Module 3 Case – SAMPLING

Module 3 – Case

Sampling

Case Assignment

Read the background materials for this module. After doing so, address the following questions in a four-page paper:

1. The sampling frame is arguably the most critical element of a study’s sampling plan. Why is this so?

2. How might a poorly specified sampling frame forestall the research process?

3. Are studies that employ convenience sampling invalid? Please explain.

Of the sampling methods presented in this module, which optimize external validity? If this term is unfamiliar, revisit the Module 2 home page. Please explain.

Assignment Expectations

1. You are expected to consult the scholarly literature in preparing your paper; you are also expected to incorporate relevant background readings.

2. Your paper should be written in your own words. This will enable me to assess your level of understanding.

3. In order to earn full credit, you must clearly show that you have read all required background materials.

4. Cite your references in the text of all papers and on the reference list at the end. For examples, look at the way the references are listed in the modules and on the background reading list.

5. Proofread your paper to be sure grammar and punctuation are correct and that each part of the assignment has been addressed clearly and completely.

6. Your assignment will not be graded until you have submitted an Originality Report with a Similarity Index (SI) score <20% (excluding direct quotes, quoted assignment instructions, and references). Papers not meeting this requirement by the end of the session will receive a score of 0 (grade of F). Do keep in mind that papers with a lower SI score may be returned for revisions. For example, if one paragraph accounting for only 10% of a paper is cut and pasted, the paper could be returned for revision, despite the low SI score. Please use the report and your SI score as a guide to improve the originality of your work.

Length: 4 pages typed, double-spaced.

Note: Wikipedia is not an acceptable source of information.

Module 3 – Home

Sampling

Modular Learning Outcomes

Upon successful completion of this module, the student will be able to satisfy the following outcomes:

· Case

· Delineate a study's sampling frame and sample.

· Distinguish the various probability and non-probability sampling methodologies.

· SLP

· Delineate a sampling frame and sample.

· Discussion

· Delineate a study's sampling frame and sample.

Module Overview

In this module, we will discuss types and levels of study variables as well as deriving a study sample. We will begin with some basic definitions.

Variables (Data)

Variables are measurable characteristics of people, objects, or events; the information we are describing and analyzing. The aggregate of our observations comprise our data set.

Operationalization: Specific manner in which one measures or manipulates variables in a study; defining variables so as to make them measurable. Click Operationalizing Variables to learn more about operationalization of study variables.

Types of Variables

Discrete:

A variable of a countable number of integer outcomes, e.g., "people’s choices of hospitals" (hospital A, B, or C) or "disease status" (diseased, non-diseased).

Categorical:

A variable made up of categories of objects/entities having no order, e.g., "gender" operationalized as male or female OR "hair color" defined as blonde, brown, brunette, red, etc.).

Continuous:

A variable that be measured to any level of precision (e.g., "time").

Independent Variables: studied for their potential or expected influence

Dependent Variables: the outcome, or influenced variables

Levels of Measurement (nominal, ordinal, interval, ratio)

Nominal (Discrete, Categorical):

Variables for which the set of all possible values falls into a finite set of mutually exclusive and exhaustive classes. The values of nominal variables need not be numerically meaningful: addition, subtraction, multiplication, and division do not necessarily make sense.

Examples: sex (male, female), color (red, yellow, blue, etc.), exposed/not exposed, with heart disease/without heart disease, marital status (single, married, widowed, divorced).

Variables with just 2 categories are also called dichotomous.

Ordinal or "Rank":

A nominal variable whose classes or categories have a natural, logical order.

Examples: quality (poor, fair, good, excellent), academic level (freshman, sophomore, junior, senior, graduate), frequency of behavior (never, rarely, often, very often), order of finish in an election, respiratory distress (absent, mild, moderate, severe).

Interval (Continuous Variables):

Interval Variables. For these variables all possible values are numbers, and subtraction makes sense (intervals are meaningful). Example: temperature.

Ratio (Continuous Variables):

Ratio. All possible values are numbers, and multiplication and division make sense (ratios are meaningful), i.e., zero (0) means "none.“ Examples: height, weight, number of children, blood pressure, grams of food.

