1. Data Processing, Fundamental Data
Chapter Thirteen:
Data Processing, Fundamental Data
Analysis, and the Statistical Testing of Hypotheses

2. Understand the importance and nature of quality control checks
Chapter Thirteen: Data Processing, Fundamental Data Analysis, and the Statistical Testing of Hypotheses
Understand the importance and nature of quality control checks
Describe the process of coding
Understand the data entry process and data entry alternatives
Explain how surveys are tabulated and cross tabulated
Describe basic descriptive statistics
Understand the concept of hypothesis development and testing

3. Data Analysis Overview
The Key Steps: 1 2 3 4 5
Machine
Cleaning
of Data
Tabulation and
Statistical
Analysis
Validation and
Editing
Data
Entry
Coding
Chapter Thirteen

4. Data Analysis Overview
Step One:
Validation: Confirming the interviews / surveys occurred
Editing: Determining the questionnaires were completed correctly
Step Two:
Coding: Grouping and assigning numeric codes to the question responses
Step Three:
Data Entry: Process of converting data to an electronic form
Scanning the questionnaire into a database
Step Four:
Clean the Data: Check for data entry errors or data entry inconsistencies
Machine cleaning: Computerized check of the data
Step Five:
One-Way Frequency Tables, Cross Tabulations

5. Editing and Skip Patterns
The process of ascertaining that questionnaires were filled out properly and completely
Skip Patterns:
Sequence in which later questions are asked, based on a respondent’s answer to an earlier question

6. Coding
Coding:
Grouping and assigning numeric codes to every potential response to a question
The Process:
List responses
Consolidate responses
Set codes
Enter codes
Keep coding sheet

7. Data Entry Data Entry: Converting information to an electronic format
Intelligent Data Entry:
A form of data entry in which the information being entered into the data entry device is checked for internal logic

8. Tabulation
The most basic tabulation is the one-way frequency table:

9. Cross-Tabulation Data
Bivariate cross-tabulation:
Cross tabulation two items: “Business Category” and “Gender”
Multivariate cross-tabulation:
Additional filtering criteria—“Veteran Status”. Now filtering three items.

10. Descriptive Statistics
Effective means of summarizing large data sets.
Key measures include: mean, median, mode, standard deviation, skewness, and variance.

11. Measure of Central Tendency
Mean:
The sum of the values for all observations of a variable divided by the number of observations
Median:
In an ordered set, the value below which 50 percent of the observations fall
Mode:
The value that occurs most frequently

12. Measures of Dispersion
Variance:
Sums of the squared deviations from the mean divided by the number of observations minus one
Same formula as standard deviation
Range:
Maximum value for variable minus the minimum value for that variable
Standard Deviation: Calculate by
Subtracting the mean of a series from each value in a series
Squaring each result then summing them
Dividing the result by the number of items minus 1
Take the square root of this value

13. Statistical Significance
Mathematical differences
Statistical significance
Managerially important differences

14. Hypothesis Testing: Key Steps
Step One: Stating the hypothesis
Null Hypothesis: status quo proven to be true
Alternative Hypotheses: another alternative proven to the true.
Step Two: Choosing the appropriate test statistic
Test of means, test or proportions, ANOVA, etc.
Step Three: Developing a decision rule
Determine the significance level
Need to determine whether to reject or fail to reject the null hypothesis

15. Hypothesis Testing: Key Steps
Step Four: Calculating the value of the test statistic
Use the appropriate formula to calculate the value of the statistic.
Step Five: Stating the conclusion
Stated from the perspective of the original research question

16. Types of Errors in Hypothesis Testing
Type I error:
Rejection of the null hypothesis when, in fact, it is true
Acceptance of the null hypothesis when, in fact, it is false
Tests are either one- or two-tailed. This decision depends on the nature of the situation and what the researcher is demonstrating.
One-Tailed Test:
“If you take the medicine, you will get better”
Two-Tailed Test:
“If you take the medicine, you will get either better or worse.”
One- and Two-Tailed Tests

17. Issues With Type I and II Errors
Type I and Type II Errors

18. Commonly Used Statistical Hypothesis Tests
Independent samples
Related samples
Degrees of freedom
p Values and significance testing

19. Copyright
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