Introduction To SPSS with the interpretation and steps

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Author: Wan Mohamad Asyraf Bin Wan Afthanorhan, Nazim Aimran …

Author: Wan Mohamad Asyraf Bin Wan Afthanorhan, Nazim Aimran
This book helps the readers to conduct their research by using SPSS. Thus, the basic, application, assupmtion, interpretation are also employ in this books. There are a lot of statistical analysis which is normality test, independent t-test, one way anova, two-way anova, association analysis, correlation analysis, and regression analysis.

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  • 1. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 1 Toolbar This toolbar is available in SPSS Data Editor, providing quick and easy access to frequently used features. The following are some of the frequent used tools in the Data Editor. Item Description File Open : allows data files to be opened for analysis. File Save : saves the file in the active window. File Print : prints the file in the active windows. Insert Cases : inserts a case above the case containing the active cell. Insert Variable : inserts a variable to the left of the variable containing the active cell. Value Labels : allows toggling between actual values and value labels in the Data Editor. Select Cases : provides methods for selecting a subgroup of cases based on criteria that include variables and complex expressions. Split Files : splits the data file into separate groups for analysis based on the values of one or more grouping variable.
  • 2. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 2 The box shown above is a dialogue box appears once PASW SPSS 18.0 opened. To open an existing file, choose a file under “open an existing data source menu”. Since we are not going to use this dialogue box, click on the Cancel button to close it. If you want to open existing data, go to File > Open > Data.
  • 3. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 3 You can now open the location where you save the existing SPSS data file. Data Editor Window The window (shown above) is the Data Editor Window. It consists of 12 pull-down PASW STATISTICS menus available for user. The menus are: File, Edit, View, Data, Transform, Analyze, Direct Marketing, Graphs, Utilities, Add-ons, Window and Help. At
  • 4. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 4 the left-bottom of the window, there are options for Data View and Variable View windows. The Data View window is where you will type in your data. However, you must first tell SPSS certain things about your data and you will do this in the Variable View window. Variable View window has 10 columns and they tell the program different things about the measurement values such as whether or not the values are qualitative or quantitative. Defining Variables To enable PASW SPSS 18.0 analysis works, variables of the research must be defined first in the Data Editor Window before entering any data. Click on the left bottom of the PASW STATISTICS Data Editor. We can see Define Variable Dialog box, as shown in the figure below. It consists of: Name Type Width Decimals Label Values Missing Measure
  • 5. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 5 o Nominal variable, is for mutual exclusive, but not ordered, categories. For example, your study might compare achievement between gender; male and female. o Ordinal variable, is one where the order matters but not the difference between values. For example, you might ask patients to express the amount of pain they are feeling on a scale of 1 to 10. Another example would be movie ratings, from * to *****. o Interval variable is a measurement where the difference between two values is meaningful. For example, you might ask the respondent’s salary in order to compare their salary and their expenses. o Ratio data is interval data with a natural zero point. For example, time is ratio since 0 time is meaningful. A weight of 4 grams is twice a weight of 2 grams, because weight is a ratio variable. A temperature of 100 degrees C is not twice
  • 6. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 6 as hot as 50 degrees C, because temperature C is not a ratio variable. A pH of 3 is not twice as acidic as a pH of 6, because pH is not a ratio variable. Variable Name Label Value Label Measure Gender Respondent’s Sex 1 = Male, 2 = Female Nominal Qualification Respondent’s Highest Education Background 1 = SPM, 2 = Diploma, 3 = Bachelor, 4 = Master, 5 = PhD Ordinal Income Respondent’s Monthly Income 1 = ≤ RM999, 2 = RM1000 – RM1999, 3 = RM2000 – RM2999, 4 = RM3000 – RM3999, 5 = ≥ RM4000 Interval Weight Respondent’s Weight Any value Ratio Value Labels Value labels is a label assigned to a particular value of variable. For example, for races label, we might use codes 1= Malay, 2= Chinese, 3= Indian, 4=others. In our case, for gender labels we will use 1= Male and 2= Female. To enter the codes: Type “1” in value box and “male” in label box. Then click “Add”.
