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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 subject is a single member of the sample, just as an
element is a single member of the population.

CBEB2105                                                         1
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.

CBEB2105                                                          2
Statistics versus Parameters

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   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 a
known and non-zero chance of being chosen

CBEB2105                                                          6
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

CBEB2105                                                                       8
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

CBEB2105                                                                               9
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

CBEB2105                                                       10
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

CBEB2105                                                       11
(Dis)proportionate Stratified Sampling

 Number of subjects in total sample is allocated among the strata
(dis)proportional to the relative number of elements in each
stratum 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

CBEB2105                                                                               12
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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 We can increase both confidence and precision by
increasing the sample size

CBEB2105                                                      18
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

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Sample Size for a Given Population
Size

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Sample Size for a Given

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Measurement
 Measurement: the assignment of numbers or other
symbols to characteristics (or attributes) of objects
according to a pre-specified set of rules.

CBEB2105                                                        22
(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.

CBEB2105                                                         23
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 subjective
nature.

CBEB2105                                                              24
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 the
concept.

CBEB2105                                                    25
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.

CBEB2105                                                      27
Nominal Scale
 A nominal scale is one that allows the researcher to assign subjects to certain
categories or groups.

O Marketing              O Maintenance              O Finance
O Production             O Servicing                O Personnel
O Sales                  O Public Relations O Accounting

O Male
O Female

CBEB2105                                                                              28
Nominal Scale

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Ordinal Scale
 Ordinal scale: not only categorizes variables in such a way as to
denote differences among various categories, it also rank-orders
categories 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, the
interval scale lets us measure the distance between any
two points on the scale.

CBEB2105                                                       32
Interval scale
   Circle the number that represents your feelings at this particular moment best. There

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

I disagree completely    1 2 3 4 5 I agree completely

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

I disagree completely    1 2 3 4 5 I agree completely

CBEB2105                                                                                          33
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.

CBEB2105                                                               35
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).

CBEB2105                                                          40
Stability
 Stability: ability of a measure to remain the same over
time, despite uncontrollable testing conditions or the
state of the respondents themselves.
– Test–Retest Reliability: The reliability coefﬁcient obtained
with a repetition of the same measure on a second occasion.
– Parallel-Form Reliability: Responses on two comparable sets
of measures tapping the same construct are highly
correlated.

CBEB2105                                                                41
Internal Consistency
 Internal Consistency of Measures is indicative of the
homogeneity of the items in the measure that tap the
construct.
– Interitem Consistency Reliability: This is a test of the
consistency of respondents’ answers to all the items in a
measure. The most popular test of interitem consistency
reliability is the Cronbach’s coefﬁcient alpha.
– Split-Half Reliability: Split-half reliability reflects the
correlations between two halves of an instrument.

CBEB2105                                                                   42