Selecting Sampling Techniques-Modern Research Methods-Lecture Slides, Slides of Research Methods for Managers

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• Lecture -

Probability sampling

and

Non-probability sampling

• Probability samples: ones in which members of the population have a known chance (probability) of being selected
• Non-probability samples: instances in which the chances (probability) of selecting members from the population are unknown
• Probability sampling requires a sampling frame, and when a sampling frame is not possible, non-probability sampling is used
• Sampling frame: Sampling frame is a complete list of all the cases in the population, from which sample will be drawn.
• Where no suitable list exists, researcher will have to compile his/her own sampling frame.
• It is important to ensure that sampling frame is unbiased, current and accurate. Remember sampling frame error; sampling frame error?

Simple Random Sampling

• Simple random sampling: the probability of being selected is

“known and equal” for all members of the population

• Blind Draw Method (e.g. putting names “placed in a hat” and then drawn randomly)
• Random Numbers Method (all items in the sampling frame given numbers, numbers then drawn using table or computer program)
• Advantages:
• Known and equal chance of selection
• Easy method when there is an electronic database
• Disadvantages: (Overcome with electronic database)
• Complete accounting of population needed
• Cumbersome to provide unique designations to every population member

Systematic Sampling

Systematic sampling: way to select a probability-based sample from a directory or list. This method is at times more efficient than simple random sampling; here sampling interval is used

Sampling interval (SI) = population list size (N) divided by a predetermined sample size (n)

How to draw:

• 1. Calculate SI,
• 2. Select a number between 1 and SI randomly,
• 3. Go to this number as the starting point and the item on the list here is the first in the sample,
• 4. Add SI to the position number of this item and the new position will be the second sampled item,
• 5. continue this process until desired sample size is reached.

Cluster Sampling – Area Method

Drawing the area sample

• Divide the geo area into sectors (sub-areas)

and give them names/numbers, determine

how many sectors are to be sampled

(typically a judgment call), randomly select

these sub-areas. Do either a census or a

systematic draw within each area.

• To determine the total geo area estimate,

add the counts in the sub-areas together and

multiply this number by the ratio of the

total number of sub-areas divided by

number of sub-areas.

Stratified sampling The population is separated into homogeneous groups/segments/strata and a sample is taken from each. The results are then combined to get the picture of the total population.

This method is used when the population distribution of items is skewed.

It allows us to draw a more representative sample. Hence, if there are more of certain type of items in the population, the sample will have more of this type; and if there are fewer of another type, there will be fewer of that type, in the sample.

Judgment samples

Samples that require a judgment or an

“educated guess” on the part of the

researcher as to who should represent

the population.

Also, “judges” (informed individuals)

may be asked to suggest who should be

in the sample.

Subjectivity enters in here, and certain

members of the population will have a

smaller, lottle or no chance of

selection compared to others

Referral and Quota Sampling Methods

• Referral samples (snowball samples): samples

which require respondents to provide the names

of additional respondents

• Members of the population who are less

known, disliked, or whose opinions conflict

with the respondent have a low probability of

being selected.

• Quota samples: samples that set a specific

number of certain types of individuals to be

interviewed

• Often used to ensure that convenience samples

will have desired proportion of different

respondent classes

Credibility of Research Findings

Important considerations

Reliability?

Validity?

Generalizability?

Validity

Whether the findings are really about

what they appear to be about.

Validity depends upon:

* History (same history or not),

* Testing (if respondents know they

are being tested),

* Mortality (participants’ dropping

out),

* Maturation (tiring up), and

* Ambiguity (about causal direction).

Generalizability

The extent to which research results

are generalizable.

Logic leaps and false

assumptions

Research design is based on a flow

of logic

and number of assumptions,

which must stand to closest scrutiny

SPSS Exercise 4 (a)

Testing reliability

(of the instrument used/of the raw data on

responses)

On Topic

An investigation into Pakistani

organizations with special reference to

the prevalence of organizational justice

and its outcome in terms of employees’

job satisfaction

SPSS Exercise 4 (b)

How is reliability test interpreted?

Reliability test results

Responses on the elements of all five constructs (JS, DJ, PJ, Ij & INJ) were entered on SPSS data editor and reliability tests were conducted; the following Cronbach’s Alphas were estimated. Table 4.4 Results of reliability test Construct Cronbach’s Alpha Job Satisfaction (JS) 0. Distributive Justice (DJ) 0. Procedural Justice (PJ) 0. Interactional Justice (IJ) 0. Informational Justice (INJ) 0.

Interpretation » According to Uma Sekaran (2003), the closer the reliability coefficient Cronbach’s Alpha gets to 1.0, the better is the reliability. In general, reliability less than 0.60 is considered to be poor, that in the 0.70 range, acceptable, and that over 0. and 0.90 are good and very good. The reliability tests of our constructs happened to be in the acceptable to good and very good ranges.