Introduction to SPSS: Basic Analyses

Transcription

1 Introduction to SPSS: Basic Analyses I. Obtaining Descriptive Statistics of the Data In SPSS, there are three different ways of obtaining descriptions of the data: Descriptives Frequencies Crosstabs 1. Running Descriptives: c. Click Descriptives. d. Select the required variables. e. Click Options. f. Select Mean (only for quantitative variables), Maximum, Minimum and Standard Deviation. g. Click Continue. Click OK. On average, how do respondents compare the Visa credit card offer to the card they use most often? (Hint: Determine the average value for variable q30f). c. Click Descriptives. d. Select q30f. e. Click Options. f. Select Mean, Std Deviation, Minimum, Maximum. 1

2 g. Click Continue. Click OK. NOTE: SPSS gives the output of the analysis in a separate output window. You can use the tables and charts in the output window by simply copying and pasting it to a Word document. If you will be using the same output later, you need to save it in your computer by clicking on the Save button (or alternatively choosing Save option from File menu). 2. Running Frequencies: This procedure also provides graphical displays for the description of data. c. Click Frequencies. d. Select the required variable. e. Options available are as follows: Statistics for descriptive statistics of quantitative variables Charts for graphical display of data Format to specify the order in which the results are to be displayed f. Click Continue. Click OK. Which card do the respondents use most often? (Hint: Use variable q3 for answer.) c. Click Frequencies. d. Select q3. e. Check Display frequency tables. f. Click Statistics. g. Choose Minimum and Maximum. h. Click Continue. 2

3 i. Click Charts. j. Select Bar Charts. Select Percentages. k. Click Continue. Click OK. 3. Running Crosstabs: This is useful in examining the way different variables maybe associated with each other. This is in general restricted to non-metric measures since metric data can be judged by other procedures such as correlations. c. Select Crosstabs. d. Choose one or more variables for Row and one or more variables for Column. e. Select Statistics for tests of association. f. Select Cells for observed and expected values. g. Select Format. h. Click OK. Is there a relationship between respondents agreeing to a change in their attitude towards the use of credit cards in the past year and the type of card they use most often? (Hint: Use variables q3 and q20 for answer). 3

4 c. Select Crosstabs. d. Choose variable q3 for column and variable q20 for row. e. Click Statistics. Select Chi-Square tests. f. Click Continue. g. Click Cells. Select Column under Percentages. h. Click Continue. Click OK. Interpretation: To interpret the output, you need to look at the table called Chi-Square Tests, which tell you whether the relation is statistically significant or not. II. Transforming Variables This is required when variables need to be condensed for ease of further analysis, when they need to be reverse coded for negative responses or for determining the average response of a question. 1. Recoding: We need to recode the variables for the following purposes: Reassigning values of existing variables Creating a new variable by collapsing ranges of existing values into new values Reverse coding negative items a. Choose Transform from menu. b. Select Recode. c. Select Into Different Variables. d. Select required variables (in case of multiple variables they must of the same type that is either all numeric or all string). e. Enter an output name for each new variable and select Change. 4

5 f. Click Old and New Values. Specify an old value and a new value. Click Add. g. Click OK. Check that a new column is created at the end of the data editor file. SPSS Tutorial Part II Collapse responses to variable q1i into 3 categories, Agree, Disagree, and Neither agree nor disagree a. Choose Transform from the menu. b. Select Recode. c. Select Into different variables. d. Select variable q1i. e. Under Output Variable, go to Name. Type newq1i. Click Change. f. Click Old and New Values. g. Recode as follows: Old Values New Values SYS MISSING NOTE: When entering old and new values you have two options. You can enter each value one by one using Value option under both old and new values. e.g. Old Value: Value: 5 New Value: Value: 1 You can specify a range under old values. e.g. Old Value: Range: 4 through 5 New Value: Value: 1 Don t forget to click Add each time you enter an old and new value. h. After you finish all old and new values, click Continue. Click OK. Check to see that the new variable has been added as a new column. Creating dummy variables to represent a categorical variable: You can use recoding to create dummy variables. (You will need to execute this step before your regression analysis.) 5

6 Create dummy variables from the level of education of the primary wage earner of a household (Hint: Use answers for q36). Create dummy variables for three categories, Less than high school graduates and high school graduates, Some college and college graduates, Any postgraduate work. Since you have three different categories in your analysis, you will need two dummy variables. Use Old and New Values in the Recoding to create dummy variables as follows: Dummy Variables Old and New Values High School Less than high school (1) AND high school (2) = 1 All else (3,4,5) = 0 College Some College (3) AND College graduates (4) = 1 All else (1,2,5) = 0 2. Computing Variables: This is another method of collapsing data but it allows the computation of variables based on numeric transformations of other variables. a. Choose Transform from menu. b. Click Compute. c. Type a name for the target variable. d. To build an expression, either use functions that are listed in the Function area or type directly in the Numeric Expression field. When you use functions from the Function list, you need to fill in the parameters indicated by the question marks. e. Click OK and a new column will be created at the end of the data editor file with the new variable. Find the average response to the importance people attach to their money, credit cards and material possessions. (Hint: Use variables l, m and n from question 1). a. Choose Transform from the menu. b. Click Compute. c. Type in Target Variable as q1lmn. d. Select Mean from Functions. e. Select variables q1l, q1m and q1n. 6

