Chapter 1 Introduction and Data Collection

a manager needs to know about statistics
The growth and development of modern statistics
Key definitions
Descriptive versus inferential statistics

data are needed
Types of data and their sources
Design of survey research
Types of sampling methods
Types of survey errors

(continued)

Manager Needs to Know about Statistics

To know how to properly present information
To know how to draw conclusions about populations based on sample information
To know how to improve processes
To know how to obtain reliable forecasts

and Development of Modern Statistics

Needs of government to collect data on its citizens

The development of the mathematics of probability theory

The advent of the computer

population (universe) is the collection of things under consideration
A sample is a portion of the population selected for analysis
A parameter is a summary measure computed to describe a characteristic of the population
A statistic is a summary measure computed to describe a characteristic of the sample

Sample

Population

Sample

Use parameters to summarize features

Use statistics to summarize features

Inference on the population from the sample

statistics
Collecting and describing data
Inferential statistics
Drawing conclusions and/or making decisions concerning a population based only on sample data

data
e.g. Survey
Present data
e.g. Tables and graphs
Characterize data
e.g. Sample mean =

Estimate the population mean weight using the sample mean weight
Hypothesis testing
e.g.: Test the claim that the population mean weight is 120 pounds

Drawing conclusions and/or making decisions concerning a population based on sample results.

Need Data

To provide input to survey
To provide input to study
To measure performance of service or production process
To evaluate conformance to standards
To assist in formulating alternative courses of action
To satisfy curiosity

Collection

Secondary
Data Compilation

Observation

Experimentation

Survey

Print or Electronic

Data

Survey Research

Choose an appropriate mode of response
Reliable primary modes
Personal interview
Telephone interview
Mail survey
Less reliable self-selection modes (not appropriate for making inferences about the population)
Television survey
Internet survey
Printed survey on newspapers and magazines
Product or service questionnaires

Survey Research

Identify broad categories
List complete and non-overlapping categories that reflect the theme
Formulate accurate questions
Make questions clear and unambiguous. Use universally-accepted definitions
Test the survey
Pilot test the survey on a small group of participants to assess clarity and length

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Survey Research

Write a cover letter
State the goal and purpose of the survey
Explain the importance of a response
Provide assurance of respondent’s anonymity
Offer incentive gift for respondent participation

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Drawing a Sample

Less time consuming than a census
Less costly to administer than a census
Less cumbersome and more practical to administer than a census of the targeted population

Sampling Methods

Quota

Samples

Non-Probability Samples

Judgement

Chunk

Probability Samples

Simple
Random

Systematic

Stratified

Cluster

of the sample are chosen based on known probabilities

Probability Samples

Simple
Random

Systematic

Stratified

Cluster

Samples

Every individual or item from the frame has an equal chance of being selected
Selection may be with replacement or without replacement
Samples obtained from table of random numbers or computer random number generators

sample size: n
Divide frame of N individuals into groups of k individuals: k=n/n
Randomly select one individual from the 1st group
Select every k-th individual thereafter

Systematic Samples

N = 64
n = 8
k = 8

First Group

divided into two or more groups according to some common characteristic
Simple random sample selected from each group
The two or more samples are combined into one

divided into several “clusters,” each representative of the population
Simple random sample selected from each
The samples are combined into one

Population divided into 4 clusters.

Disadvantages

Simple random sample and systematic sample
Simple to use
May not be a good representation of the population’s underlying characteristics
Stratified sample
Ensures representation of individuals across the entire population
Cluster sample
More cost effective
Less efficient (need larger sample to acquire the same level of precision)

Worthiness

What is the purpose of the survey?
Is the survey based on a probability sample?
Coverage error – appropriate frame
Nonresponse error – follow up
Measurement error – good questions elicit good responses
Sampling error – always exists

Survey Errors

Coverage error
Non response error
Sampling error
Measurement error

Excluded from frame.

Follow up on non responses.

Chance differences from sample to sample.

Bad Question!

why a manager needs to know about statistics
Discussed the growth and development of modern statistics
Addressed the notion of descriptive versus inferential statistics
Discussed the importance of data