Encuestas de Comunicación Interna entre colaboradores de la

UNIT 4: SAMPLING

by a much larger pool of cases, may be one of the disbelief. It sounds too good to be true. But sampling is powerful and it works. With a well- conducted sample, a researcher can measure variables with 2,000 cases, generalize to 200 million, and not be off by more than 2 to 4 percent from the results that would be obtained if all 200 million were used.

How is it possible to use so few cases to generalize accurately to so many?

It is not based on trickery or magic but on logical statistical reasoning that has been tested repeatedly with empirical evidence. Moreover, a researcher cannot use just any sample is generalize accurately. The sample must be selected according to precise procedure, and statements made about it are subject to limitations.

3.1.1 Populations, Elements and Sampling Frames

A researcher draws a sample from a larger pool of cases, or elements. A sampling element is the unit of analysis or case in a population. It can be a person, a group, an organization, a written document nor symbolic message, or even a social action (e.g. an arrest, a divorce, a kiss) that is being measured. The large pool is the population which has an important role in sampling. Sometimes the term universe (defined in chapter 6) is used interchangeably with population. To define the population, a researcher specifies the unit being sampled, the geographical location and the temporal boundaries of populations. Consider the examples of population in Box 10.1. All the examples include the elements to be sampled (people, businesses, hospital admissions, commercials) and geographical and time boundaries.

A researcher begins with an idea of the population (e.g. all people in a city) but defines it more precisely. The term target population refers to the specific pool of cases that she wants to study. The ratio of the size of the sample to the size of the target population is the sampling ratio. For example, the population has 50,000 people, and a researcher draws a sample of 150 from it. Her sampling ratio is 150050.000 = 0.003 or 03%.

3.1.2 Non-probability Sampling

Samples can be divided into two groups: those that are based on the principles of randomness from probability theory, and those that are not.

Sampling based on probability theory test a researcher say precise things about sampling and use powerful statistics. Samples that are not based on probability theory are more limited. A researcher uses them out of

ignorance, because of a lack of time, or in special situations. Except for special situations, quantitative researchers prefer probability samples.

3.1.3 Types of Samples:

Non-Probability Probability

Haphazard: Select anyone who is Simple: Select people based

on on

convenient a true random procedure

Quota: Select anyone in predetermined Systematic: Select every person

groups (quasi-random)

Snowball: Select people connected to Systematic: Randomly select

one another people in predetermined

groups

Purposive: Select anyone in a hard-to- Cluster: Take multistage random

find target population samples in each several

levels

She selected some because they had low incomes and some because they had high incomes. Some were male and some were female.

In this study of the political influence of corporate elites, Michael Useem (1984) used a type of quota and purposive sampling. He interviewed 72 directors of major British corporations and 57 officials from large U.S firms. He chose the sample to include both U.S and British firms and to include some directors who sat on the boards of more than one firm In addition, he matched firms by industry and size, and limited geographical locations in order to reduce travel costs.

3.1.4 Snowball Sampling

Social researchers are often interested in an interconnected network of people or organizations. The network could be scientific around the world investigating the same problem, the elites of a medium-sized city,

boards of directors of major banks and corporations, or people on a college campus who have had sexual relations with each other. The crucial feature is that each person or unit is connected with another through a director or indirect linkages. This does not mean that each person directly knows, interacts with, or is influenced by every other person in the network. Rather, it means that taken as a whole, with direct and indirect links, most are within an interconnected web of linkages.

3.1.5 Types of Probability Samples

Simple Random: The simple random sample is both the easiest random sample to understand and the one on which other types are modeled. In simple random sampling, a researcher develops an accurate sampling frame, selects elements from the sampling frame according to a mathematically random procedure, then locates the exact element that was selected for inclusion in the sample.

After numbering all elements in a sampling frame, a researcher uses a list of random numbers to decide which elements to select. She needs as many random numbers as there are elements to be sampled, for example, for a sample of 100 you need 100 random numbers. She can get random numbers from a random number table, a table of numbers chosen in a mathematically random way. Random number tables are available in most statistics and research methods books, including this one (see Appendix B). The numbers are generated by a pure random process so that any number has an equal probability of appearing in any position. Computer programs can also produce lists of random numbers.

4.0 CONCLUSION

We have known sampling as being widely used in social research, especially in survey research and non-reactive research techniques. We are now aware of four types of sampling that are not based on random processes: haphazard, quota, snowball and purposive, only the last two are acceptable, and even then their use depends on special circumstances.

5.0 SUMMARY

In general, probability sampling is preferred because it produces a sample that represents the population and enables the researcher the researcher to use powerful statistical techniques.

Likewise, sampling issues influence research design, measurement of variables and data collection strategies.

6.0 TUTOR MARKED ASSIGNMENT (TMA)

2. When is purpose sampling used?

7.0 REFERENCES/FURTHER READINGS

Davis, James A. (1985); Why Sampling?; Beverly Hills, CA: Sage.

Campbell, John and Charles L. Hulin (1982); What to Study: Generating and Developing Research Questions; Beverly Hills, CA: Sage.

MODULE 6

INTRODUCTION

Unit 1 Experimental Research Unit 2 Survey Research Unit 3 Field Research

Unit 4 Qualitative Research Design

UNIT 1: EXPERIMENTAL RESEARCH