1. Chapter Twelve
Data Processing, Fundamental Data Analysis, and the Statistical Testing of Differences
Chapter Twelve

2. Chapter Twelve Objectives
To develop an understanding of the importance and nature of quality control checks.
To understand the data entry process and data entry alternatives.
To learn how surveys are tabulated and cross-tabulated.
To understand the concept of hypothesis development and how to test hypotheses.
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3. Data Analysis Overview
The Key Steps: 1 2 3 4 5
Machine
Cleaning
of Data
Tabulation &
Statistical
Analysis
Validation &
Editing
Data
Entry
Coding
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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
Can use scanning devices to enter data
Scanning the questionnaire into a data base (such as with bubble sheets)
Step Four:
Clean the Data: Check for data entry errors or data entry inconsistencies
Machine cleaning - computerized check of the data
Step Five:
Data tabulations and statistical analysis.
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5. Editing & 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 or questions.
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6. Coding
Coding:
The Process of grouping and assigning numeric codes to the various responses to a question.
The Process:
List Responses
Consolidate Responses
Set Codes
Enter Codes
Keep Coding Sheet
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7. Data Entry Data Entry: Intelligent Data Entry:
The Process of 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.
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8. Machine Cleaning of Data
Final computer error check of data.
Error Checking Routines:
Computer programs that accept instructions from the user to check for logical errors in the data.
Marginal Report:
Computer-generated table of the frequencies of the responses to each question, used to monitor entry of valid codes and correct use of skip patterns.
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9. Cross Tabulation Data
Examination of the responses to one question relative to the responses to one or more questions in a survey set.
Bi-variate cross-tabulation:
Cross tabulation two items - “Business Category” and “Gender”
Multi-variate cross-tabulation:
Additional filtering criteria - “Veteran Status” - Now filtering three items.
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10. Graphic Representations of Data One Way Frequency Tables
A table showing the number of respondents choosing each answer to a survey question.
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11. Graphic Representations of Data
Line, Pie, and Bar Charts
Line Charts: Good for demonstrating linear relationships.
Pie Charts: Good for special relationships among data points.
Bar Charts: Good for side by side relationships / comparisons
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12. Descriptive Statistics
Effective means of summarizing large data sets. Key measures include: mean, median, mode, kurtosis, standard deviation, skewness, and variance.
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13. Descriptive Statistics
Measures 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.
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14. Descriptive Statistics
Measures of Dispersion
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:
Calculated by:
subtracting the mean of a series from each value in a series
squaring each result then summing them
then dividing the result by the number of items minus 1
and finally taking the square root of this value
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15. Statistical Significance
Mathematical Differences:
By definition, if numbers are not exactly the same, they are different. This fact does not, however, mean that the difference is either important or statistically significant.
Statistical Significance:
If a particular difference is large enough to be unlikely to have occurred because of chance or sampling error, then the difference is statistically significant.
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16. Statistical Significance
Managerial Important Differences:
One must be able to distinguish between mathematically differences and statistically significant differences in using the data analysis in managerial decision making.
Hypothesis:
An assumption, argument, or theory that a researcher or manager makes about some characteristics of the population under study.
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17. The Key Steps: Hypothesis Testing 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.
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18. The Key Steps: Hypothesis Testing
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.
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19. Types of Errors in Hypothesis Testing
Type I:
Rejection of the null hypothesis when, in fact, it is true.
Acceptance of the null hypothesis when, in fact, it is false.
Type II:
Tests are either one or two-tailed. This decision depend on the nature of the situation and what the researcher is demonstrating.
One-Tailed:
“If you take the medicine, you will get better”
Two-Tailed:
“If you take the medicine, you will get either better or worse.”
One and Two
Tailed Tests
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20. Issues With Type I and II Errors
Type I and Type II Errors
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21. Commonly Used Statistical Hypothesis Tests
Independent Samples:
Samples in which measurement of a variable in one population has no effect on measurement of the variable in the other.
Related Samples:
Samples in which measurement of a variable in one population might influence measurement of the variable in the other.
Degrees of Freedom:
Is equal to the number of observations minus the number of assumptions or constraints necessary to calculate a statistic.
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22. About One and Two Means Respectively
Hypothesis Tests
About One and Two Means Respectively
About One Mean:
Z-Test:
Hypothesis test used for a single mean if the sample is large enough and drawn from a normal population. Usually for samples of about 30 and above.
t-Test:
Hypothesis test used for a single mean if the sample is too small to use the Z-test. Usually for samples below 30.
About Two Means:
Hypothesis test that tests the difference between groups of data.
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23. About Proportions and P-Value
Hypothesis Tests
About Proportions and P-Value
Proportion in One Sample:
Test to determine whether the difference between proportions is greater than would be expected because of sampling error.
Two Proportions in Independent Samples:
Test to determine the proportional differences between two or more groups.
p-value:
The exact probability of getting a computed test statistic that was largely due to chance. The smaller the p-value, the smaller the probability that the observed result occurred by chance.
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24. Statistics and the Internet
ActivStats -
Autobox -
Math Software -
Minitab -
SAS -
SPSS -
Stata -
SYSTAT -
Vizion -
xISTAT -
In “Slide Show” mode, click on the arrow to be taken to the respective web page.
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25. Index Cross-tabulation Data Analysis Overview Descriptive Statistics
Editing, Coding, & Cleaning the Data
Hypothesis Testing - Common Types
Hypothesis Testing - Steps
Measures of Central Tendency
Measures of Dispersion
Statistical Testing of Differences
Type I and Type II Errors
Index