chapter twelve copyright © 2006 john wiley & sons, inc. data processing, fundamental data...
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Chapter TwelveChapter Twelve
Copyright © 2006John Wiley & Sons, Inc.
Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences
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1. To develop an understanding of the importance and nature of quality control checks.
2. To understand the data entry process and data entry alternatives.
3. To learn how surveys are tabulated and crosstabulated.
4. To understand the concept of hypothesis development and how to test hypotheses
Learning Objectives
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The Data Analysis Procedure
To develop an understanding of the importance and nature of
quality control checks
• Five Step Procedure for Data Analysis– Step One: Validation and editing (quality
control)– Step Two: Coding– Step Three: Data Entry– Step Four: Machine Cleaning of Data– Step Five: Tabulation and Statistical Analysis
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Step One:Validation and
Editing• Validation
– The process of ascertaining that interviews actually were conducted as specified.
– Telephone Validation• Was the person actually interviewed?• Was the respondent actually qualified?• Was the interview conducted in the required manner?• Did the interviewer cover the entire survey?
– Check for other types of problems– Purpose of the Validation
To develop an understanding of the importance and nature of
quality control checks
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• Editing
• Checking for interviewer and respondent mistakes
• Editing Process1. Did the interviewer ask or record answers for
certain questions?
2. Questionnaires are checked to make sure Skip patterns are followed.
3. Responses to open-ended responses are checked.
To develop an understanding of the importance and nature of
quality control checks
Step One:Validation and
Editing
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Step Two: Coding
• Coding – Grouping and assigning numeric codes to the
responses
• The Coding Process1. Listing responses
2. Consolidating responses
3. Setting codes
4. Entering codes
To develop an understanding of the importance and nature of
quality control checks
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Step Three: Data Entry
• Data Entry– Process of converting information to a form that can be read by a
computer
• Intelligent Data Entry– The checking of information being entered for internal logic by either
that data entry device or another device connected to it.
• The Data Entry Process– The mechanics of the process.– The validated, edited, and coded questionnaires are given to a data
entry operator.– The process of going directly from the questionnaire to the data entry
device and storage medium is more accurate and efficient.
To understand the data-entry process and data-entry
alternatives.
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To understand the data-entry process and data-entry
alternatives.
• Scanning– Optical Scanning– Electronically Captured Data is Increasing
• Computer-assisted telephone interviewing
• Internet surveys
• Disks-by-mail surveys
• TouchScreen Kiosk surveys
Step Three: Data Entry
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Step Four: Machine Cleaning
of Data• Machine Cleaning of Data
– A final computerized error check of data.
• Error Checking Routines– Check for logical errors in the data
• Marginal Report– A computer-generated table of the frequencies of the
responses to each question to monitor entry of valid codes and correct use of skip patterns.
• Final Error Check in the Process– Should be ready for tabulation and statistical analysis
To understand the data-entry process and data-entry
alternatives.
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Step Five: Tabulation and
Statistical Analysis
To learn how surveys are tabulated and cross-tabulated
• One Way Frequency Tables– A table showing the number of responses to each answer.
– The first summary of survey results
• Options for Base of the Percentages1. Total respondents
2. Number of people asked the question
3. Number of people answering the question
• Selecting the Base for One-Way Frequency Tables
• Showing Results from Multiple-Choice Questions
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• Cross-Tabulations– Examination of the responses of one question
relative to responses to one or more other questions.
– Three different percentages calculated for each cell in a crosstabulation table
• Column percentage
• Row percentage
• Total percentages
Step Five: Tabulation and
Statistical Analysis
To learn how surveys are tabulated and cross-tabulated
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Graphic Representations
of Data• Line Charts
– The simplest form of graphs.• Pie Charts
– Appropriate for displaying marketing research results in a wide range of situations.
• Bar Charts1. Plain bar chart2. Clustered bar charts3. Stacked bar charts4. Multiple row, three-dimensional bar chartsExamples follow slides 13-19
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0102030405060708090
100
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
East
West
Exhibit 12.11
Line Chart
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Exhibit 12.12 Pie Chart
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
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Exhibit 12.13
Simple Two Dimensional Bar
Cart
0
5
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50
1st Qtr
East
West
North
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Exhibit 12.14
0
10
20
30
40
50
1st Qtr
East
West
North
Simple Three Dimensional Bar Chart
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Exhibit 12.15 Clustered Bar Chart
0
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1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
East
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Exhibit 12.16
0%10%20%30%40%50%60%70%80%90%
100%
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
North
West
East
Stacked Bar Chart
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Exhibit 12.17
Multiple-Row, Three Dimensional Bar Chart
0
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100
1stQtr
2ndQtr
3rdQtr
4thQtr
East
North
East
West
North
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Descriptive Statistics
• Measures of Central Tendency– Nominal and Ordinal Scales– Interval and Ratio Scales– Mean– Median– Mode
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Measures of Central Tendency
• Formula for the Mean
X
h
I = 1
n
fiXi
=where
fi = the frequency of the ith class
Xi = the midpoint of that class
h = the number of classes
n = the total number of observations
Descriptive Statistics
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• Measures of Dispersion– Standard deviation – 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 square-root sign removed.
