SPSS (the Statistical Package for the Social Sciences)

PART 2

SPSS (the Statistical Package for the Social Sciences)

Lesson objectives Recap SPSS

Data entry Data view Variable view

Descriptive analysis Determining reliability Inferential Statistics with SPSS

Inferential Statistics Based on the assumption that the

sample is random Types of tests

Chi Squared Correlation T test

Example research

Purpose : To determine if a certain method of teaching will lead to higher achievement among visual learners

Design

Population: EDU 540 students (154) Sample ( chosen at random, 3 lessons taught by

the same person using the ‘method’) Class 1 (30) Class 2 (45) Class 3 (38)

Dependent VariableIndependent Variable

AchievementSatisfaction

Learning styles

INTERVENTIONTeaching method

Instruments Learning style inventory

Scores will determine learning styles Can categorize as visual, tactile or auditory

Questionnaire Satisfaction regarding the teaching method higher score – higher lesson satisfaction

Test Scores will determine achievement

What to describe? Descriptive stats

Age Gender Program Learning styles

Cross tabulate? Gender and learning styles

Significance If significant, unlikely to have

occurred by chance there is statistical evidence that

there is a difference, a correlation, an association between etc….

Significance level Significance levels show you how likely a result is due to

chance. The most common level, used to mean something is good

enough to be believed, is .95. The finding has a 95% chance of being true. No statistical package will show you "95%" or ".95" to

indicate this level. Instead it will show you ".05," meaning that the finding has a five percent (.05) chance of not being true, which is the converse of a 95% chance of being true.

To find the significance level, subtract the number shown from one. For example, a value of ".01" means that there is a 99% (1-.01=.99) chance of it being true

Hypothesis testing The Null hypothesis states there is no true

difference/no relationship between parameters in the population We reject or accept the null hypothesis

It is rejected only when it becomes evidently false, that is, when the researcher has a certain degree of confidence, usually 95% to 99%, that the data do not support the null hypothesis

Example There is no significant difference between the

mean test scores of visual and tactile learners

Hypothesis testing YOU ALWAYS TEST THE NULL

HYPOTHESIS!

Significance Test of significance

To decide whether to accept or reject the null hypothesis

Select probability 5 out of 100 times the difference did not

occur by chance ( Significance level: 0.05) 1 out of 100 times the difference did not

occur by chance ( Significance level: 0.01) Confidence level?

95% or 99%

Example Null hypothesis

There is no relationship between variables.. Significance level : 0.05 Test statistic

Probability value 0.009 or Sig. 0.009 (smaller than 0.05)

What does that mean? very unlikely that there’s no relationship

between the variables Variables not independent of each other REJECT Null hypothesis

Example Null hypothesis

There is no relationship between variables.. Significance level : 0.01 Test statistic

Probability value 0.12 or Sig. 0.12(greater than 0.01)

What does this mean? Higher likelihood that there’s no relationship

between the variables Variables are independent of each other ACCEPT Null hypothesis

Let’s get on with inferential statistics

Now.. What to infer? Independence/ Association Correlation Differences

Independence test –Chi squared Chi squared test is used in situations

where you have two categorical variables Gender and employment sector Gender and learning styles

Chi-square test of independence tests the null hypothesis that there is no association between the two variables

Example: Test for independence Gender

Female Male

Learning styles Visual Tactile Auditory

Null Hypothesis: No association between gender and learning styles

Using SPSS for chi squared Click

Analyze Descriptive

Crosstabs Statistics

Using SPSS for chi squared Low chi squared statistic Sig.961 Accept null hypothesis There is no association… Variables independent of

each other

Chi-Square Tests

.079a 2 .961

.080 2 .961

10

Pearson Chi-Square

Likelihood Ratio

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)

6 cells (100.0%) have expected count less than 5. Theminimum expected count is .90.

a.

Correlation Measure of the linear relationship between two variables. A correlation coefficient has a value ranging from -1 to 1. Values that are closer to the absolute value of 1 indicate

that there is a strong relationship between the variables being correlated whereas values closer to 0 indicate that there is little or no linear relationship.

