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 (<60), medium=”” 60=”” height=”” 72=”” tall=””>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