Spss software
- 1. What is SPSS? “Statistical Package for the Social Science” One of the most popular statistical packages which can perform highly complex data and analysis with simple instructions.
- 2. SPSS Inc. founded in 1968 2009 acquired by IBM SPSS is now owned b y IBM
- 3. Opening SPSS Start → All Programs → SPSS Inc→ SPSS 21.0 → SPSS 21.0
- 4. Opening SPSS The default window will have the data editor There are two sheets in the window: 1. Data view 2. Variable view
- 6. Variable View window Name The first character of the variable name must be alphabetic less than 64 characters. Spaces are NOT allowed.
- 7. Type The two basic types of variables that you will use are numeric and string.
- 8. Width Width allows you to determine the number of characters SPSS will allow to be entered for the variable
- 9. Decimals Number of decimals It has to be less than or equal to 16 3.14159265
- 10. Label Describing the variable You can write characters with spaces up to 256 characters
- 11. Values(coding) This is used and to suggest which numbers represent which categories when the variable represents a category
- 12. Defining the value labels Click the cell in the values column For the value, and the label, you can put up to 60 characters. After defining the values click add and then click OK.
- 13. MISSING Missing value: • Not captured in the data set: errors in feeding ... •Empty value - No value in the population
- 14. Measure Nominal, Ordinal and scale (Interval & ratio )
- 15. Sample Questionnaire Gender : Male / Female Age : below 25/ 26-35/ 36-45/ 46-55 Category : student / Faculty / research scholars How do you come to know about this program(multiple choice) Mail / WhatsApp/ friends/website
- 16. Data entry Exercise Data entry
- 17. Saving the data To save the data file -click ‘file’ and click ‘save as.’ You can save the file in different forms by clicking “Save as type.” Click
- 18. Opening the sample data Open ‘Employee data.sav’ from the SPSS Go to “File,” “Open,” and Click Data
- 19. Descriptive statistics Percentage analysis Frequency analysis Charts/graphs Mean , median and mode ( Numercial scale) Cross tabs
- 20. Descriptive statistics Frequencies Click ‘Analyze,’ ‘Descriptive statistics,’ then click ‘Frequencies’
- 21. Frequencies Click gender and put it into the variable box. Click ‘Charts.’ Then click ‘Bar charts’ and click ‘Continue.’ Click Click
- 23. Descriptives Click ‘Analyze,’ ‘Descriptive statistics,’ then click ‘Descriptives…’ Select the variables Click Options Click
- 25. Cronbach's alpha is the most common measure of internal consistency
- 26. STEPS Click Analyze > Scale > Reliability Analysis...
- 29. Transfer the variables Qu1 to Qu9 into the Items:
- 30. Leave the Model: set as "Alpha", which represents Cronbach's alpha in SPSS Statistics. If you want to provide a name for the scale, enter it in the Scale label.
- 32. Click the CONTINUE button, Click the OK button to generate the output.
- 33. OUTPUT
- 35. Cronbach's Alpha .00-1.0 00 = no consistency in measurement (negative consistency) 1.0= perfect consistency in measurement(positive consistency) .70= 70% of the variance in score is reliable (acceptable level .7)
- 36. Sample -1 case
- 37. Testing hypotheses about two means
- 38. Cont.. The independent-samples t-test compares the means between two unrelated groups on the dependent variable. Eg : Did male and female in the study have same academic score?
- 39. Hypotheses Ha = Male and female have same academic score. Ho = Male and female don’t have same academic score.
- 40. Rule of Thumb If sig. value is > .05 = Accept Ho If sig. value is < .05 = Accept Ha
- 41. Conditions : dependent variable should be measured on a interval or ratio scale. Independent variable should consist of two categorical group.(Nominal ) There needs to be homogeneity of variances. Levene’s test for homogeneity
- 46. OUTPUT If the levene’s sig value > .05 t-test should be based on equal variance.
- 48. Paired Samples t Test Detect a difference between the mean of two dependent variables. Eg : Measure the employees performance before and after training .
- 49. Conditions Dependent variable should be interval or ratio scale. Two groups(dependent) Normality Homogeneity of variances
- 51. Click on OK
- 53. The sig. value is .000 which is < .05 Reject Ho There is a significant difference between before and after training. We can conclude that the training program is effective
- 54. Paired Samples t Test Sample .2
- 55. Closeness of relationship between two variables
- 56. Eg How strongly the sales are related with advertising expenditure? Work experience and their output. Exam performance and preparation time. Online test score and students work experience.
- 57. Conditions Two variables should be measured at the interval or ratio level.
- 61. Output
- 62. Pearson correlation coefficient, r, is 0.777, and that this is statistically significant (p < 0.05).
