Introduction to SPSS-01!07!11

Introduction to SPSS Statistical Package for the Social

Sciences

Most of the examples presented here use the data file demo.sav. This data file is a ficticious survey of several thousand people, containing basic demographic and consumer information.

If you are using the Student version of SPSS, your version of the demo.sav is a representative sample of the original data file, reduced to meet the 1,500 case limit. Results that you obtain using that data file will differ from the results shown here.

All sample files used in these examples are located in the tutorial\sample files folder within the folder in which SPSS is installed.

SPSS- Statistical Package for the Social Sciences

When you start a session, you see the Data Editor window.


Before you can analyze data, you need some data to analyze.


To open a data file: From the menus, choose: File Open Data


Descriptive value labels are now displayed. This makes it easier to interpret the responses.


The Analyze menu contains a list of general reporting and statistical analysis categories. Most of the categories are followed by an arrow, which indicates that there are several analysis procedures available within the category; they will appear on a submenu when the category is picked. We'll start with a simple frequency table (table of counts).


Descriptive Statistics

From the menus, choose: Graphs

Bar

Graphical Analysis

For example, you could create a chart that shows the relationship between wireless telephone service and PDA (personal digital assistant) ownership.

Graphical Analysis

Graphical Analysis

This section uses the files demo.sav and bhelptut.spo.

Data Transformation

Use the Transform menu to calculate new values and variables, and to recode data.

Rather than typing all your data directly into the Data Editor, you can read data from applications like Microsoft Excel.

Getting Data from Excel

Select Excel (*.xls) from the Files of Type listbox.


The Opening Excel Data Source dialog box is displayed, allowing you to specify whether or not variable names are included in the spreadsheet, as well as the cells that you want to import.


The data now appear in the Data Editor, with the column headings used as variable names.


Data can be typed into the Data Editor, which may be useful for small data files or making minor edits to larger data files.

Entering Data in SPSS

Click the Variable View tab at the bottom of the Data Editor window.

Define the variables that are going to be used. In this case, only three variables are needed: age, marital status, and income.


Non-numeric data, such as strings of text, can also be entered in the Data Editor.


Summary Measures

Summary Measures

Click Charts.

Summary Measures

Click Statistics.

Summary Measures

Summary Measures

What factors affect the products that people buy? The most obvious is probably how much money people have to spend. In this example, we'll examine the relationship between income level and PDA (personal digital assistant) ownership.

Cross tabulation Tables

From the menus choose:Analyze Descriptive Statistics Crosstabs


The purpose of a crosstabulation is to show the relationship (or lack thereof) between two variables.

A number of tests are available to determine if the relationship between two crosstabulated variables is significant. One of the more common tests is chi-square. One of the advantages of chi-square is that it is appropriate for almost any kind of data.

Chi-square Test

Open the Crosstabs dialog box again. Click Statistics.

Chi-square Test

Chi-square Test

Pearson chi-square tests the hypothesis that the row and column variables are independent. The actual value of the statistic isn't very informative.

Chi-square Test

In this case, the significance value is so low that it is displayed as .000, which means that it would appear that the two variables are, indeed, related.

The significance value (Asymp. Sig.) has the information we're looking for. The lower the significance value, the less likely it is that the two variables are independent (unrelated).

Chi-square Test

Several categorical variables in the data file demo.sav are, in fact, derived from scale variables in that data file. For example, the variable inccat is simply income grouped into four categories -- and here's how it was done...

Transformation of a Variable

From the menus in the Data Editor window choose: Transform Recode Into Different Variables


Select Household income in thousands (income) for the Numeric Variable --> Output Variable list. Enter inccat2 for the output variable name. Enter Income category for the output variable label. Click Change. Click Old and New Values.


In the Old Value group, click the second Range button and enter 24.999 in the text field after Lowest through.

In the New Value group, click Value and enter 1. Click Add.


Click Continue, and then click OK in the main dialog box.


Pie Chart

In this example, we'll create a simple pie chart that shows how many respondents have internet service at home.From the menus choose: Graphs Pie

Pie Chart

In order to increase sales, motor vehicle design engineers want to focus their attention on aspects of the vehicle that are important to customers--for example, how important is fuel efficiency with respect to sales? One way to measure this is to compute the correlation between past sales and fuel efficiency.

Information concerning various makes of motor vehicles is collected in car_sales.sav . Use Bivariate Correlations to measure the importance of fuel efficiency to the salability of a motor vehicle.

Correlation Analysis


To run a correlations analysis, from the menus choose: Analyze Correlate Bivariate...

The correlation reported in the table is negative(!), although not significantly different from 0, which suggests that designers should not focus their efforts on making cars more fuel efficient because there isn't an appreciable effect on sales.


Linear regression is used to model the value of a dependent scale variable based on its linear relationship to one or more predictors.

Regression Analysis

The Nambe Mills company has a line of metal tableware products that require a polishing step in the manufacturing process. To help plan the production schedule, the polishing times for 59 products were recorded, along with the product type and the relative sizes of these products, measured in terms of their diameters.

This information can be found in polishing.sav . Use linear regression to determine whether the polishing time can be predicted by product size.

Before running the regression, you should examine a scatterplot of polishing time by product size to determine whether a linear model is reasonable for these variables.

To produce a scatterplot of time by diam, from the menus choose: Graphs Scatter...

Regression Analysis

Click Define.

Regression Analysis

Select time as the y variable and diam as the x variable. Click OK.

These selections produce the scatterplot.

Regression Analysis

To see a best-fit line overlaid on the points in the scatterplot, activate the graph by double-clicking on it. From the Chart Editor menus choose: Chart Options...

Regression Analysis

Select Total in the Fit Line group.

Click OK.

Regression Analysis

The resulting scatterplot appears to be suitable for linear regression, with two possible causes for concern.

Regression Analysis

To run a linear regression analysis, from the menus choose: Analyze Regression Linear...

Regression Analysis

Select time as the dependent variable. Select diam as the independent variable. Select type as the case labeling variable.

Click Plots.

Regression Analysis

This table shows the coefficients of the regression line.

It states that the expected polishing time is equal to 3.457 * DIAM - 1.955. If Nambe Mills plans to manufacture a 15-inch casserole, the predicted polishing time would be 3.457 * 15 - 1.955 = 49.9, or about 50 minutes.

Regression Analysis

The ANOVA table tests the acceptability of the model from a statistical perspective.

The Regression row displays information about the variation accounted for by your model.

The Residual row displays information about the variation that is not accounted for by your model.

The regression and residual sums of squares are approximately equal, which indicates that about half of the variation in polishing time is explained by the model.

Regression Analysis

The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance.

The ANOVA table tests the acceptability of the model from a statistical perspective.

While the ANOVA table is a useful test of the model's ability to explain any variation in the dependent variable, it does not directly address the strength of that relationship.

Regression Analysis

The model summary table reports the strength of the relationship between the model and the dependent variable.

R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship.

Adjusted R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. It shows that about half the variation in time is explained by the model.

Regression Analysis

THANK YOU

Introduction to SPSS-01!07!11

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