SPSS Introduction. Yi Li

Transcription

1 SPSS Introduction Yi Li Note: The report is based on the websites below Aruga, kenakai Introduction to SPSS (ppt). Use Google. SPSS statistical package is one of the most popular statistical packages which can perform highly complex data manipulation and analysis with simple instructions. It is frequently used in the social science. SPSS has four windows--- Data editor; Output viewer; Syntax editor; Script window. Many tasks can be performed with the menus and dialog boxes but some very powerful features are available only with command syntax. Graphs command is used exclusively in SPSS to make graphs. SPSS usually creates commonly used graphics in the fields of social science, such as histograms, scatterplots, and regression line, etc. The Graphs command allows changing aspects of axes, adding text, changing color and font, copying, pasting, and exporting, etc. You could also manually editing graphics for publication. For more information regarding making publication ready graphics, please refer SPSS cannot give you very complicated statistical graphics, such as maps, contour plot, etc. Several graphs produced by SPSS are listed below. Linear Regression Analysis Example: analyze the model salbegin 0 1edu 1) Click Analyze Regression Linear from the main menu. 2) Put Beginning Salary as Dependent and Educational Level as Independent. 3) Clicking OK gives the result.

2 Analysis of Variance 1) Click Analyze Compare means One Way ANOVA 2) Click on the variable that corresponds to your dependent variable.move it into the Dependent List by clicking on the upper arrow button. In this example, the GPA is the variable that we recorded, so we click on it and the upper arrow button. Select the independent variable from the list at the left and click on it. Move it into the Factor box by clicking on the lower arrow button. In this example, the quasiindependent variable is the recoded variable from above, MAJORNUM. 3) Click on the Post Hoc button to specify the type of multiple comparisons that you would like to perform. The Post Hoc dialog box appears. In this example, Tukey test is selected. 4) Click on the Continue button to return to the One-Way ANOVA dialog box. Click on the Options button in the One-Way ANOVA dialog box. The One-Way ANOVA Options dialog box appears. Click on the check box to the left of Descriptives (to get descriptive statistics), Homogeneity of Variance (to get a test of the assumption of homogeneity of variance) and Means plot (to get a graph of the means of the conditions). 5) Click on the Continue button to return to the One-Way ANOVA dialog box. In the One Way ANOVA dialog box, click on the OK button to perform the analysis of variance. Nested variable modeling SPSS allows nesting only in the syntax route. Here is the way to run a 2-factor nested ANOVA. 1) Make sure you have the data organized in columns with subject, nesting variable, nested variable, and dependent measure. 2) From the pull-down menu: Analyze General Linear Models Univariate <click on this> 3) Specify the appropriate variables. For example: DATA is the dependent variable, FACTOR A is fixed, FACTOR B is random. 4) Specify the appropriate ANOVA model. Click on the Model button, then click on Custom, and then add to the model list: FACTOR A. 5) Add whatever options you with to the ANOVA analysis; for example, you might want to specify a multiple comparison procedure for factor A, power, check assumptions, etc. 6) Alter the command language to get the correct text for the nested factor. Click on the Paste button and a syntax window will appear with the program code in it. On the DESIGN subcommand line in the code, specify the nested effect by inserting after the fixed effect (i.e., FACTOR A) the following: FACTOR B (FACTOR A). Run the program, click on the Run Current tool on the toolbar at the top of this syntax window. Non-linear modeling SPSS allows you to apply more sophisticated models with its wide range of non-linear modeling procedure. But this analyzing capability is not as powerful as SAS. These procedures include: binary logistic regression,

3 multinomial logistic regression, nonlinear regression, and constrained nonlinear regression. Take analyzing binary logistic as an illustration. 1) Click Analyze Regression Binary Logistic. 2) Fill in the Dependent box Covariates box according to the data. Click OK. 3) Click Options and check Hosmer-Lemeshow goodness of fit and CI for exp(b) 95%. Continuous/ categorical variable analyses Frequencies Example: make a bar chart for categorical variable gender. 1) Click Analyze Descriptive statistics Frequencies. 2) Click gender and put it into the variable box. 3) Click Charts. 4) Then click Bar charts and click Continue. 5) Finally Click OK in the Frequencies box. Descriptivives Click Analyze Descriptive statistics. The option allows you to analyze other descriptive statistics besides the mean and Std., such as variance, skewness, and kurtosis, etc. Click what you want in the front small box and click continue and OK in the descriptives box. You will be able to see the results of the analysis. Histogram & QQ-plot Example: use data from the SPSS data file Employee data.sav, to assess whether the variable Current salary is normally distributed. 1) Open SPSS data file Employee data.sav. 2) Click Analyze Descriptives Statistics Explore. 3) Select and move the variable Current Salary to the Dependent list area. 4) Click on Plots button to open Explore: Plots dialogue box. 5) In the dialogue box, make sure to select the options Histogram and Normality plots with tests options. 6) Click on Continue to close the dialogue box. 7) Click on OK to run procedure. Random effect models Example: One-way ANOVA with a random factor. Use menu. 1) Analyze General Linear Model Variance Components 2) Choose response into the Dependent Variable box. 3) random effects into the Random Factors box. 4) Click Options ANOVA. Example: Nested mixing random and fixed effects.

