to define the ranges for each category. 4. Check Range lowest through value, enter 13 and then 1 for the New Value 5. Click Add to store and repeat for the rest of the tax brackets. 6. Click Continue and then OK 7. Select the Values tab for your new variable in the Variable View 15. Enter the names for the different tax brackets 4
PART 2: DESCRIPTIVE STATISTICS OF YOUR DATA Goal: Be able to calculate and store descriptive statistics Running Descriptives 1. From the Analyze menu, click Descriptive Statistics and then click on Descriptives. 2. Select Claim_amount and press the arrow to select the variable for analysis. It will then appear in the Variable(s) window. 3. Select Options. 4. In the Options menu, select Mean (for quantitative variables), maximum, minimum, and standard deviation. Click Continue. 5. Check the Save standardized values as variables box to save the Zscore 6. Click OK. 7. The results of your query will appear in your Output window. Running Frequencies 1. From the Analyze menu, click on Descriptive Statistics, and select Frequencies. 2. Select the desired variable. From here, you have several options: a. Press Statistics to determine which descriptive statistics you d like for your quantitative variables (mean, median, mode, dispersion, etc.). b. Press Charts to determine how your data will be displayed (in a bar chart, pie chart, or histogram). 5
c. Press Format to select the order (ascending or descending) that your information is displayed. 3. Press OK. 4. The results of your query will appear in your Output window. 5. Double-clicking on the bar chart in your Output window will open the Chart editor, which can be used to edit or change chart attributes. Running Crosstabs 1. From the Analyze menu, click on Descriptive Statistics. 2. Select Crosstabs. 3. Select a variable for Row and one or more for Columns. 4. Click Statistics, and select Chi-square. Click Continue. 5. Click Cells. This is where you can choose to display both the observed and expected outcomes, as well as column and row percentages. Check the desired settings, and click Continue. 6. Click OK. 7. In your output editor, you will see your Case Summary, your Crosstabulation Chart, and a chart showing the results of your Chi-square test. The Chi-square chart will tell you if the association between the variables is statistically significant, and you can use the Crosstabulation chart to determine what might be driving that association (if one exists). 6
Graphical Representation 1. SPSS is widely used in part because of its strong graphical capabilities. In order to create charts and graphs we will use a tool called the Chart Builder. 2. Go to Graphs>Chart Builder 3. Click through the pop-up if you re sure of your data or click define variable properties to adjust variable settings. This will automatically check variables and make sure their data is consistent. 4. Use the Gallery tab to select the type of chart you wish to draw 5. From the gallery select one of the available designs and drag it to the canvas above 6. From the Variables list select and drag the X and Y variables to their respective positions 7. From the Element Properties window you can adjust statistics you want displayed. 8. Click Okay to draw the graph. 7
PART 3: STATISTICAL ANALYSES OF YOUR DATA Goal: Understanding how to preform more advanced statistical analyses Running a Correlation Analysis 1. From the Analyze menu, click Correlate, and choose Bivariate. 2. Select the appropriate variables. 3. Under Correlation Coefficients, choose either Pearson or Spearman. 4. Select Two-Tailed under Test of Significance 5. Select Flag Significant Correlations 6. Click Run. Running a Regression Analysis Regression analysis shows the relationship between an independent variable and one or more dependent variables. In order to run a regression analysis, you must first develop a regression model that includes the following steps: Identify a dependent variable Choose the independent variable(s) Run correlations among the variables Run the regression model(s) Interpret the output To run a regression analysis in SPSS: 1. From the Analyze menu, choose Regression. 2. Select Linear. 3. Select lnsales as the dependent variables 4. Select price, engine size, horsepower, wheelbase, width, length, curb weight, fuel capacity and fuel efficiency as the independent variables. 5. From Statistics select estimates, model fit, and Part and Partial correlations 6. Click OK Interpreting the results of your regression analysis: 1. Check the R 2 value. This will tell you the proportion of variation in the dependent variable that is explained by the independent variables. 2. Is the regression model significant? In order to be significant, ANOVA F-test results must be p<0.05. 3. Examine the standardized betas and their significance level. To be significant, p<0.05, or t>2. 4. Use the relative size of the significant standardized betas to arrive at substantive conclusions. 5. Determine the direction of the relationship. 6. Interpret the coefficients of dummy variables. 8
Automatic Linear Modeling SPSS has an advanced feature known as Automatic Linear Modeling, which is used to simplify the process of creating regression models. The data is automatically prepared and a model is created based on what SPSS thinks is best. The most useful part of this feature is the in depth graphical report that is created which is designed to be easier to read than the normal regression output. 1. From the Analyze menu, choose Regression 2. Select Automatic Linear Modeling 3. Move the variables Cost of Claims in Thousands from predictors to fields and then to Target 4. Click Run The first page of the report summarized how accurate the model s predictions are. This is used to compare one model to another. The third page provides a quick overview of the most important predictors 9
The fourth page plots the actual values versus the predicted values from the model. This helps visualize the data and determine if any transformation is needed. The seventh page is a visualization of the ANOVA table. The full table can be accessed by selecting table from the styles menu. 10
The eighth page is a visualization of the coefficients for each predictor. Select table from styles to see the full table. 11