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Spss basic1
 

Spss basic1

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    Spss basic1 Spss basic1 Presentation Transcript

    • SPSS ACTIVITY
      • SPSS is the most commonly used statistical software for social sciences.
      • It is powerful (perhaps too powerful) but makes it easy to compute the scores from a set of data and do the statistical analysis.
      • Let’s start with a quick run through of the things you should know.
    • 1. We will start by using the SPSS DATA EDITOR To define and enter data
      • Variable view
      • Define IVs & DVs here.
      • Use separate line for each & give sensible names.
      • Decide format of data: String = text, numeric = numbers. Numeric is generally the best format.
      • Data view
      • Insert data for variables here.
      • Data is input in columns under appropriate variable names.
      • You should be able to calculate descriptive statistics with the Frequency..., Descriptives..., Explore... And Crosstabs... function.
    • 1. SPSS DATA EDITOR To define and enter data
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      • Use to check on errors in typing the data and for screening to detect out of range data.
      • Select Analyze menu, click on Descriptive Statistics and then Frequencies
      • You will get a Frequency dialog box
      • Select the variables and send to variables box
      • Click OK
      • Checking on normality: histogram, stem-and-leaf plot, boxplot, and others
      • Select Analyze menu, click on Descriptive Statistics and then Explore to get the Explore dialog box
      • Select the variables you require and click on to the dependent list
      • Click on the plots to obtain the Explore Plots sub-dialogue box
      • Click on the Histogram check box and the normality-plots with tests
      • Variables maybe distributed in varying degrees of skewness hence need to transformed.
      • Variables also need to be transform as intended by the researcher as stated in the objectives.
      • TRANSFORM- COMPUTE
      • TRANSFORM – RECODE
        • Recoding negatively worded scale items
        • Collapsing continuous variables
        • Replacing missing values
      • Frequency/percentage table,
      • Pie or bar Charts,
      • Histogram
      • Frequency Polygon,
      • Cross-tabulation
      • Scatter diagram
      • Mean, Median, Mode, Maximum, Minimum
      • Range, Variance, Standard Deviation, Coefficient of variation, Standard Scores
    • 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
      • Plot graphs – you should be able to plot bar charts for sets of scores & plot scattergrams of relationships between the two sets of scores.
      • Remember: Select Graphs then explore the alternatives.
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      • Examine descriptive statistics first.
      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.