  • 7. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 7 Type “2” in value box and “female” in label box. Then click “Add”. End the process “OK”. Missing Values Select Discrete Missing Value button. Then, type “99” (example) or any other codes that will not be used in other variable’s code to replace the missing value. Repeat the step for labelling other variables. For age, the Measure column should be in “scale” since age is an interval measure. The same measure goes to visit, serv_prop, ser_friendly, serv_clean, serv_time, serv_overall variables. For employment and residence, we will use a “Nominal” measure.
  • 8. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 8
  • 9. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 9
  • 10. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 10 Assumption on Parametric Test 1. Data must be normal 2. Data have equal variance 3. Data must be more than 30 cases
  • 11. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 11 Testing Normality Testing normality of our data is prerequisite for inferential statistical technique. The normality test also needed in order to use parametric test on our data. Only normal data can use parametric tests. To check normality for single variable, follow the following steps: Analyze > Descriptive Statistics > Explore Select the variable of interest, for example : age. Then, click on “Plots” button.
  • 12. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 12 Ansure that the “Factor levels together” button is selected in the Boxplot display. Tick on “Stem and Leaf”, “Histogram” and “Normality plots with tests” buttons Click Continue for the results.
  • 13. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 13 Normality Test (Single Variable) Output In the above diagram, the Histogram shows a perfect bell-shaped distribution without skewness to either left or right. Therefore, the age variable can be concluded as normal.
  • 14. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 14 Another way to look at the distribution of our data is by using Normal Q-Q plot. In our case, the points lie along the straight line and show no pattern, therefore the age data distribution can be concluded as normal. To check normality for multiple variables, follow the following steps: Analyze > Regression > Linear
  • 15. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 15 Click “overall quality” and insert it to the Dependent box. Click other observed variable (demographic profile excluded) to the independent variables. Then, click on “Plots” button. Normality Test (Multiple Variables) Output In the above diagram, the Histogram shows a perfect bell-shaped distribution without skewness to either left or right. Therefore, variables in this case study can be concluded as normal.
  • 16. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 16 Another way to look at the distribution of our data is by using Normal Q-Q plot. In our case, the points lie along the straight line and show no pattern, therefore the age data distribution can be concluded as normal. Recode Into Different Variable Recode assigns discrete values to a variable, based solely on the present values of the variable being recoded. You may want to recode variable for easier interpretation or decision making. Transform > Recode Into Different Variables
  • 17. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 17 Step 2: Type “overall” in the Name box. Then, click on “Old and New Values” button. Step 3 : Let us recode the overall perception variable 1 thru 2 = 1 (low / disagree) 3 = 2 (medium / undecided) 4 thru 5 = 3 (high / agree) Then, click on “Continue” button.
  • 18. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 18 Now, new recoded value will appear to the left side of the Data View window. Independent Sample T-Test The Independent Sample T-test procedure tests the null hypothesis that the population mean o a variable is the same for the two groups of cases. It also displays confidence interval for the different between the population means of the groups Step 1: Click analyze > Compare Means > Independent- Sample T- test
  • 19. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 19 Step 2: Transfer the variable into Test Variable(s) box, following gender variable into the Grouping Variable: box (below) Step 3: Click on the Define Groups button and you will need to define which two categories for gender variable. In this case, there are only two categories which are male for Group 1 and female in Group 2. These categories referred as the values 1 and 2. Hence, type the value 1 in the Group 1 and 2 in the Group 2.
