Determining Sample Size Page 2

Figure 1. Distribution of Means for Repeated

Samples.

Degree Of Variability

The third criterion, the degree of variability in the

attributes being measured refers to the distribution of

attributes in the population. The more heterogeneous

a population, the larger the sample size required to

obtain a given level of precision. The less variable

(more homogeneous) a population, the smaller the

sample size. Note that a proportion of 50% indicates

a greater level of variability than either 20% or 80%.

This is because 20% and 80% indicate that a large

majority do not or do, respectively, have the attribute

of interest. Because a proportion of .5 indicates the

maximum variability in a population, it is often used

in determining a more conservative sample size, that

is, the sample size may be larger than if the true

variability of the population attribute were used.

STRATEGIES FOR DETERMINING

SAMPLE SIZE

There are several approaches to determining the

sample size. These include using a census for small

populations, imitating a sample size of similar studies,

using published tables, and applying formulas to

calculate a sample size. Each strategy is discussed

below.

Using A Census For Small Populations

One approach is to use the entire population as

the sample. Although cost considerations make this

impossible for large populations, a census is attractive

for small populations (e.g., 200 or less). A census

eliminates sampling error and provides data on all the

individuals in the population. In addition, some costs

such as questionnaire design and developing the

sampling frame are "fixed," that is, they will be the

same for samples of 50 or 200. Finally, virtually the

entire population would have to be sampled in small

populations to achieve a desirable level of precision.

Using A Sample Size Of A Similar Study

Another approach is to use the same sample size

as those of studies similar to the one you plan.

Without reviewing the procedures employed in these

studies you may run the risk of repeating errors that

were made in determining the sample size for another

study. However, a review of the literature in your

discipline can provide guidance about "typical" sample

sizes which are used.

Using Published Tables

A third way to determine sample size is to rely on

published tables which provide the sample size for a

given set of criteria. Table 1 and Table 2 present

sample sizes that would be necessary for given

combinations of precision, confidence levels, and

variability. Please note two things. First, these

sample sizes reflect the number of obtained responses,

and not necessarily the number of surveys mailed or

interviews planned (this number is often increased to

compensate for nonresponse). Second, the sample

sizes in Table 2 presume that the attributes being

measured are distributed normally or nearly so. If

this assumption cannot be met, then the entire

population may need to be surveyed.

Using Formulas To Calculate A Sample Size

Although tables can provide a useful guide for

determining the sample size, you may need to

calculate the necessary sample size for a different

combination of levels of precision, confidence, and

variability. The fourth approach to determining

sample size is the application of one of several

formulas (Equation 5 was used to calculate the

sample sizes in Table 1 and Table 2).

Determining Sample Size Page 3

Table 1. Sample size for ±3%, ±5%, ±7% and ±10%

Precision Levels Where Confidence Level is 95% and

P=.5.

Size of Sample Size (n) for Precision (e) of:

Population

±3% ±5% ±7% ±10%

500 a 222 145 83

600 a 240 152 86

700 a 255 158 88

800 a 267 163 89

900 a 277 166 90

1,000 a 286 169 91

2,000 714 333 185 95

3,000 811 353 191 97

4,000 870 364 194 98

5,000 909 370 196 98

6,000 938 375 197 98

7,000 959 378 198 99

8,000 976 381 199 99

9,000 989 383 200 99

10,000 1,000 385 200 99

15,000 1,034 390 201 99

20,000 1,053 392 204 100

25,000 1,064 394 204 100

50,000 1,087 397 204 100

100,000 1,099 398 204 100

>100,000 1,111 400 204 100

a = Assumption of normal population is poor (Yamane,

1967). The entire population should be sampled.

Formula For Calculating A Sample For

Proportions

For populations that are large, Cochran (1963:75)

developed the Equation 1 to yield a representative

sample for proportions.

Which is valid where n

is the sample size, Z

is the

abscissa of the normal curve that cuts off an area α at

the tails (1 - α equals the desired confidence level,

e.g., 95%)

, e is the desired level of precision, p is the

estimated proportion of an attribute that is present in

the population, and q is 1-p. The value for Z is

found in statistical tables which contain the area

under the normal curve.

To illustrate, suppose we wish to evaluate a state-

Table 2. Sample size for ±5%, ±7% and ±10% Precision

Levels Where Confidence Level is 95% and P=.5.

Size of Sample Size (n) for Precision (e) of:

Population

±5% ±7% ±10%

100 81 67 51

125 96 78 56

150 110 86 61

175 122 94 64

200 134 101 67

225 144 107 70

250 154 112 72

275 163 117 74

300 172 121 76

325 180 125 77

350 187 129 78

375 194 132 80

400 201 135 81

425 207 138 82

450 212 140 82

wide Extension program in which farmers were

encouraged to adopt a new practice. Assume there is

a large population but that we do not know the

variability in the proportion that will adopt the

practice; therefore, assume p=.5 (maximum

variability). Furthermore, suppose we desire a 95%

confidence level and ±5% precision. The resulting

sample size is demonstrated in Equation 2.

