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Week 6

Sampling Design

Measurement and Scaling

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Sampling

Sampling: theprocess of selecting a sufficientnumber of

elements from the population, so that results from analyzing the

sample are generalizable to the population.

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Relevant Terms - 1

Population refers to the entire group of people, events,

or things of interest that the researcher wishes to

investigate.

An elementis a single member of the population.

A sample is a subset of the population. It comprisessome members selected from it.


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Relevant Terms - 2

Sampling unit:the element or set of elements that is

available for selection in some stage of the sampling

process.

A subjectis a single member of the sample, just as an

element is a single member of the population.


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Relevant Terms - 3

The characteristics of the population such as (the

population mean), (the population standard

deviation), and 2 (the population variance) are referred

to as itsparameters. The central tendencies, thedispersions, 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.


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Statistics versus Parameters


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Advantages of Sampling

Less costs

Less errors due to less fatigue

Less time Destruction of elements avoided


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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


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Sampling Techniques

Probability versus nonprobability sampling

Probability sampling: elements in the population have aknown and non-zero chance of being chosen


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Sampling Techniques

Probability Sampling

Simple Random Sampling

Systematic Sampling

Stratified Random Sampling

Cluster Sampling

Nonprobability Sampling

Convenience Sampling Judgment Sampling

Quota Sampling


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Simple Random Sampling

Procedure

Each element has a known and equal chance of being selected

Characteristics

Highly generalizable

Easily understood

Reliable population frame necessary


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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


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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


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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


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(Dis)proportionate Stratified Sampling

Number of subjects in total sample is allocated among the strata(dis)proportional to the relative number of elements in eachstratum in the population

Disproportionate case: strata exhibiting more variability are sampled more than proportional to

their relative size

requires more knowledge of the population, not just relative sizes of strata

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Example

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Overview

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Overview

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Overview

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Choice Points in Sampling Design

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Tradeoff between precision and confidence

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We can increase both confidence and precision byincreasing the sample size

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Sample size: guidelines

In general: 30 < n < 500

Categories: 30 per subcategory

Multivariate: 10 x number of vars

Experiments: 15 to 20 per condition

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Sample Size for a Given

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Measurement

Measurement: the assignment of numbers or other

symbols to characteristics (orattributes) ofobjects

according to a pre-specified set of rules.

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(Characteristics of) Objects

Objects include persons, strategic business units,

companies, countries, kitchen appliances, restaurants,

shampoo, yogurt and so on.

Examples of characteristics of objects are arousal

seeking tendency, achievement motivation,

organizational effectiveness, shopping enjoyment,

length, weight, ethnic diversity, service quality,conditioning effects and taste.

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Types of Variables

Two types of variables:

One lends itself to objective and precise measurement;

The other is more nebulous and does not lend itself to

accurate measurement because of its abstract and subjectivenature.

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Operationalizing Concepts

Operationalizing concepts: reduction of abstract

concepts to render them measurable in a tangible way.

Operationalizing is done by looking at the behavioral

dimensions, facets, or properties denoted by theconcept.

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Example

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Scale

Scale: tool or mechanism by which individuals are

distinguished as to how they differ from one another on

the variables of interest to our study.

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Nominal Scale

A nominal scale is one that allows the researcher to assign subjects to certaincategories or groups.

What is your department?

O Marketing O Maintenance O FinanceO Production O Servicing O Personnel

O Sales O Public Relations O Accounting

What is your gender?

O MaleO Female

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Nominal Scale

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Ordinal Scale

Ordinal scale: not only categorizes variables in such a way as todenote differences among various categories, it also rank-orderscategories in some meaningful way.

What is the highest level of education you have completed?O Less than High School

O High School/GED Equivalent

O College Degree

O Masters Degree

O Doctoral Degree

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Ordinal Scale

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Interval Scale

Interval scale: whereas the nominal scale allows us only

to qualitatively distinguish groups by categorizing them

into mutually exclusive and collectively exhaustive sets,

and the ordinal scale to rank-order the preferences, theinterval scale lets us measure the distance between any

two points on the scale.

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Interval scale

Circle the number that represents your feelings at this particular moment best. Thereare no right or wrong answers. Please answer every question.

1. I invest more in my work than I get out of it

I disagree completely 1 2 3 4 5 I agree completely

2. I exert myself too much considering what I get back in return


3. For the efforts I put into the organization, I get much in return


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Interval scale

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Ratio Scale

Ratio scale: overcomes the disadvantage of the

arbitrary origin point of the interval scale, in that it has

an absolute (in contrast to an arbitrary) zero point,

which is a meaningful measurement point.

What is your age?

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Ratio Scale

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Properties of the Four Scales

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Goodness of Measures

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Validity

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Reliability

Reliability of measure indicates extent to which it is

without bias and hence ensures consistent

measurement across time (stability) and across the

various items in the instrument (internal consistency).

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Stability

Stability: ability of a measure to remain the same overtime, despite uncontrollable testing conditions or thestate of the respondents themselves.

TestRetest Reliability:

The reliability coefficient obtainedwith a repetition of the same measure on a second occasion.

Parallel-Form Reliability: Responses on two comparable setsof measures tapping the same construct are highlycorrelated.

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Internal Consistency

Internal Consistency of Measuresis indicative of the

homogeneity of the items in the measure that tap the

construct.

Interitem Consistency Reliability:This is a test of theconsistency of respondents answers to all the items in a

measure. The most popular test of interitem consistency

reliability is the Cronbachs coefficient alpha.

Split-Half Reliability:Split-half reliability reflects thecorrelations between two halves of an instrument.

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