Spss basic1

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Spss basic1

  1. 1. SPSS ACTIVITY
  2. 2. <ul><li>SPSS is the most commonly used statistical software for social sciences. </li></ul><ul><li>It is powerful (perhaps too powerful) but makes it easy to compute the scores from a set of data and do the statistical analysis. </li></ul><ul><li>Let’s start with a quick run through of the things you should know. </li></ul>
  3. 3. 1. We will start by using the SPSS DATA EDITOR To define and enter data
  4. 4. <ul><li>Variable view </li></ul><ul><li>Define IVs & DVs here. </li></ul><ul><li>Use separate line for each & give sensible names. </li></ul><ul><li>Decide format of data: String = text, numeric = numbers. Numeric is generally the best format. </li></ul>
  5. 5. <ul><li>Data view </li></ul><ul><li>Insert data for variables here. </li></ul><ul><li>Data is input in columns under appropriate variable names. </li></ul>
  6. 6. <ul><li>You should be able to calculate descriptive statistics with the Frequency..., Descriptives..., Explore... And Crosstabs... function. </li></ul>
  7. 7. 1. SPSS DATA EDITOR To define and enter data
  8. 9. <ul><li>Use to check on errors in typing the data and for screening to detect out of range data. </li></ul><ul><li>Select Analyze menu, click on Descriptive Statistics and then Frequencies </li></ul><ul><li>You will get a Frequency dialog box </li></ul><ul><li>Select the variables and send to variables box </li></ul><ul><li>Click OK </li></ul>
  9. 10. <ul><li>Checking on normality: histogram, stem-and-leaf plot, boxplot, and others </li></ul><ul><li>Select Analyze menu, click on Descriptive Statistics and then Explore to get the Explore dialog box </li></ul><ul><li>Select the variables you require and click on to the dependent list </li></ul><ul><li>Click on the plots to obtain the Explore Plots sub-dialogue box </li></ul><ul><li>Click on the Histogram check box and the normality-plots with tests </li></ul>
  10. 11. <ul><li>Variables maybe distributed in varying degrees of skewness hence need to transformed. </li></ul><ul><li>Variables also need to be transform as intended by the researcher as stated in the objectives. </li></ul><ul><li>TRANSFORM- COMPUTE </li></ul><ul><li>TRANSFORM – RECODE </li></ul><ul><ul><li>Recoding negatively worded scale items </li></ul></ul><ul><ul><li>Collapsing continuous variables </li></ul></ul><ul><ul><li>Replacing missing values </li></ul></ul>
  11. 12. <ul><li>Frequency/percentage table, </li></ul><ul><li>Pie or bar Charts, </li></ul><ul><li>Histogram </li></ul><ul><li>Frequency Polygon, </li></ul><ul><li>Cross-tabulation </li></ul><ul><li>Scatter diagram </li></ul><ul><li>Mean, Median, Mode, Maximum, Minimum </li></ul><ul><li>Range, Variance, Standard Deviation, Coefficient of variation, Standard Scores </li></ul>
  12. 13. GENDER MATHS-PMR MATHS-FINAL PRETEST SCORE POSTTEST SCORE PROC CONC 2 B A 6.0 14.0 19.0 8.0 2 B C 7.0 14.0 19.0 8.0 2 A A 1.0 14.5 17.0 9.0 2 A C 7.0 14.5 19.0 9.0 2 B C 7.0 13.5 18.0 8.0 2 B C 10.0 12.0 20.0 6.0 2 A A 8.0 15.0 19.0 9.0 2 C B 6.0 9.0 18.0 3.0 2 B B 8.0 10.0 17.0 5.0 2 A A 10.0 15.0 19.0 10.0 1 C D 1.0 17.0 18.0 11.0 1 B C 6.0 16.0 18.0 10.0 1 A C 4.0 12.0 18.0 8.0 1 D D .0 12.0 17.0 7.0 1 D C 8.0 15.0 15.0 8.0 1 C A 4.0 14.0 20.0 8.0 1 B A 8.0 12.0 15.0 7.0 1 B B 7.0 11.0 17.0 5.0 1 C B 8.0 15.0 18.0 10.0 1 A A 6.0 16.0 18.0 11.0 1 D C 6.0 15.0 17.0 9.0 1 C C 6.0 14.0 20.0 8.0 1 A A 15.0 18.0 20.0 12.0 1 A A 7.0 16.0 17.0 10.0 1 A B 9.0 18.0 18.0 12.0 1 A A 16.0 20.0 20.0 14.0 1 A B 7.0 15.0 16.0 9.0 1 A A 20.0 20.0 19.0 14.0 1 B B 9.0 19.5 18.0 14.0 1 A B 12.0 19.0 17.0 13.0 1 A A 16.0 18.0 15.0 12.0 2 B A 6.0 8.0 19.0 2.0 2 A B 7.0 10.0 19.0 4.0 2 A B 9.0 10.0 18.0 4.0 2 B B 9.0 10.0 19.0 4.0 2 B C 4.0 8.0 19.0 2.0 2 C C 4.0 8.0 18.0 2.0 2 B B 7.0 12.0 19.0 6.0
  13. 14. <ul><li>Plot graphs – you should be able to plot bar charts for sets of scores & plot scattergrams of relationships between the two sets of scores. </li></ul><ul><li>Remember: Select Graphs then explore the alternatives. </li></ul>
  14. 23. <ul><li>Examine descriptive statistics first. </li></ul>Quantitative Statistics: correlation & t-test Results suggest that males could eat more chillies than females. But need to conduct t-test to determine if this difference is significant.
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