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 EDITORTo define and enter data

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.

Examine descriptive statistics first.

Quantitative Statistics: correlation & t-test

Group Statistics

8 5.6250 1.4079 .4978

8 4.1250 1.1260 .3981

GENDERmale

female

CHILLIESN Mean Std. Deviation

Std. ErrorMean

GENDER

femalemale

Me

an

CH

ILL

IES

6.0

5.5

5.0

4.5

4.0

3.5

Results suggest that males could eat more chillies than females. But need to conduct t-test to determine if this difference is significant.