– Range• The maximum value for a variable minus the minimum
value for that variable
Descriptive Statistics
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Measures of Dispersion
Standard deviation
S
n
I = 1
n - 1
(Xi - X) 2 = √
whereS = sample standard deviation
Xi = the value of the ith observation
X = the sample mean
n = the sample size
Descriptive Statistics
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• Percentages, and Statistical Tests– Whether to use measures of central tendency or
percentages.– Responses are either categorical or take the form
of continuous variables• Variables such as age can be continuous or categorical.• If categories are used, one-way frequency tables and
crosstabulations are used for analysis
– Continuous data can be put into categories.• Evaluating Differences and Changes
Descriptive Statistics
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Statistical Significance
• Statistical Inference– To generalize from sample results to population
characteristics
• Three Concepts of Differences– Mathematical differences– Statistical significance– Managerially important differences
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To understand the concept of hypothesis development and how to test hypotheses.
Hypothesis Testing
• Hypothesis– An assumption that a researcher makes about some
characteristic of the population under study.• Explanation for Differences between a
Hypothesized Value and a Particular Research Result– The Hypothesis is true and the observed difference
is likely due to sampling error– The Hypothesis is false and the true value is some
other value
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• Steps in Hypothesis Testing– Step One: Stating the Hypothesis
• Null hypothesis: Ho
• Alternative hypothesis: Ha
– Step Two: Choosing the Appropriate Test Statistic
• Exhibit 12.20 Statistical Tests and Their Uses—provides a guide to selecting the appropriate test for various situations
To understand the concept of hypothesis development and how to test hypotheses.
Hypothesis Testing
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– Step Three: Developing a Decision Rule• Significance level (α)—0.01, 0.05, or 0.10—that will determine
whether to reject or fail to reject the null hypothesis
– Step Four: Calculating the Value of the Test Statistic• Use the appropriate formula
• Compare calculated value to the critical value.
• State the result in terms of:– rejecting the null hypothesis
– failing to reject the null hypothesis
– Step Five: Stating the Conclusion• Summarizes the results of the test—should be stated from the
perspective of the original research question
To understand the concept of hypothesis development and how to test hypotheses.
Hypothesis Testing
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• Types of Errors in Hypothesis Testing– Type I Error
• Rejection of the null hypothesis when, in fact, it is true.1 – α is the probability of making a correct decision by not rejecting
the null hypothesis when, in fact, it is true
– Type II Error• Acceptance of the null hypothesis when, in fact, it is false.
1– β reflects the probability of making a correct decision in rejecting the null hypothesis when, in fact, it is false
– Accepting Ho or Failing to Reject Ho?• Is there enough data to conclude that Ho is correct
• One-Tailed Test or Two-Tailed Test?
Hypothesis Testing To understand the concept of hypothesis development and how to test hypotheses.
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Type I and Type II Errors
Actual State of the Null Hypothesis
Fail to Reject Ho Reject Ho
Ho is true
Ho is false
Correct (1-) no error
Type II error ()
Type I error ()
Correct (1- ) no error
Tab
le 1
2.21
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Commonly Used Statistical Hypothesis Tests
• Independent Versus Related Samples– Independent samples
• Measurement of a variable in one population has no effect on the measurement of the other variable
– Related Samples• Measurement of a variable in one population may influence the
measurement of the other variable.
• Degrees of Freedom– The number of observations minus the number of
constraints.
– The number of degrees of freedom
To understand the concept of hypothesis development and how to test hypotheses.
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• p - VALUES AND SIGNIFICANCE TESTING– P- Value
• The exact probability of getting a computed test statistic that was largely due to chance1. The smaller the p-value, the smaller the probability that the
observed result occurred by chance.
2. The p-value is the demanding level of statistical significance that can be met, based on the calculated value of the statistic
To understand the concept of hypothesis development and how to test hypotheses.
Commonly Used Statistical Hypothesis Tests
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• Overview of the Data Analysis Procedure
• Step One: Validation and Editing
• Step Two: Coding
• Step Three: Data Entry
• Step Four: Machine Cleaning of Data
• Step Five: Tabulation and Statistical Analysis
• Graphic Representations of Data
• Descriptive Statistics
SUMMARY
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• Statistical Significance
• Hypothesis Testing
• Commonly Used Statistical Hypothesis Tests
• P-Values and Significance Testing
• Statistics on the Internet
SUMMARY
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The End
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