A consultant had administered a questionnaire to some 285 employees using a simple random sampling procedure. As she looked at the responses, she suspected that two questions might not have been clear to the respondents. Based on what you have learned in this session, Explain what should she do to find out if her suspicion is correct?

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Chapter 13

Sampling

Slide 13-2

Sampling

Sampling: the process of selecting a sufficient

number of elements from the population, so that

results from analyzing the sample are

generalizable to the population.

The reasons for using a sample are self-evident.

In research involving hundreds or even

thousands of elements, it would be practically

impossible to collect data from every element.

Even if it were possible, it would be prohibitive in

terms of time, cost, and other human resources.

Slide 13-3

Relevant Terms – 1

Population refers to the entire group of

people, events, or things of interest that the

researcher wishes to investigate.

An element is a single member of the

population.

A sample is a subset of the population. It

comprises some members selected from it.

Slide 13-4

Relevant Terms – 2

Sampling unit: the element or set of elements

that is available for selection in some stage

of the sampling process.

A subject is a single member of the sample,

just as an element is a single member of the

population.

Slide 13-5

Relevant Terms – 3

The characteristics of the population such as

µ (the population mean), σ (the population

standard deviation), and σ2 (the population

variance) are referred to as its parameters.

The central tendencies, the dispersions, and

other statistics in the sample of interest to the

research are treated as approximations of

the central tendencies, dispersions, and

other parameters of the population.

Slide 13-6

Statistics versus Parameters

Slide 13-7

Advantages of Sampling

Less costs

Less errors due to less fatigue

Less time

Destruction of elements avoided

Slide 13-8

The Sampling Process

Major steps in sampling:

Define the population.

Determine the sample frame

Determine the sampling design

Determine the appropriate sample size

Execute the sampling process

Slide 13-9

Sampling Techniques

Probability versus nonprobability sampling

Probability sampling: elements in the

population have a known and non-zero

chance of being chosen

Slide 13-10

Sampling Techniques

Probability Sampling

Simple Random Sampling

Systematic Sampling

Stratified Random Sampling

Cluster Sampling

Nonprobability Sampling

Convenience Sampling

Judgment Sampling

Quota Sampling

Slide 13-11

Simple Random Sampling

Procedure

Each element has a known and equal

chance of being selected

Characteristics

Highly generalizable

Easily understood

Reliable population frame necessary

Slide 13-12

Systematic Sampling

Procedure

Each nth element, starting with random

choice of an element between 1 and n

Characteristics

Idem simple random sampling

Easier than simple random sampling

Systematic biases when elements are not

randomly listed

Slide 13-13

Cluster Sampling

Procedure

Divide of population in clusters

Random selection of clusters

Include all elements from selected

clusters

Characteristics

Intercluster homogeneity

Intracluster heterogeneity

Easy and cost efficient

Low correspondence with reality

Slide 13-14

Stratified Sampling

Procedure

Divide of population in strata

Include all strata

Random selection of elements from

strata

Proportionate

Disproportionate

Characteristics

Interstrata heterogeneity

Intrastratum homogeneity

Includes all relevant subpopulations

Slide 13-15

Overview

Slide 13-16

Tradeoff Between Precision and Confidence

We can increase both confidence and precision by

increasing the sample size

Slide 13-17

Sample Size: Guidelines

In general:

30 < n < 500
Categories:
30 per subcategory
Multivariate:
10 x number of var’s
Experiments:
15 to 20 per condition
Slide 13-18
Sample Size for a Given Population Size
Slide 13-19
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