SPSS OVERVIEW

By

Brijendra Tripathi

Flow of Presentation

• Basic SPSS functions• Linear Regression • One Way Anova• Factor Analysis

Basic SPSS Functions

• Data view and Variable view• Mean , Standard Deviation• Splitting the file(Data->Split File)• Combining several factors into one

factor.(Transform->Compute variable)• Correlation

Correlation

• Analyze \ Correlate \ Bivariate• The null hypothesis states that the

correlation is equal to zero.

The null hypothesis is that the population coefficient of correlation between the pretest and the final exam is zero. The alternative hypothesis is that  the population coefficient of correlation between the pretest and the final exam is significantly different from zero

Correlation

• Examine the output. For a p-value of .000, report It as p < .001

Linear Regression

1. Analyze\Regression\Linear.

2. Place handgun in the Dependent box and place mankill in the Independent box.

• 3. Statistics button to set confidence interval.

Linear Regression

Linear Regression

Linear Regression

Linear Regression

One Way Anova

1. Write the null hypothesis:H0: µMath = µEnglish = µVisual Arts = µHistory

Where µ represents the mean GPA.

2. Write the alternative hypothesis:H1: not H0

3.Specify the α level: α = .05

One Way Anova

One Way Anova

One Way Anova

One Way Anova

One Way Anova

The final column gives the significance of the F ratio. This is the p value. If the p value is less than or equal your α level, then you can reject H0 that all the means are equal. In this example, the p value is .511 which is greater than the α level, so we fail to reject H0. That is, there is insufficient evidence to claim that some of the means be different.

Factor Analysis

• Meaning• Factor Loading• Eigen Value• Process

Analyze -> Data Red -> Factor -> all variables

descriptive->KMO Test

Rotation ->varimax

Thank You