Continuous variables can be transformed and analyzed as categorical variables by establishing cutoffs between ranges of values.

Example: height in inches can be converted into categories of height: short (72in).

Sampling Terminology

Sample: a number of individual cases that are drawn from a larger population.

Sampling Frame: the group of sampling units or elements from which a sample is actually selected; the list from which a sample is selected.

Population: The group to which the researcher wishes to generalize the findings of his or her study; also, the group she or he samples from in a study.

Probability Sample: a sample that gives every member of the population a known (nonzero) chance of being selected.

Non-probability Sample: a sample that has been drawn in a way that doesn’t give every member of the population a known chance of being selected. Probability samples are generally more representative of the populations from which they are drawn as compared with non-probability samples.

Probability Sampling

Simple Random Sampling: The most common type of probability sampling. Each member of the population has an equal and independent chance of being selected to be part of the sample.

Steps in simple random sampling:

1. Define the population from which you want to select the sample

2. List all the members of the population

3. Assign numbers to each member of the population

4. Establish criterion to select the sample you want and use random number tables

Systematic Sampling: involves selecting every kth element from a list of population elements, after the first element has been randomly selected.

Selection interval (k) = population size/sample size.

Steps in systematic sampling:

1. Determine the selection interval

2. Choose one observation on the list at random

3. Once the starting point is determined, use the selection interval to select your sample

Systematic sampling is easier than random sampling, but can introduce bias if the sampling frame is cyclical in nature.

Stratified Sampling: A procedure that involves dividing the population into groups or strata or sub-groups defined by the presence of certain characteristics and then random sampling from each of the strata.

Stratified Sampling can be done proportionately and disproportionately.

Cluster Sampling: A procedure that involves randomly selecting clusters of elements from a population and subsequently selecting every element in each cluster for inclusion in the sample.

Multistage Sampling: A procedure that involves several stages, such as randomly selecting clusters from a population, then randomly selecting elements from each of the clusters.

Non-probability Sampling

Quota Sampling: A non-probability sampling procedure that begins with a description of the target population (e.g., its proportion of males, females, of people of different age groups, education, etc.).

· It does not randomly select from the population a subset of all the elements.

· Usually done on a first-come first-included basis.

· Sampling stops when enough are included in each category.

Purposive Samples: A non-probability sampling procedure that involves selecting elements based on the researcher’s judgment about which elements will facilitate his or her investigation.

Used for many exploratory studies and qualitative research. It differs from quota sampling by restricting the sample population to a very specific population and then using all of the subjects available.

Snowball Sampling: A sampling procedure that involves using members of the group of interest to identify other members of the group; can be used when population listing are unavailable.

Convenience Sampling: This procedure involves selecting participants that are readily accessible to the researcher. Examples: those in attendance at a meeting/class; interviewing people in a mall; first 200 patients admitted to a medical unit.

Cheap, but potential for bias related to what motivated people to volunteer and segment of the population that is missed because they were not available. To read more go to recruitment of participants.

Module 3 – Background

Sampling

Available via ProQuest:

Nokes, K. M., & Nwakeze, P. C. (2007). Exploring research issues: In using a random sampling plan with highly marginalized populations. Journal of Multicultural Nursing & Health, 13(1), 6-9.

Richards, D. (2007). Types of data. Evidence – Based Dentistry, 8(2), 57-8.

Wolf, H. K., Kuulasmaa, K., Tolonen, H., Sans, S., Molarius, A., & Eastwood, B. J. (2005). Effect of sampling frames on response rates in the WHO MONICA risk factor surveys. European Journal of Epidemiology, 20(4), 293-9.

Available via the Internet:

Trochim, W. M. K. (2006). Sampling terminology. Retrieved from http://www.socialresearchmethods.net/kb/sampterm.php

Statistics Learning Center (2011, December 13). Types of Data: Nominal, Ordinal, Interval/Ratio – Statistics Help. Retrieved from https://youtu.be/hZxnzfnt5v8

Statistics Learning Center (2012, March 13). Sampling: Simple Random, Convenience, systematic, cluster, stratified – Statistics Help. Retrieved from https://youtu.be/be9e-Q-jC-0