What is one example of why researchers must take into consideration the benefits of their research? A study should only be conducted if the study’s benefits outweigh the risks.

Jasmeet did not announce what her dependent variable was, she did not say what would be assessed from the different amounts of feed she would give the fish. For this reason, we can conclude that the dependent variable is the aspect of the Jasmeet experiment that failed.

Scientific Psychologists follow a specific guidelines for their decisions in doing research because it helps them ensure that their research participants are protected from potential harm. These guidelines are known as a code of ethics, indicating how human participants must be treated in pyschological research.

The margin of error can be calculated in two ways, depending on whether you have parameters from a population or statistics from a sample: Margin of error = Critical value x Standard deviation for the population. Margin of error = Critical value x Standard error of the sample.

The purpose of random selection is to generate a sample that represents a larger population. By the way, in experimental research, random assignment is much more important than random selection; that’s because the purpose of an experiment to establish cause and effect relationships.

Answer: As sample size increases, the margin of error decreases. As the variability in the population increases, the margin of error increases.

If it is an election poll or census, then margin of error would be expected to be very low; but for most social science studies, margin of error of 3-5 %, sometimes even 10% is fine if you want to deduce trends or infer results in an exploratory manner.

As the sample size gets larger (from black to blue), the Type I error (from the red shade to the pink shade) gets smaller. For one-tail hypothesis testing, when Type I error decreases, the confidence level (1-α) increases. Thus, the sample size and confidence level are also positively correlated with each other.

Sample Size and Variability A smaller sample size or a higher variability will result in a wider confidence interval with a larger margin of error. If you want a higher level of confidence, that interval will not be as tight. A tight interval at 95% or higher confidence is ideal.

A good maximum sample size is usually 10% as long as it does not exceed 1000. A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000. For example, in a population of 5000, 10% would be 500. In a population of 200,000, 10% would be 20,000.

The Cochran formula allows you to calculate an ideal sample size given a desired level of precision, desired confidence level, and the estimated proportion of the attribute present in the population. p is the (estimated) proportion of the population which has the attribute in question, q is 1 – p.

Slovins’s formula is used to calculate an appropriate sample size from a population. Statistics is a way of looking at a population’s behavior by taking a sample. It’s usually impossible to survey every member of a population because of money or time.

In a number of research studies involving surveys, the so-called Slovin’s formula is used to determine the sample size. Unfortunately, many of these studies use the formula inappropriately, giving the wrong impression that it can be used in just about any sampling problem.

Slovin’s formula allows a researcher to sample the population with a desired degree of accuracy. Slovin’s formula gives the researcher an idea of how large the sample size needs to be to ensure a reasonable accuracy of results.

To know how many people you should send your survey to, you want to take your sample size (how many responses you need back) divided by the response rate. For example, if you have a sample of 1,000 and an estimated response rate of 10%, you would divide 1000 by . 10. Your survey group should be around 10,000.

Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.

Which of the following is NOT a type of non-probability sampling? Quota sampling. Convenience sampling.

Probability sampling: Each member of the population has an equal probability of being sampled. This is required when you want to make precise statements about a specific population on the basis of your survey. Non-probability sampling: Unequal probability of being sampled. Quite common and can be very useful.

One major disadvantage of non-probability sampling is that it’s impossible to know how well you are representing the population. Plus, you can’t calculate confidence intervals and margins of error. This is the major reason why, if at all possible, you should consider probability sampling methods first.

Methods of sampling from a population

• Simple random sampling. In this case each individual is chosen entirely by chance and each member of the population has an equal chance, or probability, of being selected.
• Systematic sampling.
• Stratified sampling.
• Clustered sampling.
• Convenience sampling.
• Quota sampling.
• Judgement (or Purposive) Sampling.
• Snowball sampling.