chapter twelve copyright © 2006 john wiley & sons, inc. data processing, fundamental data...

Chapter TwelveChapter Twelve

Copyright © 2006John Wiley & Sons, Inc.

Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences

John Wiley & Son, Inc. 2

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


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


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




• 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.



Step One:Validation and

Editing


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




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.



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


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


alternatives.


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


• 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


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


0102030405060708090

100

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

East

West

Exhibit 12.11

Line Chart


Exhibit 12.12 Pie Chart

1st Qtr

2nd Qtr

3rd Qtr

4th Qtr


Exhibit 12.13

Simple Two Dimensional Bar

Cart

0

5

10

15

20

25

30

35

40

45

50

1st Qtr

East

West

North


Exhibit 12.14

0

10

20

30

40

50

1st Qtr

East

West

North

Simple Three Dimensional Bar Chart


Exhibit 12.15 Clustered Bar Chart

0

10

20

30

40

50

60

70

80

90


East

West

North


Exhibit 12.16

0%10%20%30%40%50%60%70%80%90%

100%


North

West

East

Stacked Bar Chart


Exhibit 12.17

Multiple-Row, Three Dimensional Bar Chart

0

20

40

60

80

100

1stQtr

2ndQtr

3rdQtr

4thQtr

East

North

East

West

North


Descriptive Statistics

• Measures of Central Tendency– Nominal and Ordinal Scales– Interval and Ratio Scales– Mean– Median– Mode


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



• 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



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



• 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



Statistical Significance

• Statistical Inference– To generalize from sample results to population

characteristics

• Three Concepts of Differences– Mathematical differences– Statistical significance– Managerially important differences


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


• 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


Hypothesis Testing


– 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


Hypothesis Testing


• 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.


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


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



• 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


Commonly Used Statistical Hypothesis Tests


• 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


• Statistical Significance

• Hypothesis Testing

• Commonly Used Statistical Hypothesis Tests

• P-Values and Significance Testing

• Statistics on the Internet

SUMMARY

chapter twelve copyright © 2006 john wiley & sons, inc. data processing, fundamental data...

Documents