Why is it important to use a representative sample in research?

Which of the following best describes a representative sample?

What is a key characteristic of a representative sample in research?

Which of the following is a benefit of using a representative sample in research?

The primary purpose of using a representative sample is to enable the generalization of research findings to the broader population. By accurately reflecting the composition of the population, a representative sample helps ensure that the results are not skewed by sample bias.

Generalization and Bias with Representative Samples

An example of a non-representative sample would be measuring the height of women in a single classroom that happens to include the women's basketball team. Because basketball players are typically taller than average, this sample would be biased and would not accurately reflect the height distribution of all American women. To create a more representative sample and a natural bell curve, one would need to measure the heights of a much larger and more diverse group, such as all women at a school.

Example of a Non-Representative Sample

If researchers cannot test all algebra students in a city, they might select a group of about 200 students that accurately reflects the city's overall composition. By using this representative sample, they can confidently generalize their findings to the entire population of algebra students without introducing bias.

Example of a Representative Sample

The standard deviation is the most widely used measure of variability, representing the average distance that individual scores within a distribution deviate from the mean.

Standard Deviation

What is the primary characteristic of a representative sample?

A representative sample is a study sample that is similar to the broader population in important respects. It is crucial for researchers to obtain a representative sample so they can appropriately generalize their findings from the small subset of participants to the entire population of interest.

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Norming involves establishing reference scores, or norms, for an assessment by administering the test to a large, representative population. This process collects data on performance across different groups, such as age brackets, which serve as a baseline for interpreting future scores and demonstrating what a specific group actually knows rather than a prescribed expectation.

Norming

A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. This method is often preferred for selecting experimental participants because, provided the sample is large enough, it provides reasonable assurance that the participating individuals are representative of the broader population.

Random Sample

A sample is a smaller subset of individuals selected from a larger population of interest. Because it is usually impossible to study every member of a population, researchers measure variables within a sample with the intention of generalizing their findings back to the broader group, ideally using a representative sample that closely resembles the population.

Sample

Simple random sampling is a method for obtaining a study sample where every single member of the target population has an equal chance of being selected. While this approach is effective for ensuring a representative sample, it is often difficult or impossible to achieve in psychological research because populations are rarely defined clearly enough to give everyone an equal selection chance.

Simple Random Sampling

Spielman, R. M., Jenkins, W. J., & Lovett, M. D. (2020, April 22). Psychology (2nd ed.). OpenStax. https://openstax.org/books/psychology-2e/pages/1-introduction

OpenStax Psychology (2nd ed.) Textbook

https://kpu.pressbooks.pub/psychmethods4e/

KPU Research Methods in Psychology - 4th American Edition

What does the process of norming in assessments involve?

Why is norming an important step in the development of an assessment?

Which of the following best describes the purpose of norming an assessment?

What is a key outcome of the norming process in assessment development?

The tools used to measure intelligence, particularly IQ tests, have been a subject of ongoing debate and controversy throughout the history of their development and application.

Intelligence Measurements Controversies

The Stanford-Binet Intelligence Scale is an influential intelligence assessment created by Stanford professor Louis Terman, who modified Alfred Binet's original work for use in the United States. Terman standardized the test's administration and normed it by testing thousands of children across various ages to establish an average score for each age group, ensuring reliable comparisons.

Stanford-Binet Intelligence Scale

To maintain their accuracy and relevance, widely used intelligence tests like the Wechsler scales undergo periodic recalibration. This process involves re-norming and re-standardizing the test by administering it to large, contemporary populations. For example, the WISC-V was given to thousands of children across the U.S. so that current test-takers' scores can be compared against an up-to-date, same-age peer group.

Recalibration of Intelligence Tests

Representative Sample

A research team wants to understand the opinions of all 30,000 residents of a town regarding a new public park. They decide to survey a smaller group of 300 residents. Which of the following methods represents the correct procedure for selecting a random sample?

In research, random samples are preferred because, assuming the sample size is sufficiently large, researchers can be reasonably sure that the participating individuals form a representative sample. This ensures the percentages of key characteristics in the sample match the larger population, making it possible to generalize the findings.

Purpose of Large Random Samples

To select a random sample for an experiment involving algebra students, researchers must first narrow their population of interest from all algebra students globally to a more manageable group, such as all algebra students in a specific city. From this localized population, they can work with local schools to randomly select a smaller subset, like 200 students, ensuring the sample represents diverse backgrounds, including various income brackets, family situations, ethnicities, and geographic areas.