  • 20. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 20 Step 4: Click on Continue. Then OK. The following output is appeared: Group Statistics respondent's sex N Mean Std. Deviation Std. Error Mean infrastructure male 7 3.5714 .53452 .20203 female 16 3.6250 .95743 .23936 service quality male 7 3.4286 .53452 .20203 female 16 3.8125 1.04682 .26171 cleanliness quality male 7 4.2857 .75593 .28571 female 16 3.8125 .75000 .18750 queue time male 7 3.1429 .89974 .34007 female 16 2.8750 .88506 .22127 overall quality male 7 4.0000 .81650 .30861 female 16 4.0000 .73030 .18257 The table above shows the means of infrastructure, service quality, cleanliness quality, queue time and overall quality between male and female. By referring on that table, the mean to cleanliness quality for male is the highest following to the overall quality between male and female. The least mean is queue time for female. Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2- tailed) Mean Differen ce Std. Error Differe nce 95% Confidence Interval of the Difference Lower Upper infrastructure Equal variances assumed 1.447 .242 -.138 21 .892 -.05357 .38888 -.86228 .75514 Equal variances not assumed -.171 19.387 .866 -.05357 .31322 -.70827 .60113 service quality Equal variances assumed 3.000 .098 -.911 21 .372 -.38393 .42131 -1.26010 .49224 Equal variances not assumed -1.161 20.237 .259 -.38393 .33061 -1.07306 .30520 cleanliness Equal variances assumed .000 .987 1.389 21 .179 .47321 .34064 -.23519 1.18162
  • 21. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 21 The table above encompasses the result of Levene’s Test for equality of variances and t- test for equality of means. However, most of researchers just focus on the value of significant in t- test for equality of means to determine whether differences exist between male and female students. In this case, all of the variables indicates that p> 0.05 and therefore is not significant. Hence, the null hypothesis is accepted that there is no significant difference between male and female pertaining to all variable included. One-Way Anova Step 1: Click analyze > compare means > One- way ANOVA quality Equal variances not assumed 1.385 11.433 .193 .47321 .34174 -.27550 1.22193 queue time Equal variances assumed .106 .748 .665 21 .513 .26786 .40299 -.57020 1.10592 Equal variances not assumed .660 11.342 .522 .26786 .40571 -.62184 1.15755 overall quality Equal variances assumed .091 .765 .000 21 1.000 .00000 .34256 -.71239 .71239 Equal variances not assumed .000 10.424 1.000 .00000 .35857 -.79456 .79456
  • 22. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 22 Step 2: Transfer the infrastructure (serv_prop) from the list variable into Dependent List following the work place (employment) into the factor. Step 3: Click Post Hoc > Tick LSD Step 4: Click Option > Tick Descriptive, Fixed and Homogeneity of Variance Test
  • 23. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 23 Step 5: Click on Continue, followed by OK. The following result is produced: Descriptives Infrastructure N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Between- Component Variance Lower Bound Upper Bound Government 6 4.1667 1.32916 .54263 2.7718 5.5615 2.00 6.00 Private Sector 6 3.1667 .40825 .16667 2.7382 3.5951 3.00 4.00 GLC Sector 5 3.6000 .54772 .24495 2.9199 4.2801 3.00 4.00 Self Employed 6 3.5000 .54772 .22361 2.9252 4.0748 3.00 4.00 Total 23 3.6087 .83878 .17490 3.2460 3.9714 2.00 6.00 M o d e l Fixed Effects .80677 .16822 3.2566 3.9608 Random Effects .21266 2.9319 4.2855 .06731
  • 24. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 24 The descriptive table shows mean of infrastructure for each categories of work place. The result obtained shows the respondents among government sector are the highest interest on infrastructure towards customer’s satisfaction. Instead, the respondents among private sector are the lowest interest on infrastructure towards customer satisfaction. Test of Homogeneity of Variances infrastructure Levene Statistic df1 df2 Sig. 1.643 3 19 .213 The test of homogeneity shows insignificant since 0.213> 0.05. So, the null hypothesis is accepted and proved that the population variances for each group are approximately equal. This test is required to ensure the probability of the test value is homogeneity or heterogeneity. ANOVA Infrastructure Sum of Squares df Mean Square F Sig. Between Groups 3.112 3 1.037 1.594 .224 Within Groups 12.367 19 .651 Total 15.478 22 The significant value of the ANOVA table is 0.224 which is greater than 0.05. Hence, the null hypothesis is accepted which defines that the infrastructure towards customer satisfaction is not different at work place. However, the ANOVA result does not enough to identify which work place differed with each other. Thus, the LSD is required to determine where the significant lies. The result is shown as below: Multiple Comparisons Infrastructure LSD (I) work place (J) work place Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Government Sector Private Sector 1.00000 * .46579 .045 .0251 1.9749 GLC Sector .56667 .48852 .260 -.4558 1.5892
