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Chapter Thirteen 13-1

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Page 1: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Chapter Thirteen

13-1

Page 2: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Validation& Editing

Coding MachineCleaningof Data

Tabulation &StatisticalAnalysis

DataEntry

Overview of the Data Analysis Procedure

13-2Key Terms & Definitions

Page 3: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Step One:• Validation: Process of ascertaining that interviews actually were conducted as specified.• Editing: Process of ascertaining that questionnaires were filled out properly and

completely.• Skip Pattern: Sequence in which later questions are asked, based on a respondent’s

answer to an earlier question or questions.

Step Two:• Coding: Process of grouping and assigning numeric codes to the various responses to a

question.

Step Three:• Data Entry: Process of converting information to an electronic format. • Scanning: Form of data entry in which responses on questionnaires are read in

automatically by the data entry device.

Overview of the Data Analysis Procedure

13-3Key Terms & Definitions

Page 4: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Step Four:• Clean the Data: Check for data entry errors or data entry inconsistencies.• Logical or Machine Cleaning: Final computerized error check of data.• Error Check Routines: Computer programs that accept instructions from the user

to check for logical errors in the data.

Step Five:• Data Analysis – e.g., One-Way Frequency Tables: Table

showing the number of respondents choosing each answerto a survey question.

• Cross Tabulation Tables - Examination of the responses to one question relative to the responses to one or more other questions.

Overview of the Data Analysis Procedure

13-4Key Terms & Definitions

Page 5: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Validation: Once all interviews have been completed, the research firm typically contacts a percentage of the respondents to validate. Phone validation asks four questions:

1. Was the person actually interviewed?2. Did the person who was interviewed qualify to be interviewed according to

the screening questions on the survey?3. Was the interview conducted in the required manner? 4. Did the interviewer cover the entire survey?

Ensuring the interviews are administered properly and completely help to ensure research results and legitimate responses.

Data Analysis Procedure in Depth

13-5Key Terms & Definitions

Page 6: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Editing: This gives the research firm a chance to check for interviewer and respondent mistakes by manually checking for the following issues:

1. Whether the interviewer failed to ask certain questions or record answers for certain questions.

2. Whether skip patterns were followed3. Whether the interviewer paraphrased respondents’ answers to open-ended

questions.

The person doing the editing must make judgment calls on sub-standard answers to open-ended questions.

Data Analysis Procedure in Depth

13-6Key Terms & Definitions

Page 7: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Coding: The process of grouping and assigning numeric codes to various responses to questions. Closed-ended questions are usually pre-coded and need to be tabulated, but open-ended questions create a coding dilemma:

1. List responses providing the client with the full list of actual responses.2. Consolidate the responses if a number can mean the same thing

Consolidated responses require a judgment call on the part of the person doing the coding.

Data Analysis Procedure in Depth

13-7Key Terms & Definitions

Page 8: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

One-Way Frequency Table

13-8Key Terms & Definitions

Page 9: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

The Coding Process

13-6Key Terms & Definitions

1. List responses2. Consolidate responses3. Set codes4. Enter codes

1. Read responses to individual open-ended questions on questionnaires

2. Match individual responses with consolidated list of response categories

Page 10: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

The Coding Process

13-7Key Terms & Definitions

Page 11: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

One-Way Frequency Tables

13-11Key Terms & Definitions

Page 12: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Bivariate cross tabulation:• Cross tabulation two items -

“Business Category” and “Gender”

Are You a Veteran? (All)You Liked the Chamber's Services (All)Race/Ethnicity (All)

Count of Respondent GenderBusiness Category Female Male Grand TotalComputers/Technology 5 7 12Construction 2 4 6General Services 1 1Manufacturing 13 6 19No Response 1 4 5Other 15 11 26Professional 1 3 4Retail 4 4 8Wholesale 1 1 2#N/A 1 1Grand Total 42 42 84

Cross Tabulations, Examples

13-12Key Terms & Definitions

Page 13: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Race/Ethnicity (All)Are You a Veteran? YesYou Liked the Chamber's Services (All)

Count of Respondent GenderBusiness Category Female Male Grand TotalComputers/Technology 1 3 4Construction 1 1Manufacturing 5 5Other 3 2 5Professional 1 1Grand Total 9 7 16

Multivariate cross tabulation:• Additional filtering criteria - “Veteran Status”• Now filtering three items.

Cross Tabulations, Examples

13-13Key Terms & Definitions

Page 14: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Cross Tabulations, Examples

13-14Key Terms & Definitions

Page 15: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Cross Tabulations Examples

13-15Key Terms & Definitions

Page 16: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

1. Make hypotheses.

2. Look for what is not there.

3. Scrutinize for the obvious.

4. Keep your mind open.

5. Trust the data.

6. Watch the “n.”

Practical Tips for Cross Tabulations

To Make things Easier:

Key Terms & Definitions13-16

Page 17: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

One Way Frequency Tables

A table showing the number of respondents choosing each answer to a survey question.

A table showing the number of respondents choosing each answer to a survey question.

Did You Like the Movie?

43

7

0

2

4

6

8

Female

No

Yes

Grand Total

Did You Like the Movie?