- 63. Exercise How strongly online test score is related to work experience?
- 65. ANOVA is used to determine whether there are any significant differences between the means of more than two independent groups. understand whether exam performance differed based on their community type ( urban, rural, semi-urban)
- 66. Conditions : Dependent variable should be measured at the interval or ratio level. Independent variable should consist of three or more categorical group. Normality
- 70. Post hoc ANOVA result will tell us whether there is a difference among at least two of the group , but it will not tell us which of the group exhibited this difference. Post hoc help us to identify which group are different.
- 72. Click continue then options
- 73. Click continue then ok
- 74. Exercise 1 Do students in each of the three groups of community type have similar academic score?
- 75. Two way ANOVA SIGNIFICANT DIFFRENCE BETWEEN TWO VARIABLES . EXAMPLE Scores – dependent Age - Independent Gender – Independent
- 76. Conditions : Dependent variable should be ratio scale Independent variables should be nominal scale. Two independent variable Homogeneity (equality) of variances(levene’s model)
- 77. Hypotheses H1= Gender have no significant effect on students academic score H2= age have no significant effect on score. H3 = gender and age interaction have no significant effect on score.
- 78. Analyse / General Linear Model / Univariate
- 79. Dependent Variable : Score Fixed Factors: duration + modality
- 80. Displays overall mean, means for each level of duration, mean for each level of modality and the means for each combination of duration by modality (= the interaction means). Means
- 81. Levene’s test. This significant result means the assumption of equal group variances has not been met. Output
- 82. In this case the analysis is not valid !. Output
- 84. Exercise : sample 3 Does the place of operations influences ROCE? Does the Industry type influences ROCE?
- 86. Eg Is there a significant association between mentor evaluation and community type?
- 87. Conditions Two variables should be measured at an ordinal or nominal level Minimum expected cell count should be 5 (using cross tap)
- 92. Click continue then OK
- 93. OUTPUT
- 95. p = .485 there is no statistically significant association between two variables. P> .05 Accept Ho
- 96. The next step up after correlation
- 97. It is used when we want to predict the value of a variable(dependent) based on the value of another variable.(independent). Use multiple regression if independent variable are more than one. Example: How well can we predict a test score based on work experience?
- 98. Conditions variables should be measured at the interval or ratio scale. There needs to be a linear relationship between the two variables(use scatter plot) No significant outliers (observed data should not be deviated from rest of the data). One independent and one dependent variable.
- 99. Eg Exam performance can be predicted based on revision time. Cigarette consumption can be predicted based on smoking duration. sales can be predicted based on ad.expenditure.
- 104. R is correlation value R square = How much of the total variation in the dependent variable can be explained by the independent variable. 76.2% which is very large.
- 106. Price = 8287 + 0.564(Income)
- 107. How well can we predict a test score if we know the work experience?
- 108. EXAMPLESALES (UNITS) 120 140 190 200 220 170 180 190 220 250 ADV.EX (in 1000s) 12 15 17 20 23 23 21 22 21 25
- 109. An extension of simple linear regression
- 110. Cause and effect relationship Eg you could use multiple regression to understand whether exam performance can be predicted based on revision time, lecture attendance and work exp.
- 111. It helps you to determine the overall fit (variance explained) of the model . Eg 2: sales can be predicted by sales men exp. And ad exp.
- 112. Conditions Dependent variable should be an interval scale. You have two or more independent variables. There needs to be a linear relationship between independent and dependent variable.(scatter plot) Your data must not show multicollinearity(R) There should be no significant outliers
- 118. Determining how well the model fits
- 119. R, the multiple correlation coefficient, R2 value ,which is the proportion of variance in the dependent variable that can be explained by the independent variables.
- 121. The table shows that the independent variables statistically significantly predict the dependent variable, F(4, 95) = 32.393, p < .0005 (i.e., the regression model is a good fit of the data).
- 122. Statistical significance of the independent variables
- 123. Exploratory Factor Analysis Basic Concepts What is Factor Analysis? The basic assumption of factor analysis is that for a collection of observed variables there are a set of underlying variables called factors (smaller than the observed variables), that can explain the interrelationships among those variables.
