WASON CARD SORT:

DATA ANALYSIS

Week 3 Practical

WEEK 3 PRACTICALWASON CARD SORT

WEEK 1

WEEK 2

WEEK 3

WEEK 4

WEEK 5

WEEK 6

WEEK 7

WEEK 8

WEEK 9

WEEK 10

LECTURE 1 PRACTICAL

NONPARAMETRICS 1 1ST PRACTICAL

NONPARAMETRICS 2 1ST ANALYSIS

SAMPLING DISTRIBUTIONS 1ST ANALYSIS + PROBLEMS 1

HYPOTHESIS TESTING 2ND PRACTICAL

RELATED T-TEST 2ND ANALYSIS + SOLUTIONS 1

INDEPENDENT T-TEST

INDEPENDENT ANOVA

DEPENDENT ANOVA

NO LECTURE

2ND ANALYSIS + PROBLEMS 2

3RD PRACTICAL

3RD ANALYSIS + SOLUTIONS 2

3RD ANALYSIS + PROBLEMS 3

NO LECTURE NO PRACTICAL

LEARNING OUTCOMES

BY THE END OF THE SESSION, YOU SHOULD BE ABLE TO:

Use the graphs to interpret your chi-square findings.

Use SPSS to test the second experimental hypothesis of the Wason card sorting experiment and produce a related graph.

Use SPSS to test the first experimental hypothesis of the Wason card sorting experiment and produce a related graph.

Make a start on writing up your RESULTS and DISCUSSION sections for your lab report.

WASON CARD SORT

HYPOTHESES

WASON CARD SORT

Our current experiment raises two hypotheses:

METHOD RECAP

Q2: Is performance on the abstract task affected if it follows a concrete scenario?

Q1: Is performance better on some versions of the Wason card sorting task than others the first time it is performed?

DESIGN

Half of the people in the room did the concrete first, half did the abstract.

ABSTRACT CONCRETEthen ABSTRACT CONCRETE then

ABSTRACT CONCRETEthen ABSTRACT CONCRETE then

ABSTRACT CONCRETE thenABSTRACT CONCRETEthen

WASON CARD SORT RESULTS

The first column shows subject number. And yes, there are 180.

The second column shows which type of problem each individual solved

first.

1 =

2 =

ABSTRACT

CONCRETE

The third column shows whether the individual got their first problem right

or wrong.

The fourth column shows whether the individual got the abstract problem right or wrong.

1 = 2 =

WRONG

RIGHT

1 = 2 =

WRONG

RIGHT

WASON CARD SORT RESULTS

Let’s make the data look a little more meaningful by changing the numeric values into textual values. Go to variable view and select values.

1 =

2 =

ABSTRACT

CONCRETE

correcta

correct1

first_problem

For first_problem, correct1 and correcta, associate the values (e.g., 1, 2) with the appropriate value labels (e.g., abstract, concrete; wrong, right).

1 = 2 =

WRONG

RIGHT

1 = 2 =

WRONG

RIGHT

WASON CARD SORT RESULTS

ABSTRACT (1st) CONCRETE (1st)

Q1: Is performance better on some versions of the Wason card sorting task than others the first time it is performed?

vs.

If we are interested in comparing categorises of responses, then the

chi-square test is appropriate.

In SPSS, the chi-square test is hidden away underneath

descriptive statistics > crosstabs.

Let’s go there now.

WASON CARD SORT RESULTS

Q1: Is performance better on some versions of the Wason card sorting task than others the first time it is performed?

In order to build a chi-square table, we need to put our various

categories into rows and columns.

Let’s put first_pr as a row and correct as a column. This will show

us the frequency distributions.

Under statistics, we also need to make sure the chi-square test is

performed, so tick that.

Under cells, also make sure that both observed and expected are clicked.

ABSTRACT (1st) CONCRETE (1st) vs.

WASON CARD SORT RESULTS

Q1: Is performance better on some versions of the Wason card sorting task than others the first time it is performed?

The second table (after case processing summary) confirms

our 180 observations and displays the frequency distribution of right and wrong responses for the two

kinds of Wason card sort test.

The third table provides us with the chi-square value, which may

be reported as:

χ2 (1) = 19.74, p < .001

CORRECT1 * FIRST_PR Crosstabulation

8 33 41

20.5 20.5 41.0

82 57 139

69.5 69.5 139.0

90 90 180

90.0 90.0 180.0

Count

Expected Count

Count

Expected Count

Count

Expected Count

right

wrong

CORRECT1

Total

1 2

FIRST_PR

Total

Chi-Square Tests

19.740b 1 .000

18.193 1 .000

20.887 1 .000

.000 .000

19.631 1 .000

180

Pearson Chi-Square

Continuity Correctiona

Likelihood Ratio

Fisher's Exact Test

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)Exact Sig.(2-sided)

Exact Sig.(1-sided)

Computed only for a 2x2 tablea.