  • 25. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 25 Self Employed .66667 .46579 .169 -.3082 1.6416 Private Sector Government Sector -1.00000 * .46579 .045 -1.9749 -.0251 GLC Sector -.43333 .48852 .386 -1.4558 .5892 Self Employed -.33333 .46579 .483 -1.3082 .6416 GLC Sector Government Sector -.56667 .48852 .260 -1.5892 .4558 Private Sector .43333 .48852 .386 -.5892 1.4558 Self Employed .10000 .48852 .840 -.9225 1.1225 Self Employed Government Sector -.66667 .46579 .169 -1.6416 .3082 Private Sector .33333 .46579 .483 -.6416 1.3082 GLC Sector -.10000 .48852 .840 -1.1225 .9225 *. The mean difference is significant at the 0.05 level. The outcome illustrates that government sector and private sector have significantly different mean on infrastructure towards customer’s satisfaction. Association Analysis Association Analysis is the weakest measurement of relationship. It is usually used for categorical types of data with nominal and ordinal measurement. Measurement of association is obtained together with cross tabulation between qualitative variables (Nominal and Ordinal). For example, we want to measure the relationship between the gender (male and female) with their attitude towards Mathematics whether high, medium or low. Step 1: Click analyze > Descriptive Statistics > Crosstabs
  • 26. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 26 Step 2: Transfer the cleanliness quality into the Row(s) following the respondent’s sex into the Column(s). Step 3: Click Statistics > Tick Chi-square
  • 27. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 27 Step 4: Click Cell Display > Tick Observed and Unstandardized Residual Step 5: Click on Continue and then press OK.
  • 28. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 28 Crosstab respondent's sex Totalmale female overall quality neither agree nor disagree Count 2 4 6 Residual .2 -.2 agree Count 3 8 11 Residual -.3 .3 strongly agree Count 2 4 6 Residual .2 -.2 Total Count 7 16 23 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square .100 a 2 .951 Likelihood Ratio .100 2 .951 Linear-by-Linear Association .000 1 1.000 N of Valid Cases 23 a. 5 cells (83.3%) have expected count less than 5. The minimum expected count is 1.83. First, look at the Pearson Chi- Square value. Based on the result, the Chi-Square test value is not significant since p-value is greater than 0.05. This result indicates that there is no association exist between gender and the perception towards overall quality.
  • 29. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 29 Correlation Analysis Correlation Analysis is used to measure the relationship between variables. To measure the relationship using correlation analysis, there are two types of correlation coefficient which are the Spearman rank coefficient of correlation and the Pearson product moment coefficient of correlation. The Spearman is appropriate for abnormal data and it is also known as the non parametric version of correlation analysis. The Pearson is appropriate for normal distributed data and it is calculated using the actual data values while Spearman replaces the actual data with ranks. Step 1: Click Analyze > Correlate > Bivariate Step 2: Transfer the five variables from the list variables into the Variables.
  • 30. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 30 Step 3: Click OK. Correlations infrastructure service quality cleanliness quality queue time overall quality infrastructure Pearson Correlation 1 -.408 .046 .111 -.592 Sig. (2-tailed) .054 .835 .613 .003 N 23 23 23 23 23 service quality Pearson Correlation -.408 1 -.147 -.408 -.066 Sig. (2-tailed) .054 .502 .053 .763 N 23 23 23 23 23 cleanliness quality Pearson Correlation .046 -.147 1 -.604 .401 Sig. (2-tailed) .835 .502 .002 .058 N 23 23 23 23 23 queue time Pearson Correlation .111 -.408 -.604 1 .210 Sig. (2-tailed) .613 .053 .002 .335 N 23 23 23 23 23 overall quality Pearson Correlation -.592 -.066 .401 .210 1 Sig. (2-tailed) .003 .763 .058 .335 N 23 23 23 23 23 **. Correlation is significant at the 0.01 level (2-tailed).
  • 31. Ahmad Nazim & W.M.Asyraf Introduction to SPSS hands-0n 31 The table above shows that there is only two significant relationships exist among the variables which are relationship between infrastructure with overall quality and cleanliness with queue time with p-value of 0.03 and 0.02 respectively (<0.05). Both relationships are considered as negative moderate relationship.