Finite Population Correction For Proportions

If the population is small then the sample size can

be reduced slightly. This is because a given sample

size provides proportionately more information for a

small population than for a large population. The

sample size (n

) can be adjusted using Equation 3.

Where n is the sample size and N is the population

size.

Determining Sample Size Page 4

Suppose our evaluation of farmers’ adoption of

the new practice only affected 2,000 farmers. The

sample size that would now be necessary is shown in

Equation 4.

As you can see, this adjustment (called the finite

population correction) can substantially reduce the

necessary sample size for small populations.

A Simplified Formula For Proportions

Yamane (1967:886) provides a simplified formula

to calculate sample sizes. This formula was used to

calculate the sample sizes in Tables 2 and 3 and is

shown below. A 95% confidence level andP=.5are

assumed for Equation 5.

Where n is the sample size, N is the population size,

and e is the level of precision. When this formula is

applied to the above sample, we get Equation 6.

Formula For Sample Size For The Mean

The use of tables and formulas to determine

sample size in the above discussion employed

proportions that assume a dichotomous response for

the attributes being measured. There are two

methods to determine sample size for variables that

are polytomous or continuous. One method is to

combine responses into two categories and then use

a sample size based on proportion (Smith, 1983).

The second method is to use the formula for the

sample size for the mean. The formula of the sample

size for the mean is similar to that of the proportion,

except for the measure of variability. The formula for

the mean employs σ

instead of (p x q), as shown in

Equation 7.

Where n

is the sample size, z is the abscissa of the

normal curve that cuts off an area α at the tails, e is

the desired level of precision (in the same unit of

measure as the variance), and σ

is the variance of an

attribute in the population.

The disadvantage of the sample size based on the

mean is that a "good" estimate of the population

variance is necessary. Often, an estimate is not

available. Furthermore, the sample size can vary

widely from one attribute to another because each is

likely to have a different variance. Because of these

problems, the sample size for the proportion is

frequently preferred

OTHER CONSIDERATIONS

In completing this discussion of determining

sample size, there are three additional issues. First,

the above approaches to determining sample size have

assumed that a simple random sample is the sampling

design. More complex designs, e.g., stratified random

samples, must take into account the variances of

subpopulations, strata, or clusters before an estimate

of the variability in the population as a whole can be

made.

Another consideration with sample size is the

number needed for the data analysis. If descriptive

statistics are to be used, e.g., mean, frequencies, then

nearly any sample size will suffice. On the other

hand, a good size sample, e.g., 200-500, is needed for

multiple regression, analysis of covariance, or log-

linear analysis, which might be performed for more

rigorous state impact evaluations. The sample size

should be appropriate for the analysis that is planned.

In addition, an adjustment in the sample size may

be needed to accommodate a comparative analysis of

subgroups (e.g., such as an evaluation of program

participants with nonparticipants). Sudman (1976)

suggests that a minimum of 100 elements is needed

for each major group or subgroup in the sample and

for each minor subgroup, a sample of 20 to 50

elements is necessary. Similarly, Kish (1965) says that

30 to 200 elements are sufficient when the attribute is

present 20 to 80 percent of the time (i.e., the

distribution approaches normality). On the other

hand, skewed distributions can result in serious

departures from normality even for moderate size

samples (Kish, 1965:17). Then a larger sample or a

census is required.

Finally, the sample size formulas provide the

number of responses that need to be obtained. Many

researchers commonly add 10% to the sample size to

compensate for persons that the researcher is unable

Determining Sample Size Page 5

to contact. The sample size also is often increased by

30% to compensate for nonresponse. Thus, the

number of mailed surveys or planned interviews can

be substantially larger than the number required for

a desired level of confidence and precision.

ENDNOTES

1. The area α corresponds to the shaded areas in

the sampling distribution shown in Figure 1.

2. The use of the level of maximum variability

(P=.5) in the calculation of the sample size for

the proportion generally will produce a more

conservative sample size (i.e., a larger one) than

will be calculated by the sample size of the mean.

REFERENCES

Cochran, W. G. 1963. Sampling Techniques, 2nd Ed.,

New York: John Wiley and Sons, Inc.

Israel, Glenn D. 1992. Sampling The Evidence Of

Extension Program Impact. Program Evaluation

and Organizational Development, IFAS,

University of Florida. PEOD-5. October.

Kish, Leslie. 1965. Survey Sampling. New York:

John Wiley and Sons, Inc.

Miaoulis, George, and R. D. Michener. 1976. An

Introduction to Sampling. Dubuque, Iowa:

Kendall/Hunt Publishing Company.

Smith, M. F. 1983. Sampling Considerations In

Evaluating Cooperative Extension Programs.

Florida Cooperative Extension Service Bulletin

PE-1. Institute of Food and Agricultural Sciences.

University of Florida.

Sudman, Seymour. 1976. Applied Sampling. New

York: Academic Press.

Yamane, Taro. 1967. Statistics, An Introductory

Analysis, 2nd Ed., New York: Harper and Row.