7 f. Click OK. Check that a new variable is created at the end of the data editor file. III. Statistical Analysis 1. Correlation Analysis: There are two types of correlation: Spearman for ordinal data Pearson for metric data (interval and ratio data) a. Choose Analyze from menu. b. Choose Correlate. c. Choose Bivariate. d. Select required variables. e. Choose Spearman or Pearson under Correlation Coefficients. f. Select Two Tailed under test of significance. g. Select Flag Significant Correlations. h. Paste and Run. Find the level of correlation between variables q1(c, d and e). a. Choose Analyze from menu. b. Choose Correlate. c. Choose Bivariate. d. Select question 1(c,d and e). e. Choose Pearson under Correlation Coefficients. f. Select Two-tailed under Test of Significance. 7

8 g. Check Flag significant Correlations. h. Click OK. 2. Regression Analysis: In order to run a regression analysis in SPSS, you first have to come up with a regression model. A thorough regression analysis follows the following steps: Identify dependent variable. Choose the independent variables. Run correlations among variables. Run regressions model(s). Interpret the output. The regression model is run in SPSS as follows: b. Choose Regression. c. Select Linear. d. Select dependent variable. e. Select independent variables. f. Click OK. Analyze the impact of an individual s attitudes towards shopping on the level of satisfaction with his or her present financial situation. (Hint: According to the corresponding regression model, dependent variable is q1i 8

9 and independent variables are q1c, q1d, q1f, q1g, q1n, q35 and q39. Note that it is assumed here that you have already run a correlation between these variables to see whether they are correlated.) Need to run correlation to check whether there is multicollinearity, and then dummy code the variables q35 and q39. b. Choose Regression. c. Select Linear. d. Select dependent variable (q1i). e. Select independent variables q1c, q1d, q1f, q1g, q1n, q35 and q39. f. Click OK. How to interpret results of regression analysis: Check R 2 value to see what proportion of variability in the dependent variable is explained by the independent variables. The regression model must be significant. ANOVA F-test results must have p<.05. Examine the standardized betas and their significance level. Check if p<.05 or t>2. Use the relative size of the significant standardized betas to arrive at substantive conclusions. Examine the direction of the relationship. Interpret the coefficients of dummy variables Factor analysis Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Used for: Data reduction: come up with a new, smaller set of variables that express what is common among the original variables. Substantive interpretation: identify the constructs or dimensions that underlie the observed variables. There are three steps in Factor Analysis: 1. Determining the factors to retain: the amount of variation explained by each factor as indicated by its eigenvalue. Factors with eigenvalue greater than 1 are retained. 2. Identifying which variables belong to which factors: 9

10 Find the highest loading for each variable and highlight it. Examine loadings for significance (cutoff value of 0.3 or 0.35) within each factor. Identify all variables that do not have significant loadings on any factor (hopefully none) or have significant loading on multiple factors (known as cross-loading) and eliminate them from further analysis. 3. Naming the factors: Focus on the significant loadings and try to name the factors on the basis of what those variables that load on one factor seem to have in common. SPSS generates several different tables and graphs. However, not all of them are useful to us. Specifically, we have to examine only two of them: 1. Total Variance Explained It identifies factors with Eigenvalues greater than 1.0. Shows total variance captured by valid factors. 2. Rotated Component Matrix Identifies factor loadings after rotation. It is the matrix used for interpretation of factor names. An Example of Factor Analysis Are there distinct dimensions to the interests and opinions of respondents in Question 1 of the Rockingham Visa Case? Steps: Analyze Data Reduction Factor Select variables q1a to q1o and move to Variable box Descriptives select Initial Solution and continue Extraction select Principal Components, Correlation Matrix, Unrotated Factor Solution, Eigen Values over 1 Rotation select Varimax Scores select Display Factor Score Coefficient Matrix Options o Missing Values select Exclude Cases Listwise o Coefficients Display Format click on Sorted by Size OK 10

11 Total Variance Explained Comp onent Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Rotated Component Matrix(a)- Please note that I am showing all coefficients (not just those >.4) Component I attach great importance to money I attach great importance to credit cards I attach great importance to material possessions Money may not be everything, but it's got a big lead over whatever is second Buying things gives me pleasure Shopping is fun Important for me to be fashionable Buy things to make myself feel better Satisfied with present financial situation During last 3 yrs my financial situation has gotten worse Money can't buy happiness I buy things even though I can't afford them Make only minimum payments on credit cards You can a lot about people by the CCs they use I read all offers that I receive through the mail Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 7 iterations. Question: How would you name these factors? 11