43

7

0

2

4

6

8

Female

No

Yes

Grand Total

Graphic Representations of Data

13-17Key Terms & Definitions

Page 18: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Line Charts:

Graphic Representations of Data

13-18Key Terms & Definitions

• Good for demonstrating linear relationships• Particularly useful for presentinga given measurement taken at several points over time

Page 19: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Pie Charts:

Graphic Representations of Data

13-19Key Terms & Definitions

• Good for special relationships among data points• Should total to 100%

Page 20: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Bar Charts:

Graphic Representations of Data

13-20Key Terms & Definitions

• Good for side by side relationships/comparisons• Most flexible of the graphs

Page 21: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Types of Bar Charts:

Graphic Representations of Data

Multiple-Row Bar Chart

13-21Key Terms & Definitions

Page 22: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Mean:• The sum of the values for all observations of a variable

divided by the number of observations.

Median:• Value below which 50 percent of the observations fall.

Mode:• The value that occurs most frequently.

Mean:• The sum of the values for all observations of a variable

divided by the number of observations.

Median:• Value below which 50 percent of the observations fall.

Mode:• The value that occurs most frequently.

Descriptive Statistics

13-22Key Terms & Definitions

Page 23: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Descriptive Statistics

13-23Key Terms & Definitions

Page 24: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Variance:• The sums of the squared deviations from the mean divided by

the number of observations minus one.• The same formula as standard deviation with the squaring.

Range:• The maximum value for a variable minus the minimum value for

that variable.

Standard Deviation:• Measure of dispersion calculated by subtracting the mean of the

series from each value in a series, squaring each result, summing the results, dividing the sum by the number of items minus 1, and taking the square root of this value.

Variance:• The sums of the squared deviations from the mean divided by

the number of observations minus one.• The same formula as standard deviation with the squaring.

Range:• The maximum value for a variable minus the minimum value for

that variable.

Standard Deviation:• Measure of dispersion calculated by subtracting the mean of the

series from each value in a series, squaring each result, summing the results, dividing the sum by the number of items minus 1, and taking the square root of this value.

Measures of Dispersion

13-24Key Terms & Definitions

These measures indicate how spread out the data are:

Page 25: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Measures of Dispersion

13-25Key Terms & Definitions

Page 26: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

Descriptive statistics are the most efficient means of summarizing the characteristics of large sets of data. In a statistical analysis, the analyst calculates one number or a few numbers that reveal something about the characteristics of large sets of data.

Descriptive statistics are the most efficient means of summarizing the characteristics of large sets of data. In a statistical analysis, the analyst calculates one number or a few numbers that reveal something about the characteristics of large sets of data.

Years in Business

Mean 22.4Standard Error 2.6Median 15.0Mode 5.0Standard Deviation 23.1Sample Variance 534.5Kurtosis 3.8Skewness 2.1Range 98.0Minimum 2.0Maximum 100.0Sum 1770.5Count 79.0

Significant discrepancies in “Mean”and Median” should cause you tolook further into this data.

Descriptive Statistics

13-26Key Terms & Definitions

Page 27: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

The issue of whether certain measurements are different from one another is central to many questions of marketing managers.

The issue of whether certain measurements are different from one another is central to many questions of marketing managers.

Statistical Significance

13-27Key Terms & Definitions

• The posttest measure of top-of-mind awareness is slightly higher than the level pretest. Did the top-of-mind awareness really increase? Is there another explanation for the increase?

• The overall customer satisfaction score increased from 92 percent to 93.5 percent in one three month period Did it really increase?

• In an awareness test, 28.3 percent of those surveyed have heard of the product on an unaided basis. Is this a good result?

Page 28: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

The issue of whether certain measurements are different from one another is central to many questions of marketing managers.

The issue of whether certain measurements are different from one another is central to many questions of marketing managers.

Statistical Significance

13-28Key Terms & Definitions

• Mathematical differences – by definition if the numbers are not exactly the same, they are different. That doesn’t mean they are important or statistically different.

• Statistical significance – if the difference is large enough to be unlikely to have occurred because of chance or sampling error, then it is considered statistically significant.

• Managerially important differences – An argument can be made that a difference, even a small one, may have managerial implications.

Page 29: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

A hypothesis is an assumption or theory that a researcher or manager makes about some characteristics of the population under study.

A hypothesis is an assumption or theory that a researcher or manager makes about some characteristics of the population under study.

Hypothesis Testing

13-29Key Terms & Definitions

• Step One: State the Hypothesis• Step Two: Choose the Test Statistic• Step Three: Develop the Decision Rule• Step Four: Calculate the Value of the Test Statistic• Step Five: State the Conclusion

This is the point of the survey, questionnaire and testing – to determine if the marketing idea, product, or service has an actionable result.

Page 30: Chapter Thirteen 13-1. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis

13-30

Key Terms & Definitions

•Overview of the Data Analysis Procedure•Validation•Editing•Skip Pattern•Coding•Data Entry• Intelligent Data Entry•Scanning Technology•Logical or Machine Cleaning of Data•Error Checking Routines

Links and button are active when in “Slide Show Mode”Key Terms & Definitions

• Marginal Report• One-way Frequency Table• Cross Tabulation•

Graphic Representations of Data

• Descriptive Statistics• Measures of Central Tendency• Measures of Dispersion• Mean• Median• Mode