Chapter 5

Describe the relationship between a sample and the population (both target and accessible in a research study and explain the importance of obtaining representative, as opposed to biased, samples.

*Population* - The entire set of individuals of interest to a researcher. *Target Population* - A group defined by the researcher's specific interests / Typically share one characteristic (e.g., children of divorced parents) clearly way too difficult to access all children of divorced parents. *Accessible Population* - e.g., children at two local schools with divorced parents *Sample* - A set of individuals selected from a population and is usually intended to represent the population in a research study.

Simple Random Sampling

*Sampling with Replacement*: The individual selected for the sample is recorded and then replaced before the next selected is made *Sampling without Replacement*: This method removes each selected individual from the population before the next selection is made. - Although Simple Random Sampling does provide a truly random sample, if the population is small may be biased (Flipping a coin ten times may result with the same side 10 times)

Sampling: Representativeness, Representative Sample, Biased Sample, Selection Bias (Sampling Bias)

*Sampling*: The process of selecting individuals for a study *Representativeness*: Refers to the extent to which the characteristics of the sample accurately reflect the characteristics of the population. *Representative Sample*: A sample with the same characteristics as the population. *Biased Sample*: A sample with different characteristics from those of the population. *Selection Bias/Sampling Bias*: Occurs when participants or subjects are selected in a manner that increases the probability of obtaining a biased sample.

Describe the process of: Simple Random Sampling, recognize this technique when it appears in a research report, and explain its strengths and weaknesses

*Simple Random Sampling*: The starting point for most probability sampling techniques. *Equal* - Each individual has an equal chance of being selected. No individual is more likely of being chosen than the other *Independent* - The choice of one individual does not influence the probability of choosing another individual. 1. Define the population of interest from which you want to select your sample 2. List all of the members of the population n =x 3. Assign numbers to each member of the population 4.Select the sample size you want, using some random process (Use a Table of Random Numbers-check end of text)

Describe the four probability sampling methods presented in the book, other than simple random sampling, recognize these techniques when they appear in research reports and explain the strengths and weaknesses of each

*Systematic Sampling* - Type of probability sampling that is very similar to Simple Random Sampling. - List all individuals in the population - Randomly pick a starting point - Move down the list every 9th name - n is determined by dividing population size by desired sample size - Less random than Simple Random Sampling because the principle of independence - However, as with Simple Random Sampling this option does ensure a high degree of representativeness

Stratified Random Sampling

A form of probability sampling; a random sampling technique in which the researcher identifies particular demographic categories of interest and then randomly selects individuals within each category. - Ensures different subgroups are accurately represented - 1st identify the specific subgroups - Then select equal-sized random samples from each of the pre-identified strata to be included in the sample - Pros: Ensures different subgroups will be represented - Cons: Can lead to overrepresentation of minority groups + Every individual does not have an equal chance of being selected (Group 1 may have a 30% chance while group 2 has a 10% chance)

Cluster Sampling

A probability sampling technique in which clusters of participants within the population of interest are selected at random, followed by data collection from all individuals in each cluster. - All previous probability sampling methods focused on selecting individual participants. Occasionally, the individuals in the population are already clustered in pre existing groups, and a researchers can randomly select groups instead of individuals. -Compared to simple random sampling and stratified sampling , cluster sampling has advantages and disadvantages. For example, given equal sample sizes, cluster sampling usually provides less precision than either simple random sampling or stratified sampling. On the other hand, if travel costs between clusters are high, cluster sampling may be more cost-effective than the other methods.

Describe quota sampling, recognize examples of this technique in research reports, and explain why it is used

Can ensure sub groups are equally represented in a convenience sample. Example ensure 15 male and 15 female participants in a psych study rather than 30 female. - Can adjust quotas (just like proportionate stratified sampling) to represent sub populations - Note that quota sampling is not like stratified and proportionate stratified sampling because it does not randomly select individuals from the population. Individuals are selected on the basis of convenience.

Explain the basic distinction between probability sampling methods and nonprobability sampling methods and recognize examples of these two sampling techniques when they appear in research

Sampling methods fall into two basic categories: Probability and Nonprobability sampling. *Probability* - Entire population is known, each individual has a specifiable probability of selection, and sampling occurs by a *random process* based on the probabilities *Nonprobability Sampling* - The population is not completely known, individual

Proportionate Random Sampling

Selection of population units for study according to strata, based on the size of each stratum in the population - Identify a set of subgroups -Determine what proportion of the population corresponds to each subgroup. - The sample is obtained such that the porportions in the sample exactly match the proportions in the overall population. - Common for political polls - Very time consuming

Describe the process of convenience sampling, recognize examples of this technique in research reports, and explain why it is used and how researchers using this method can limit the risk of a biased sample

The most commonly used sampling method in behavioural science research is probably convenience sampling. - Researchers simply use participants who are easy to get (e.g., Intro Psych / Local daycare) - Considered a weak form of sampling: Does not require knowledge of the population, and does not use a random process for selection. - Very little control over the representativeness of the sample, possibly biased. How to Limit Risk: Select a diverse crowd from into psych for example (different ages, genders, ethnicities); and simply provide a clear description of how the sample was obtained and allow the reader to determine.

Combined Strategy Sampling

The use a mix of sampling strategies, if appropriate, to try to get a good representation of the population; - For example, a superintendent of schools may first divide the district into regions (North East South West) - Stratified Sampling - From the different regions, may select two third grade classrooms (Cluster Sampling)

Determining Sample Size The Law of Large Numbers

There is no simple answer to how many participants should be included in a sample. Although there are some general guidelines. *Law of Large Numbers*: The larger the sample size, the more likely the values obtained from the sample are similar to the entire population. - Another helpful guide is to review similar research studies to see how many participants they used.