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Chapter 4: Problem 93

Quota sampling An interviewer stands at a street corner and conducts interviews until obtaining a quota in various groups representing the relative sizes of the groups in the population. For instance, the quota might be 50 factory workers, 100 housewives, 60 elderly people, 30 Hispanics, and so forth. This is called quota sampling. Is this a random sampling method? Explain, and discuss potential advantages or disadvantages of this method. (The Gallup organization used quota sampling until it predicted, incorrectly, that Dewey would easily defeat Truman in the 1948 presidential election.)

Short Answer

Expert verified
Quota sampling is not a random method; it risks bias and lacks true population representation, as seen in historical errors like the 1948 election prediction.

Step by step solution

01

Understanding Quota Sampling

Quota sampling involves dividing a population into distinct subgroups based on specific characteristics, such as occupation, age, ethnicity, etc., and sampling individuals from these groups until a predetermined quota is filled. It aims to ensure that the sample represents certain characteristics of the population.
02

Analyzing Randomness

Random sampling requires every member of the population an equal chance of being selected. In quota sampling, the interviewer decides whom to interview within each quota, which is not random because it's based on subjective choice at the interviewer's discretion.
03

Advantages of Quota Sampling

Quota sampling allows for control over subgroup representation in the sample, ensuring that specific characteristics of the population are mirrored in the sample distribution, which can be faster and less costly than random sampling.
04

Disadvantages of Non-Random Sampling

The lack of randomness in quota sampling can introduce bias since interviewers might consciously or unconsciously select respondents who are easily accessible or more willing to participate. This can result in findings that are not truly representative of the population.
05

Historical Context

An example of the potential pitfalls of quota sampling was seen in the 1948 U.S. presidential election prediction by Gallup, which inaccurately indicated that Dewey would defeat Truman. The non-random nature of quota sampling contributed to the prediction error.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Random Sampling
In statistical studies, random sampling is a crucial method used to ensure that every individual in a population has an equal chance of being selected in a sample. This technique is fundamental for achieving results that are free from selection bias and ensures a sample that accurately mirrors the population it represents.

Key elements of random sampling are:
  • Equal probability: Each member of the population has an equal opportunity to be chosen.
  • Impartial selection: Selection process is devoid of personal judgment or biases, often using random number generators or similar methods to select participants.
  • Reliability: Random samples tend to produce reliable and valid results that can be generalized to the whole population.
Random sampling is particularly efficient when dealing with large populations and can be a bit resource-intensive. However, its ability to provide unbiased data makes it a gold standard in research methodologies.
Bias in Sampling
Bias in sampling occurs when certain members of a population are more likely to be included than others, leading to skewed results. This can significantly impact the outcomes of research, as the sample may not accurately reflect the larger population.

Misinclusive sampling, like quota sampling, can lead to bias because:
  • Selection bias: Interviewers choose respondents based on convenience or willingness rather than randomness.
  • Non-representative sample: Certain attitudes or traits might be over or under-represented, causing misinterpretation of data.
  • Influence of external factors: Time of day, location, or even the interviewer's approach might unduly influence who participates.
Minimizing bias is critical to obtaining data that genuinely reflects population characteristics. Enhanced awareness and methodological rigor can help prevent such biases.
Population Representation
Population representation refers to the degree to which a sample mirrors and accurately reflects the traits of the larger population. It鈥檚 key to ensuring the validity and generalization of any research findings.

Quota sampling attempts to achieve population representation by forcing the sample to reflect specific population segments. However, its effectiveness is often debated due to the method's inherent biases.
  • Proportional representation: Samples should maintain the population's proportions regarding age, gender, ethnicity, or other critical traits.
  • Diverse inclusion: A truly representative sample includes diverse population aspects, minimizing narrow perspectives.
  • Validity: Higher representation increases the likelihood that research findings are valid and applicable to the population.
Carefully constructed sampling methodologies are necessary to ensure that all relevant population segments are adequately represented.
Sampling Methods
Sampling methods are strategies used to select a subset of individuals from a population to make inference about the whole. Various methods provide different strengths and weaknesses.

Some common sampling methods include:
  • Simple Random Sampling: Every member has an equal chance of selection, minimizing bias.
  • Systematic Sampling: Selects every nth member from a list, offering simplicity and regularity.
  • Stratified Sampling: Divides the population into subgroups and selects samples from each, ensuring representation of all segments.
  • Quota Sampling: Non-randomly selects individuals to meet predetermined quotas, aiming for proportional representation.
Each method has its applicable contexts, with the choice often depending on the research goals, population size, and available resources. Understanding the strengths and limitations of each gives researchers the tools to select the most appropriate method for their study.

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Most popular questions from this chapter

Distinguish helping and hindering among infants Exercise 1.10 discussed a study at Yale University's Infant Cognition Center. Researchers were interested in determining whether infants had the ability to distinguish between the actions of helping and hindering. Each infant in the study was shown two videos. One video included a figure performing a helping action and the other included a figure performing a hindering action. Infants were presented with two toys resembling the helping and hindering figures from the videos and allowed to choose one of the toys to play with. The researchers conjectured that, even at a very young age, the infants would tend to choose the helpful object. a. How might the results of this study been biased had each infant been shown the video with the helpful figure before being shown the video with the hindering figure? b. How could such a potential bias be eliminated?

Comparing female and male students You plan to sample from the 3500 undergraduate students who are enrolled at the University of Rochester to compare the proportions of female and male students who would like to see the United States have a female president. a. Describe the steps for how you would proceed, if you plan a simple random sample of 80 students. Illustrate, by picking the first three students for the sample. b. Suppose that you use random numbers to select students, but stop selecting females as soon as you have \(40,\) and you stop selecting males as soon as you have \(40 .\) Is the resulting sample a simple random sample? Why or why not? c. What type of sample is the sample in part b? What advantage might it have over a simple random sample?

Reducing high blood pressure A pharmaceutical company has developed a new drug for treating high blood pressure. They would like to compare its effects to those of the most popular drug currently on the market. Two hundred volunteers with a history of high blood pressure and who are currently not on medication are recruited to participate in a study. a. Explain how the researchers could conduct a randomized experiment. Indicate the experimental units, the response and explanatory variables, and the treatments. b. Explain what would have to be done to make this study double-blind.

Is a vaccine effective? A vaccine is claimed to be effective in preventing a rare disease that occurs in about one of every 100,000 people. Explain why a randomized clinical trial comparing 100 people who get the vaccine to 100 people who do not get it is unlikely to be worth doing. Explain how you could use a case-control study to investigate the efficacy of the vaccine.

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