1. FINA262 Financial Data Analysis
QUESTIONNAIRE DESIGN
FINA262
Financial Data Analysis

2. ATTRIBUTES OF AN EFFECTIVE QUESTIONNAIRE
Questionnares provide a uniform structure that allows responses to be analyzed and compared.
Suppose we want to understand why people eat donuts. There are lots of questions we could ask:
Why do you eat donuts?
Do you like the taste of donuts?
Does eating donuts make you feel good?
Are you concerned about the calories in donuts?
You are grossly overweight, so why do you allow yourself to eat all that sugar?
Questionnaires should be carefully designd so that questions don’t influence respondents toward answering in particular ways and that tha data accomplishes the purpose of the study.

3. When creating a questionnaire, keep several goals in mind:
1) Questionnaires should be user-friendly: clearly understandable and culturally sensitive.
2) The questionnaire shoul look professional: All questionnaires should be clean, typed and carefully designed.
3) It should be valid: Questions should be reliable and measure what you intended to measure
4) It should be attractive and motivational in nature.
5) The questionnaire should encourage respondents to answer honestly and accurately.

4. Questionnaire construction
Two question formats are used in survey research:
Open-ended and Closed-ended questions.
Closed-ended questions can be divided into dichotomous and multiple-choice questions.
Open-ended questions do not provide response choices. Respondents can respond any way they want. Open-ended questions have several advantages . They allow respondents freedom of response. They are well-suited for explatory studies.

5. Closed-ended questions supply a certain number of responses from which respondents are expected to choose. The choices can take the form of a rating system or a set of alternatives.

6. Continuous variables:
Types of variables
Continuous variables:
Always numeric
Can be any number, positive or negative
Examples: age in years, weight, blood pressure readings, temperature, concentrations of pollutants and other measurements
Categorical variables:
Information that can be sorted into categories
Types of categorical variables – ordinal, nominal and dichotomous (binary)

7. Categorical Variables: Ordinal Variables
Ordinal variable—a categorical variable with some intrinsic order or numeric value
Examples of ordinal variables:
Education (no high school degree, HS degree, some college, college degree)
Agreement (strongly disagree, disagree, neutral, agree, strongly agree)
Rating (excellent, good, fair, poor)
Frequency (always, often, sometimes, never)
Any other scale (“On a scale of 1 to 5...”)

8. Categorical Variables: Nominal Variables
Nominal variable – a categorical variable without an intrinsic order
Examples of nominal variables:
Where a person lives in the U.S. (Northeast, South, Midwest, etc.)
Sex (male, female)
Nationality (American, Mexican, French)
Race/ethnicity (African American, Hispanic, White, Asian American)
Favorite pet (dog, cat, fish, snake)

9. Categorical Variables: Dichotomous Variables
Dichotomous (or binary) variables – a categorical variable with only 2 levels of categories
Often represents the answer to a yes or no question
For example:
“Did you attend the church picnic on May 24?”
“Did you eat potato salad at the picnic?”
Anything with only 2 categories

10. Data Cleaning
One of the first steps in analyzing data is to “clean” it of any obvious data entry errors:
Outliers? (really high or low numbers)
Example: Age = 110 (really 10 or 11?)
Value entered that doesn’t exist for variable?
Example: 2 entered where 1=male, 0=female
Missing values?
Did the person not give an answer? Was answer accidentally not entered into the database?
Univariate data analysis is a useful way to check the quality of the data

11. Univariate Data Analysis
Univariate data analysis-explores each variable in a data set separately
Serves as a good method to check the quality of the data
Inconsistencies or unexpected results should be investigated using the original data as the reference point
Frequencies can tell you if many study participants share a characteristic of interest (age, gender, etc.)
Graphs and tables can be helpful

12. Univariate Data Analysis (cont.)
Examining continuous variables can give you important information:
Do all subjects have data, or are values missing?
Are most values clumped together, or is there a lot of variation?
Are there outliers?
Do the minimum and maximum values make sense, or could there be mistakes in the coding?

13. Univariate Data Analysis (cont.)
Commonly used statistics with univariate analysis of continuous variables:
Mean – average of all values of this variable in the dataset
Median – the middle of the distribution, the number where half of the values are above and half are below
Mode – the value that occurs the most times
Range of values – from minimum value to maximum value