wason card sort: data analysis week 3 practical. week 3 practicalwason card sort week 1 week 2 week...
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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.