QUOTA SAMPLING

 

INTRODUCTION: Quota sampling is a nonprobability convenience sampling technique in which the proportion of identified groups is predetermined by the researchers. Quota sampling may be used to ensure the inclusion of subject types or strata in a population that are likely to be underrepresented in the convenience sample, such as women, minority groups, elderly adults, poor people, rich people, and undereducated adults. This method may also be used to mimic the known characteristics of the target population or to ensure adequate numbers of subjects in each stratum for the planned statistical analyses.

The technique is similar to the one used in stratified random sampling, but the initial sample is not random. If necessary, mathematical weighting can be used to adjust sample values so that they are consistent with the proportion of subgroups found in the population.

ADVANTAGES: Quota sampling offers an improvement over convenience sampling and tends to decrease potential biases. In most studies in which convenience samples are used, quota sampling could be used and should be considered.

RELATED;

1.  Probability sampling designs  

2.  Stratified random sampling

3.  Research methodology

REFERENCES

STRATEGIES FOR DETERMINING SAMPLE SIZE

Introduction:  Previously we have been looking at the number of respondents a medical researcher will need to collect data from and this number will be predetermined for quantitative studies.  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.

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, acensus 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.  Such tables 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 non response). Second, the sample sizes in some of these tables 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.

RELATED;

1.  FORMULAE FOR DETERMINATION OF SAMPLE SIZE  

2.  WRITTING FORMULAE IN MS WORD

3.  HOW TO WRITE A MEDICAL RESEARCH PROPOSAL

CLUSTER SAMPLING

Cluster sampling is based on the ability of the researcher to divide a sampling population into groups (based upon a visible or easily identifiable characteristics), called clusters, and then select elements from each cluster using the SRS technique. Clusters can be formed on the basis of geographical proximity or a common characteristic that has a correlation with the main variable of the study (as in stratified sampling). Depending on the level of clustering, sometimes sampling may be done at different levels. These levels constitute the different stages (single, double or multiple) of clustering.

RELATED;

1.  NON PROBABILITY SAMPLING  

2.  ACCIDENTAL SAMPLING,

RANDOMIZATION

Introduction: Randomization involves placing subjects in groups at random. Random essentially means that every subject has an equal chance of being assigned to any group. If subjects are placed in groups randomly, there is no systematic bias in the groups with respect to attributes that could affect the dependent variable. Dependent variable

Let us consider the purpose of random assignment. Suppose we wanted to study the effectiveness of a contraceptive counseling program for multiparous women who have just given birth. Two groups of subjects are included one will be counseled and the other will not. The women in the sample are likely to differ from one another in many ways, such as age, marital status, financial situation, attitudes toward child-rearing, and others. Any of these characteristics could affect a woman’s diligence in practicing contraception, independent of whether she receives counseling. We need to have the “counsel” and “no counsel” groups equal with respect to these extraneous characteristics to assess the impact of the experimental counseling program on subsequent pregnancies. The random assignment of subjects to one group or the other is designed to perform this equalization function. One method might be to flip a coin for each woman. If the coin comes up “heads,” the woman would be assigned to one group; if the coin comes up “tails,” she would be assigned to the other group. Although randomization is the preferred scientific method for equalizing groups, there is no guarantee that the groups will, in fact, be equal. As an extreme example, suppose the study sample involves 10 women who have given birth to 4 or more children. Five of the 10 women are aged 35 years or older, and the remaining 5 are younger than age 35. We would expect random assignment to result in two or three women from the two age ranges in each group. But suppose that, by chance, the older five women all ended up in the experimental group. Because these women are nearing the end of their childbearing years, the likelihood of their conceiving is diminished. Thus, follow-up of their subsequent childbearing (the dependent variable) might suggest that the counseling program was effective in reducing subsequent pregnancies; however, a higher birth rate for the control group may reflect only age and fecundity differences, not lack of exposure to counseling.

RELATED;

1.  Phases of clinical trials

REFERENCES

SAMPLE SIZE DETERMINATION FOR CASE CONTROL STUDIES

INTRODUCTION: In a typical case–control study, cases of a specific disease are ascertained as they arise from data collection tools which may include population-based registers or lists of hospital admissions, and controls are sampled either as disease-free people from the population at risk, or as hospitalized patients having a diagnosis other than the one under study. Then in the analysis, we compare the exposure histories of the two groups. In other words, a typical case–control study fits the framework of a two-arm randomized phase III trials. Phases of clinical trials  

It should however be note that, the sample determination is somewhat more complicated, for three reasons: 

(1) Instead of searching for a difference of two means or proportions as in the case of a phase III trial, the alternative hypothesis of a case–control study is postulated in the form of a relative risk.  

(2) It must be decided whether to design a study with equal or unequal sample sizes because in epidemiologic studies, there are typically a small number of cases and a large number of potential controls to select from. Research study designs  

(3) It must be decided whether to design a matched or an unmatched study. For example, we may want to design a case–control study to detect a relative risk, due to a binary exposure, of 2.0, and the size of the control group is twice the number of the cases. 

RELATED;

1.  Sample size calculation  

2.  The case study design

3.  Calculation of sample size-formula 4

REFERENCES