Practical 3: Questionnaires Flashcards
Structure of a Questionnaire:
Part 1: Demographic information (Contextual Data)
=> This info will help you put your results into a context (e.g. experienced female auditors tend to respond yes)
- give each suggested answer a numerical value to do analysis later
e.g. Sample: Auditors
Gender: Male / Female (Dummy variable!)
Experience: 1-5 yrs / 6-15 / 16-25 / over 25
Qualifications: ACA / ACCA / … / other
Seniority: Assistant manager/ manager/ senior manage/ partner
Structure of a Questionnaire:
Part 1: Demographic information (Contextual Data)
=> This info will help you put your results into a context (e.g. experienced female auditors tend to respond yes)
- give each suggested answer a numerical value to do analysis later
e.g. Sample: Auditors
Gender: Male / Female (Dummy variable!)
Experience: 1-5 yrs / 6-15 / 16-25 / over 25
Qualifications: ACA / ACCA / … / other
Seniority: Assistant manager/ manager/ senior manage/ partner
Questionnaire structure - Part 2: Questions of ?(derived from ?):
- ? (? questions/ ? / ?):
> Questions are linked to the factors/ variables we are examining
e.g. Transparency is 1 factor so 1 of the questions can be:
To what extent does regulation affect transparency?
Answer choices:
Strongly agree/ agree/ neutral / disagree / strongly disagree
Questionnaire structure - Part 2: Questions of Discussion (derived from Literature):
- Factorisation (Research questions/ variables/ trends):
> Questions are linked to the factors/ variables we are examining
e.g. Transparency is 1 factor so 1 of the questions can be:
To what extent does regulation affect transparency?
Answer choices:
Strongly agree/ agree/ neutral / disagree / strongly disagree
Questionnaire structure - Part 3:
?? questions:
- maximum ? questions!
e.g. In general, do you think that regulations can develop more in regards to Control / Transparency/ Communication (the factors)?
Questionnaire structure - Part 3:
Open ended questions:
- maximum 3 questions!
e.g. In general, do you think that regulations can develop more in regards to Control / Transparency/ Communication (the factors)?
If the survey is in multiple languages, ???translation must be done:
e.g. I’ll translate the questionnaire into Vietnamese and then ask someone else to translate it back from Vietnamese to English.
If the survey is in multiple languages, back-to-back translation must be done:
e.g. I’ll translate the questionnaire into Vietnamese and then ask someone else to translate it back from Vietnamese to English.
Sample: 1. Who are ? ? e.g. Auditors/ Regulators/ bankers 2. ? size: - ? questionnaire: e.g. Auditors - 100 / R - 100 / B - 100 - Responses: minimum ?responses to be statistically significant! e.g. 40 / 35 / 45 - Response rate: e.g. 40% > To check if the response rate is good enough, compare it to the ?.
Sample: 1. Who are responders? e.g. Auditors/ Regulators/ bankers 2. Sample size: - Distributed questionnaire: e.g. Auditors - 100 / R - 100 / B - 100 - Responses: minimum 30 responses to be statistically significant! e.g. 40 / 35 / 45 - Response rate: e.g. 40% > To check if the response rate is good enough, compare it to the literature.
Test of normality:
- 1.96 <= Skewness <= 1.96
- 3 <= Kurtosis <= 3
Test of normality:
- 1.96 <= Skewness <= 1.96
- 3 <= Kurtosis <= 3
If data is normal, use Parametric test (t-test)
=> check for Significant / Insignificant differences between different groups of respondents.
If data is normal, use Parametric test (t-test)
=> check for Significant / Insignificant differences between different groups of respondents.
If data is not normal, use non-parametric test:
> Use Mann-Whitney test or aka Wilcoxon rank-sum test) to compare 2 groups
Do the t-test for each question separately
> Krascal Wallis test to compare more than 2 groups
—-
But it’s better to use Man-whitney test.
If data is not normal, use non-parametric test:
> Use Mann-Whitney test or aka Wilcoxon rank-sum test) to compare 2 groups
Do the t-test for each question separately
> Krascal Wallis test to compare more than 2 groups
—-
But it’s better to use Man-whitney test.
In Stata, t-test result is given at:
‘Ha: diff !=0
Pr(|T| > |t|) = …’
If t-test result is:
< 0.01 (1%): *** highly significant;
< 0.05 (5%) : ** moderate significant;
< 0.1 (10%): * ;
In Stata, t-test result is given at:
‘Ha: diff !=0
Pr(|T| > |t|) = …’
If t-test result is:
< 0.01 (1%): *** highly significant;
< 0.05 (5%) : ** moderate significant;
< 0.1 (10%): * ;