1. MK346 – Undergraduate Dissertation Preparation Part II - Data Analysis and Significance Testing

2. Introduction to SPSS Data Entry File Handling (.sav and.spo files) Creating one and two variable tables Creating Charts Undertaking significance tests

3. Data Handling This is done by a Data Editor Data is entered so that questions are represented by columns, respondents by rows on the grid. Variables can be defined by name and data type. Values can be defined to make data entry simple and tables/graphs meaningful. The data editor has a.sav extension and contains only the survey data. These can be reopened using File – Open- Data.

4. Output files These contain only the output – tables, graphs and statistics, but no data. These have a.spo extension. The output is created using the Graphs and Analyze menus. The output can by copied and pasted into Word documents as required. These can be reopened using File – Open- Output.

5. Frequency Tables These are created using Analyze – Descriptive Statistics – Frequencies. Select all of the relevant variables and put these in the variable field. Select Charts if required. The table has four columns, frequency, percent, valid percent and cumulative percent. If the data has missing values, these are ignored in the valid percent and the cumulative is a running total of these.

6. Two variable tables These are created using Analyze – Descriptive Statistics – Crosstabs. Select one variable for the row field and another variable for the column field.

7. Hypothesis Tests These are based on sample evidence, i.e. the data you have collected from your survey. You need to define hypotheses, H0 – the null hypothesis, H1 – the alternative hypothesis. You need to define a significance value – 5% or 1%, this is the acceptable level of error you will work with if you reject H0 and say an association or difference exists based on your sample evidence. This significance value is provided automatically as part of the SPSS output.

8. The chi-squared test for statistical independence This is used to measure the significance of the association between two categorical variables. H0: No association exists between the two variables H1: Association does exist Set the significance level at 5%.

9. The chi-squared test for statistical independence - SPSS Use Analyze – Descriptive Statistics – Crosstabs. Select one variable for the row field and another variable for the column field. Click on Statistics and switch on Chi- Square. Click on Cells and switch on Observed and Expected.

10. The chi-squared test for statistical independence - SPSS Look at sig-value, if sig-value < 0.05, reject H0 at the 5% level. Conclude that an association exists between the two variables. if sig-value > 0.05, accept H0 at the 5% level. Conclude that no significant association exists between the two variables. If you have reject H0, compare observed and expected values to explain why. Look at notes for assumptions and limitations of the test.

11. Mann-Whitney Test This is used to measure the significance of the difference in sample values between two independent groups. H0: No difference exists between the two groups H1: A difference does exist Set the significance level at 5%.

12. Mann-Whitney Test - SPSS Use Analyze – Nonparametric Tests – 2 Independent Samples. Select your measurement variable as the test variable, and the sample identifying variable as the group variable. Define the two groups, e.g. 1 and 2 if these are the labels used.

13. Mann-Whitney Test - SPSS Look at sig-value, if sig-value < 0.05, reject H0 at the 5% level. Conclude that a difference exists between the two groups. if sig-value > 0.05, accept H0 at the 5% level. Conclude that no significant difference exists between the two groups. If you have reject H0, compare mean ranks for the two groups to identify which group is scoring higher, i.e. the one with higher mean rank value. If H0 is accepted, the mean ranks will be similar, if it is rejected, they will be different. Look at notes for assumptions and limitations of the test.

14. Kruskal-Wallis Test An extension of the Mann-Whitney Test for 3 or more independent groups. Use Analyze – Nonparametric Tests – K Independent Samples.

15. Writing your research methods chapter Indicate with justification the types of data presentation, statistical summary and statistical tests you will perform. Link this to the data types collected. Indicate (if appropriate) the level of significance used in your hypotheses tests.

16. Writing your research results chapter Don’t produce a graph and table for every single question covered, concentrate on the interesting ones! When you are undertaking significance tests, report where significant differences and associations do or do not exist, both are findings. In the case of the former, provide a clear interpretation as to why significance has been found. Compare your findings with the literature, don’t be afraid to uphold or to challenge the literature with your findings.