Week 8_Data preparation

Question 1

In which situation a questionnaire will be returned?

Answer 1

1. Incompleted
2. pattern of responses may indicate that the respondent didn’t understand.
3. Response shows little variance
4. Pages missing
5. Received after cut off date
6. Answered by someone not qualify

Question 2

How to avoid respondents that not qualify for the questionnaire?

Answer 2

Skip question setting

Question 3

How to Editing and treatment of unsatisfactory results？

Answer 3

1. Return the field
2. Assign missing value
3. Discard unsatisfactory respondents

Question 4

If demographic information missing, still can use the survey results

Answer 4

True

Question 5

What is fixed field codes?

Answer 5

Responses got the same numbers

Question 6

How to deal with these questions with multiple answers can be chosen?

Answer 6

For each of answer has individual column in excel.

Question 7

How to deal with these questions with multiple answers that can be chosen?

Answer 7

Each answer has an individual column in excel.

Question 8

Category codes should be mutually exclusive and

collectively exhaustive.

Answer 8

True

Question 9

Only a few (10% or less) of the responses should fall into the “other” category.

Answer 9

True

Question 10

How to check if the response data out of range?

Answer 10

Use computer package to check . Extrem value must be examined

Question 11

How to treat missing value (answer)?

Answer 11

Use neutral value (mean response to variables) or leave it as blank

Question 12

What is casewise deletion and pairwise deletion?

Answer 12

• In casewise deletion, cases or respondents with any missing responses, are discarded from the analysis.
• In pairwise deletion, instead of discarding all cases with any missing values, the researcher uses only the cases or respondents with complete responses for the variables involved in each calculation.

Question 13

How to weighting responses?

Answer 13

Adjust the sample percentage to population percentage, and get the weight (population rate/sample rate)

Question 14

How to use Variable re-specification to adjust data?

Answer 14

re-specify the code for answer when respondent see the wrong code for answers: (use 1 for agree, 2 for extreme agree, but respodent use 1 for extreme agree and 2 for agree)

Question 15

How to use dummy variables?

Answer 15

Used for re-specifying categorical variables

a person is given a value of 0 if in a control group or a value of 1 if in the treatment group.

Question 16

Scale transformation

Answer 16

• Scale transformation involves a manipulation of scale values to ensure comparability with other scales or otherwise make the data suitable for analysis.
• A more common transformation procedure is standardization. Standardized scores, Zi, may be
obtainedas: Zi = (Xi- X)/sx

Question 17

Issues on international market

Answer 17

1. Standardize the unit of data for comparable manner

Question 18

Ethics in Marketing Research

Answer 18

Discarding respondents after analyzing the data raises ethical concerns, particularly if this information is not fully disclosed in the written report.
• The procedure used to identify unsatisfactory respondents and the number of respondents discarded should be clearly disclosed.
• Although interpretations, conclusions, and recommendations necessarily involve the subjective judgment of the researcher, this judgment must be exercised honestly, free from any personal biases or agendas of the researcher or the client.

Question 19

Four methods to adjust the response data

Answer 19

1. substitute with neutral value
2. substitute with imputed(基于之前的选项推理可能的结果） responses
3. casewise deletion (全部删除）
4. pairwise deletion(uses only the cases or respondents with complete responses for the variables involved in each calculation.)
5. Weighting
6. Dummy variable code
7. Scale transformation and standardization (Zi = (Xi- X-bar)/Sx