The sign of a correlation coefficient describes the type of relationship between the variables being correlated. A positive correlation coefficient indicates that there is a

positive linear relationship between the variables: as one variable increases in value, so does the other.

A negative value indicates a negative linear relationship between variables: as one variable increases in value, the other variable decreases in value.

Example: Correlation Correlation between learning styles

and test scores Correlation between learning styles

and satisfaction

Correlation in SPSS Start at the Analyze menu. Select the Correlate option from this

menu. You will see three options for correlating variables: Bivariate Partial Distances.

The bivariate correlation is for situations where you are interested only in the relationship between two variables

Correlation in SPSS Then, consider is the type of correlation coefficient.

Pearson's is appropriate for continuous data Kendall's tau-b and Spearman's, are designed for ranked

data. The choice between a one and two-tailed significance test

in the Test of Significance box should be determined by the hypothesis you are testing if you are making a prediction that there is a negative or

positive relationship between the variables, then the one-tailed test is appropriate

if you are not making a directional prediction, you should use the two-tailed test (there is not a specific prediction about the direction of the relationship between the variables)

Output

Correlations

1.000 .498**

. .003

30 30

.498** 1.000

.003 .

30 30

Pearson Correlation

Sig. (1-tailed)

N

Pearson Correlation

Sig. (1-tailed)

N

LSVISUAL

TEST

LSVISUAL TEST

Correlation is significant at the 0.01 level (1-tailed).**.

Output

Correlation is not statistically significant

Correlations

1.000 .127

. .252

30 30

.127 1.000

.252 .

30 30

Pearson Correlation

Sig. (1-tailed)

N

Pearson Correlation

Sig. (1-tailed)

N

LSVISUAL

QNAIRE

LSVISUAL QNAIRE

Let’s check for significant difference

Differences between test scores of the groups of learners

Differences: Using t test The t test is a useful technique for

comparing mean values of two sets of numbers. Statistic for evaluating whether the difference

between two means is statistically significant. t tests can be used either

to compare two independent groups (independent-samples t test)

to compare observations from two measurement occasions for the same group (paired-samples t test).

Remember

t test - tests the null hypothesis / that there is no difference …

t test If you are using the t test to compare two

groups, the groups should be randomly drawn from normally distributed and independent populations.

Using SPSS Analyze

Compare Means             One-Sample T test...             Independent-Samples T test...             Paired-Samples T test...

Types of t-test The one-sample t test is used compare a single sample

with a population value. Example, a test could be conducted to compare the average

test scores of U5C with a value that was known to represent the whole EDU 540 population.

The independent-sample t test is used to compare two groups' scores on the same variable. Example : Compare the test scores of U5C and PKPG to

evaluate whether there is a difference in their scores. The paired-sample t test is used to compare the means of

two variables within a single group. Example, it could be used to see if there is a statistically

significant difference between test 1 and test 2 among the members of U5C

Using SPSS : t test

Output

Notice the two parts of the output Equal variances assumed Equal variance not assumed

Which to use? Look at Levene’s test for equality of variance If small Sig. - groups have unequal variances

Independent Samples Test

.814 .378 -6.024 19 .000 -11.528 1.914 -15.533 -7.523

-5.483 10.805 .000 -11.528 2.103 -16.166 -6.890

Equal variancesassumed

Equal variancesnot assumed

TESTF Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

Output

t-statistics is -6.024 Sig. level : .000 The significance level tells us that the probability that

(there is no difference between visual and tactile learners) – the “NULL” is very small

Hence, there is a significant difference in the test scores between visual and tactile learners

Independent Samples Test

.814 .378 -6.024 19 .000 -11.528 1.914 -15.533 -7.523

-5.483 10.805 .000 -11.528 2.103 -16.166 -6.890

Equal variancesassumed

Equal variancesnot assumed

TESTF Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

Have fun with SPSS!

Proceed to Qualitative Analysis and Ethics in Research