4 No nesting permitted in menu route, one syntax example is as follows. glm WGAIN by SIRE DAM /random DAM /design SIRE DAM(SIRE). varcomp WGAIN by SIRE DAM /random DAM /method sstype(1) /design SIRE DAM(SIRE). Chi-squared testing SPSS permits you doing lots of kind of chi-square tests, such as Pearson s chi-square, Chi-square goodness-offit test, Likelihood ratio chi-square test, Mantel-Haenszel chi-square. Take Pearson s chi-square as an example. 1) Click Analyze Descriptive Statistics Crosstabs and Crosstabs dialog box appears. 2) Select one of the variables of interest from the list at the left and move it into the Row(s) box by clicking on the upper arrow button. 3) Select the other variable of interest from the list at the left and move it into the Column(s) box by clicking on the middle arrow button. 4) Click on the Statistics button. The Crosstabs: Statistics dialog box appears. 5) Click in the check box next to the Chi-square option. 6) Click on the Continue button to return to the Crosstabs dialog box. Click on the Cells button. The Crosstabs: Cell Display dialog box appears. 7) To display the expected frequencies, click in the check box next to Expected in the Counts frame. 8) Click on the Continue button to return to the Crosstabs dialog box. Click on the OK button to perform the chi-squared test of independence of categorical variables. The SPSS output viewer appears. Reading in multiple data formats SPSS is designed to handle a wide variety of formats including: Spreadsheet files created with Lotus and Excel, Database files created with dbase, Tab-deliminated and other types of ASCII text files, SPSS data files create on other operating systems, and SYSTAT data files. The following tutorial will indicate how to read these files into a data set in SPSS. 1) From the File menu, select Open. This will open the Open File dialog box. 2) Change the path name to your home directory and open the SPSS folder. This is where the file to be opened should be. 3) Select Excel (*.xls) / Lotus(*.w*) for Lotus files/ *.txt/*.sav/ from the Files of type box. 4) Select the file you want to import. 5) Click Open. This will open the Opening File Options dialog box. Click on the Read variable names dialog box if you want SPSS read variable names from the chosen file. Click OK. This will close the Opening File Options dialog box and will open the file in the Data Editor. The Output Navigator will also be opened.

5 Data transformation (e.g. x Log(x)) Example: Adding a new variable named lnheight which is the natural log of height 1) Click Transform Compute Variable 2) Type in lnheight in the Target Variable box. Then type in ln(height) in the Numeric Expression box. Click OK and a new variable lnheight is added to the table Factor analysis/pca Example: using factor analysis to reproduce the verbal vs. nonverbal distinction, with test WISC-III 1) Click Analyze Data Reduction Factor 2) Select the variables you want to include in the analysis. 3) Select an extraction method and a rotation method. Hit the Extraction button to specify your extraction method. 4) Check the box for a scree plot. This will give you a scree diagram, which is one way to decide how many factors to extract. 5) Look at the section labeled Extract. the default setting is for SPSS to use the Kaiser stopping criterion (i.e., all factors with eigenvalues greater than 1) to decide how many factors to extract. You can set a more conservative stopping criterion by requiring each factor to have a higher eigenvalue. Or, if you already know exactly how many factors you think there will be, you can set the extraction method to a specific Number of factors, and then put the number into this box. 6) Go back to the main dialog box by clicking Continue. Once you re there, click on the button for Rotation to see the second sub-dialog. This dialog allows you to choose a rotation method for your factor analysis. In this case, Varimax (default) is chosen. 7) Check box rotated solution. 8) Hit Continue in the sub-dialog, and then OK in the main dialog to see the output.

6 Sample size calculations There is no sample size calculations function in SPSS Base. Sample Power which is an add-on package for SPSS allows you using the function. Sample Power has functionality similar to SAS s PROC POWER and PROC GLMPOWER plus power analysis for logistic regression. General data manipulation Transposing data structure/matrix Example: Change wide data format into long format 1) Click Data Restructure and restructure wizard appears. Select Restructure selected variables into cases and click on Next. 2) A Variables to Cases: Number of Variable Groups dialog box appears. We select one and click on next. 3) Select the repeated variables and move them to the target variable box 4) After moving the repeated variables into the target variable box, we move the fixed variables into the Fixed variable box, and select a variable for case id in this case, subject. Then we click on Next 5) A create index variables dialog box appears. We leave the number of index variables to be created at one and click on next at the bottom of the box. 6) When the following box appears we just type in time and select Next. 7) When the options dialog box appears, we select the option for dropping variables not selected. We then click on Finish.

7 Filling in missing values In SPSS, there are two kind of missing values. System-missing values are values automatically recognized as missing by SPSS, appearing either a blank or a dot. User-defined missing values are numeric values that need to be defined as missing for SPSS, appearing -9 in cells. You could use Transform command to replace missing values are replaced with their predicted values. Under Transform, Replace Missing Cases, supports five estimation methods: replacement of missing values with the series mean, by the mean or median of nearby points, or linear interpolation between prior and subsequent known points, interpolating between the adjacent valid values above and below the missing one, or substitution of the linear regression trend value for that point. As for complicated imputation method, such as MCMC and monotone, you have to use SPSS Missing Values package. Deleting rows & columns Under Data Editor, click the row/or column you want to delete. The row/or column will darken. Right click the mouse and choose Clear from the pop-up list. The row/or column deletes. Smoothing There is no smoothing function in SPSS Base. You have to use SPSS Trends to execute the function. No more complicated tasks are in SPSS. More sophisticated programming tasks