chapter thirteen 13-1. validation & editing coding machine cleaning of data tabulation &...
TRANSCRIPT
Chapter Thirteen
13-1
Validation& Editing
Coding MachineCleaningof Data
Tabulation &StatisticalAnalysis
DataEntry
Overview of the Data Analysis Procedure
13-2Key Terms & Definitions
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
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
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
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
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
One-Way Frequency Table
13-8Key Terms & Definitions
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
The Coding Process
13-7Key Terms & Definitions
One-Way Frequency Tables
13-11Key Terms & Definitions
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
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
Cross Tabulations, Examples
13-14Key Terms & Definitions
Cross Tabulations Examples
13-15Key Terms & Definitions
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
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
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
Pie Charts:
Graphic Representations of Data
13-19Key Terms & Definitions
• Good for special relationships among data points• Should total to 100%
Bar Charts:
Graphic Representations of Data
13-20Key Terms & Definitions
• Good for side by side relationships/comparisons• Most flexible of the graphs
Types of Bar Charts:
Graphic Representations of Data
Multiple-Row Bar Chart
13-21Key Terms & Definitions
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
Descriptive Statistics
13-23Key Terms & Definitions
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:
Measures of Dispersion
13-25Key Terms & Definitions
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
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?
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.
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.
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