0 cells (.0%) have expected count less than 5. The minimum expected count is20.50.

b.

ABSTRACT (1st) CONCRETE (1st) vs.

WASON CARD SORT RESULTS

Q1: Is performance better on some versions of the Wason card sorting task than others the first time it is performed?

χ2 (1) = 19.74, p < .001

Chi-Square Tests

19.740b 1 .000

18.193 1 .000

20.887 1 .000

.000 .000

19.631 1 .000

180

Pearson Chi-Square

Continuity Correctiona

Likelihood Ratio

Fisher's Exact Test

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)Exact Sig.(2-sided)

Exact Sig.(1-sided)

Computed only for a 2x2 tablea.

0 cells (.0%) have expected count less than 5. The minimum expected count is20.50.

b.

Degrees of freedom

Chi-square value

Significance level

WASON CARD SORT RESULTS

Q1: Is performance better on some versions of the Wason card sorting task than others the first time it is performed?

You will need to graphically represent your results, too

Don’t forget to give your figure a number and a title

When you refer to the figure in the main text, make sure you give the

exact descriptive statistics

Note: no error bars, because this is qualitative data

WASON CARD SORT

Q2: Is performance on the abstract task affected if it follows a concrete scenario?

If we are interested in comparing categorises of responses, then the

chi-square test is appropriate.

In SPSS, the chi-square test is hidden away underneath

descriptive statistics > crosstabs.

Let’s go there now.

RESULTS

ABSTRACT (2nd)ABSTRACT (1st) vs.

WASON CARD SORT

In order to build a chi-square table, we need to put our various

categories into rows and columns.

Let’s put first_pr as a row and correc_a as a column. This will show

us the frequency distributions.

Under statistics, we also need to make sure the chi-square test is

performed, so tick that.

Under cells, also make sure that both observed and expected are clicked.

RESULTS

Q2: Is performance on the abstract task affected if it follows a concrete scenario?

ABSTRACT (2nd)ABSTRACT (1st) vs.

WASON CARD SORT

The second table (after case processing summary) confirms

our 180 observations and displays the frequency distribution of right and wrong responses for the two

kinds of Wason card sort test.

The third table provides us with the chi-square value, which may

be reported as:

χ2 (1) = 0.53, p = .47

RESULTS

Q2: Is performance on the abstract task affected if it follows a concrete scenario?

CORRECTA * FIRST_PR Crosstabulation

8 11 19

9.5 9.5 19.0

82 79 161

80.5 80.5 161.0

90 90 180

90.0 90.0 180.0

Count

Expected Count

Count

Expected Count

Count

Expected Count

right

wrong

CORRECTA

Total

1 2

FIRST_PR

Total

Chi-Square Tests

.530b 1 .467

.235 1 .628

.532 1 .466

.629 .314

.527 1 .468

180

Pearson Chi-Square

Continuity Correctiona

Likelihood Ratio

Fisher's Exact Test

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)Exact Sig.(2-sided)

Exact Sig.(1-sided)

Computed only for a 2x2 tablea.

0 cells (.0%) have expected count less than 5. The minimum expected count is9.50.

b.

ABSTRACT (2nd)ABSTRACT (1st) vs.

WASON CARD SORT RESULTS

Q2: Is performance on the abstract task affected if it follows a concrete scenario?

Once again, you will need to graphically represent your results

Don’t forget to give your figure a number and a title

When you refer to the figure in the main text, make sure you give the exact descriptive statistics

Note: no error bars, because this is qualitative data

If you have trouble, refer back to the Excel graph-making guides on Graham’s webpage, or ask a tutor for help

WASON CARD SORT DISCUSSION

GET TOGETHER IN GROUPS OF THREE OR FOUR AND REFLECT ON TODAY’S EXPERIENCE USING THE FOLLOWING QUESTIONS

Why have I done this particular statistical test?

What implications do the data have for the studies

outlined in the intro?

What do the data actually tell me with respect to my experimental hypotheses?

LEARNING OUTCOMES

BY THE END OF THE SESSION, YOU SHOULD BE ABLE TO:

SAMPLING DISTRIBUTIONS

Use the graphs to interpret your chi-square findings.

Use SPSS to test the second experimental hypothesis of the Wason card sorting experiment and produce a related graph.

Use SPSS to test the first experimental hypothesis of the Wason card sorting experiment and produce a related graph.

Make a start on writing up your RESULTS and DISCUSSION sections for your lab report.