- 124. Exploratory Factor Analysis Basic Concepts Sample Size to Run a Factor Analysis 10-15 Respondents per variable 300 or more than 300 is a good sample size Run Kaiser-Meyer-Olkin Measure of Sampling Adequacy(KMO) test
- 139. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .930 Bartlett's Test of Sphericity Approx. Chi- Square 19334.49 2 df 253 Sig. .000 Interpreting Exploratory Factor Analysis Output
- 140. Communalities Initial Extraction Statiscs makes me cry 1.000 .435 My friends will think I'm stupid for not being able to cope with SPSS 1.000 .414 Standard deviations excite me 1.000 .530 I dream that Pearson is attacking me with correlation coefficients 1.000 .469 I don't understand statistics 1.000 .343 I have little experience of computers 1.000 .654 All computers hate me 1.000 .545 I have never been good at mathematics 1.000 .739 My friends are better at statistics than me 1.000 .484 Computers are useful only for playing games 1.000 .335 I did badly at mathematics at school 1.000 .690 People try to tell you that SPSS makes statistics easier to understand but it doesn't 1.000 .513 I worry that I will cause irreparable damage because of my incompetenece with computers 1.000 .536 Computers have minds of their own and deliberately go wrong whenever I use them 1.000 .488 Computers are out to get me 1.000 .378 I weep openly at the mention of central tendency 1.000 .487 I slip into a coma whenever I see an equation 1.000 .683 SPSS always crashes when I try to use it 1.000 .597 Everybody looks at me when I use SPSS 1.000 .343 I can't sleep for thoughts of eigen vectors 1.000 .484 I wake up under my duvet thinking that I am trapped under a normal distribtion 1.000 .550 My friends are better at SPSS than I am 1.000 .464 Interpreting Exploratory Factor Analysis Output
- 141. Total Variance Explained Compon ent Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulati ve % Total % of Variance Cumulati ve % Total % of Variance Cumulati ve % 1 7.290 31.696 31.696 7.290 31.696 31.696 3.730 16.219 16.219 2 1.739 7.560 39.256 1.739 7.560 39.256 3.340 14.523 30.742 3 1.317 5.725 44.981 1.317 5.725 44.981 2.553 11.099 41.841 4 1.227 5.336 50.317 1.227 5.336 50.317 1.950 8.476 50.317 5 .988 4.295 54.612 6 .895 3.893 58.504 7 .806 3.502 62.007 8 .783 3.404 65.410 9 .751 3.265 68.676 10 .717 3.117 71.793 11 .684 2.972 74.765 12 .670 2.911 77.676 13 .612 2.661 80.337 14 .578 2.512 82.849 15 .549 2.388 85.236 16 .523 2.275 87.511 17 .508 2.210 89.721 18 .456 1.982 91.704 19 .424 1.843 93.546 20 .408 1.773 95.319 21 .379 1.650 96.969 22 .364 1.583 98.552 23 .333 1.448 100.000 Interpreting Exploratory Factor Analysis Output
- 142. Rotated Component Matrixa Component 1 2 3 4 I have little experience of computers .800 SPSS always crashes when I try to use it .684 I worry that I will cause irreparable damage because of my incompetence with computers .647 All computers hate me .638 Computers have minds of their own and deliberately go wrong whenever I use them .579 Computers are useful only for playing games .550 Computers are out to get me .459 I can't sleep for thoughts of Eigen vectors .677 I wake up under my duvet thinking that I am trapped under a normal distribution .661 Standard deviations excite me -.567 People try to tell you that SPSS makes statistics easier to understand but it doesn't .473 .523 I dream that Pearson is attacking me with correlation coefficients .516 I weep openly at the mention of central tendency .514 Statiscs makes me cry .496 I don't understand statistics .429 I have never been good at mathematics .833 I slip into a coma whenever I see an equation .747 I did badly at mathematics at school .747 My friends are better at statistics than me .648 My friends are better at SPSS than I am .645 If I'm good at statistics my friends will think I'm a nerd .586 My friends will think I'm stupid for not being able to cope with SPSS .543 Everybody looks at me when I use SPSS .428 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Interpreting Exploratory Factor Analysis Output
- 143. Factors Fear of Compute rs Fear of Statistics Fear of Maths Peer Evaluatio n I have little experience of computers .800 SPSS always crashes when I try to use it .684 I worry that I will cause irreparable damage because of my incompetence with computers .647 All computers hate me .638 Computers have minds of their own and deliberately go wrong whenever I use them .579 Computers are useful only for playing games .550 Computers are out to get me .459 I can't sleep for thoughts of Eigen vectors .677 I wake up under my duvet thinking that I am trapped under a normal distribution .661 Standard deviations excite me -.567 People try to tell you that SPSS makes statistics easier to understand but it doesn't .473 .523 I dream that Pearson is attacking me with correlation coefficients .516 I weep openly at the mention of central tendency .514 Statiscs makes me cry .496 I don't understand statistics .429 I have never been good at mathematics .833 I slip into a coma whenever I see an equation .747 I did badly at mathematics at school .747 My friends are better at statistics than me .648 My friends are better at SPSS than I am .645 If I'm good at statistics my friends will think I'm a nerd .586 My friends will think I'm stupid for not being able to .543 Interpreting Exploratory Factor Analysis Output