Example of Selecting a Random Sample

While both concepts rely on random processes, they serve distinct roles in research. Random sampling is a technique used to draw a representative sample from a larger population, though it is rarely utilized in psychological studies. Conversely, random assignment takes place after a sample is formed; it is the vital procedure used to place those selected participants into the various conditions of an experiment to ensure the groups are comparable.

Random Assignment vs. Random Sampling

Survey research is a flexible methodology that incorporates both quantitative and qualitative approaches, defined by two primary characteristics. First, it relies on self-report measures, where researchers ask participants to directly report on their thoughts, feelings, and behaviors using tools like questionnaires or interviews. Second, it emphasizes rigorous sampling techniques, typically favoring large random samples to acquire accurate estimates of population characteristics. This makes survey research one of the few approaches in psychology that routinely relies on random sampling.

Characteristics of Survey Research

Which of the following statements best describes a random sample?

In research, the sample is studied with the goal of generalizing the results to the broader population from which the sample was drawn. This process allows researchers to make inferences about a large group based on data collected from a smaller, representative subset. The relationship is visually comparable to extracting a small representative group from a vast crowd.

Relationship Between Sample and Population

A team of researchers wants to understand the typical sleep habits of all adults in a country of 50 million people. Since they cannot survey every individual, which of the following approaches would be the most practical and scientifically sound first step for their investigation?

To illustrate how a sample is used, a researcher might measure the talkativeness of a few hundred university students. This subset serves as the sample, which the researcher studies with the intention of drawing conclusions about the talkativeness of a much larger population, such as men and women in general.

Example of a Sample: Talkativeness Study

Inferential statistics are a set of mathematical techniques used by researchers to determine whether a statistical result or relationship observed in a study's sample reflects something that is genuinely true of the broader population.

Inferential Statistics

Probability sampling is a broad category of sampling methods where the researcher can mathematically specify the exact probability that each member of the defined population will be selected for the sample. Because it enables researchers to make highly accurate estimates about a population, it is the favored approach in survey research, though it requires a clear and precise specification of the target population before sampling begins.

Probability Sampling

Non-probability sampling encompasses methods where the researcher cannot mathematically specify the probability that any given member of the target population will be selected for the sample. Because it does not rely on structured random selection from a clearly defined population, it is the most common approach used in general psychological research, even though it limits the ability to make perfectly representative population estimates.

Non-probability Sampling

The optimal sample size for a survey generally depends on two primary factors: the researcher's desired level of statistical confidence and the practical constraints of the study's budget. While larger samples yield statistics that are closer to the true population value and provide greater confidence, they also require more time, effort, and money to collect. Researchers frequently use power analyses to balance these theoretical and practical considerations.

Determinants of Survey Sample Size

Convenience sampling is a common form of non-probability sampling where researchers study individuals who happen to be physically nearby and are readily willing to participate, such as college students in an introductory psychology course. While this approach is frequently used in psychological research due to its accessibility and efficiency, its main disadvantage is that the resulting sample may not accurately represent the broader population, severely limiting the ability to generalize the findings.

Convenience Sampling

A survey non-responder is an individual selected for a survey sample who fails to complete the questionnaire. This lack of participation can occur for various reasons, including moving away, being too busy, lacking interest in the topic, or refusing to participate on principle.

Survey Non-responder

Sampling bias is a systematic error that occurs when the method used to select participants results in a sample that fails to accurately represent the broader target population. Because the selected individuals differ in important ways from the overall group, any statistical findings derived from this unrepresentative sample will likely be inaccurate and cannot be safely generalized.

Sampling Bias

Measures of central tendency are descriptive statistics used to describe the typical, average, or center scores within a distribution. Common examples of these measures include the mean, median, and mode.

Measures of Central Tendency

To conduct a study, researchers typically select a smaller subset of individuals from a broader group of interest. This smaller subset is referred to as a ____.

A classic application of simple random sampling can be seen when a pollster starts with a complete list of all registered voters in a city, which represents the entire population. The pollster then randomly selects $$100$$ individuals from that list to form the sample and asks them whom they intend to vote for, ensuring each voter had an equal chance of being chosen.

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