Download No Slide Title

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

History of statistics wikipedia, lookup

Time series wikipedia, lookup

Misuse of statistics wikipedia, lookup

Data mining wikipedia, lookup

Transcript
CHAPTER fourteen
Learning Objectives
Data Processing
and
Fundamental
Data Analysis
Copyright © 2002
South-Western/Thomson Learning
1
Learning Objectives
Learning Objectives
1. To get an overview of the data analysis procedure.
2. To develop an understanding of the importance and
nature of quality control checks.
3. To understand the data entry process and data
entry alternatives.
4. To learn how surveys are tabulated and
crosstabulated.
5. To learn how to set up and interpret
crosstabulations.
6. To comprehend the basic techniques of statistical
analysis.
2
Learning Objectives
The Data Analysis Procedure
To get an overview of the data
analysis procedure.
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
3
Learning Objectives
Step 1:
Validation and Editing
To understand the importance and
nature of quality control checks.
Step 1: Validation
The process of ascertaining that interviews actually were
conducted as specified (e.g., proper screening, proper
procedures followed)
Step 1: Editing
Checking for interviewer mistakes
1. 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.
4
Learning Objectives
Step 2: Coding
To understand the data-entry process
and data-entry alternatives.
Step 2: Coding
Grouping and assigning numeric codes to the responses
The Coding Process
1. Listing responses
2. Consolidating responses
3. Setting codes
4. Entering codes
5
Learning Objectives
Step 3: Data Entry
To understand the data-entry process
and data-entry alternatives.
Step 3: Intelligent Data Entry
Information being entered checked for internal logic.
The Data Entry 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.
Optical Scanning
A data processing device that can “read”response
questionnaires
6
Learning Objectives
Step 4:Scanning
Machine
Optical
Cleaning of Data
To understand the data-entry process
and data-entry alternatives.
Step 4: Machine Cleaning of Data
A final computerized error check of 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.
7
Learning Objectives
Step 5: Tabulation
of Survey Results
To learn how surveys are tabulated.
One Way Frequency Tables
A table showing the number of responses to each
answer.
Base for Percentages
1. Total respondents
2. Number of people asked the question
3. Number of people answering the question
8
Learning Objectives
Tabulation of
Survey Results
To learn how to set up and
interpret crosstabulations.
Crosstabulations
Examination of the responses of one question relative to
responses to one or more other questions.
Provides a powerful and easily understood approach to the
summarization and analysis of survey research results.
9
Learning Objectives
Graphic Representations
of Data
To comprehend the basic
techniques of statistical analysis.
Line Charts
The simplest form of graphs.
Pie Charts
Appropriate for displaying marketing research results in a
wide range of situations.
Bar Charts
1. Plain bar chart (best for proportional relationships)
2. Clustered bar charts
3. Stacked bar charts
4. Multiple row, three-dimensional bar charts
(best for crosstabulations)
10
Learning Objectives
To comprehend the basic
techniques of statistical analysis.
Descriptive Statistics
Measures of Central Tendency
• Mean
h
X
where
=

fiXi
I=1
n
fi = the frequency of the ith class
Xi = the midpoint of that class
h = the number of classes
n = the total number of observations
11
Learning Objectives
Descriptive Statistics
To comprehend the basic
techniques of statistical analysis.
Measures of Central Tendency
• Median
The observation below which 50 percent of the
observations fall.
• Mode
The value that occurs most frequently
12
Learning Objectives
Descriptive Statistics
To comprehend the basic
techniques of statistical analysis.
Measures of Dispersion
Standard deviation
Calculated by:
• subtracting the mean of a series from each value in a
series
• squaring each result
• summing them
• dividing by the number of items minus 1
• and taking the square root of this value.
13
Learning Objectives
To comprehend the basic
techniques of statistical analysis.
Descriptive Statistics
Measures of Dispersion
Standard deviation (continued)
S =
where
√
n

(Xi - X) 2
I=1
n-1
S = sample standard deviation
Xi = the value of the ith observation
X = the sample mean
n = the sample size
14
Learning Objectives
Descriptive Statistics
To comprehend the basic
techniques of statistical analysis.
Measures of Dispersion
Variance
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
15
Learning Objectives
Descriptive Statistics
To comprehend the basic
techniques of statistical analysis.
Means, Percentages, and Statistical Tests
Choice: Whether to use measures
of central tendency or percentages.
Responses are either categorical (e.g., 1 = Atlanta, 2 = NY,
etc.) or take the form of continuous variables (e.g., weight)
(Variables such as age can be continuous or categorical.)
For categories, one-way frequency distributions and
crosstabulations are the most obvious choices.
Continuous data can be put into categories (and means
calculated)
16
Learning Objectives
SUMMARY
• Validation and Editing
• Coding
• Data Entry
• Optical Scanning
• Machine Cleaning of Data
• Tabulation of Survey Results
• Graphic Representations of Data
• Descriptive Statistics
17
Learning Objectives
The End
Copyright © 2002 South-Western/Thomson Learning
18