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Chapter #7
Intro to Research Methods - Does the Sample Represent the Population
| Term | Definition |
|---|---|
| Population: | The entire set of people or products in which you are interested |
| Population (Glossary definition): | A larger group from which a sample is drawn; the group to which a study's conclusions are intended to be applied. Also considered population of interest. |
| Sample: | A smaller set taken from that population. |
| Census: | A set of observations that contain all members of the population of interests. |
| Additional Information: | Researchers usually don't need to study every member of the population (they don't need to conduct a census). Instead, they study a sample of people, assuming that if the sample behaves a certain way, the population will do the same. |
| Population of Interest: | World population is around 8 billion. But researchers could never have the entire population in mind when conducting a study! Therefore, to decide whether a sample is biased or unbiased, they have to specify a population they want to generalize. |
| Coming from a Population Versus Generalizing to that Population: | Just because a sample COMES from a population, doesn't mean it generalizes to that population. Just because a sample consists of American Drivers, doesn't mean it represents ALL American Drivers! |
| Samples are Either: | Biased or Unbiased |
| Biased Sample: | Also called an unrepresentative sample, some members of that population of interest have a much higher probability than other members of being included in the sample. |
| Unbiased Sample: | Also called a representative sample, all members of that population have an equal chance of being included in the sample (*ONLY unbiased samples allow us to make inferences about the population of interests*) |
| If the Sample can Generalize the Population: | There is good external validity (findings can be generalized to other populations, settings, and times beyond the specific context to the study) |
| If the Sample is Biased in Some Ways: | The external validity is unknown. |
| Ways a Sample Might be Biased: | Researchers might study only those that contact conveniently or only those who volunteer. These can threaten the external validity of the study due to people who are convenient or more willing have different opinions to those who are less willing. |
| Convenience Sampling: | Using a sample of people who are easy to contact and readily available to participate. |
| Convenience Sampling (EX): | Psychology students are often conducted by psychology professors, and they find it handy to use college students as participants. HOWEVER, college students may not be representative of other populations that are less educated, older, or younger. |
| Self-Selection: | A term used when a sample is known to contain only people who can volunteer to participate. They can cause serious problems to external validity. |
| Self-Selection (EX): | When internet users choose to rate something, Amazon, Twitter, Rate my professor. The people who rate the items are not necessarily representative of the population of all people who bought the product, follow the twitter account, or took the class. |
| Probability Sampling: | BEST OPTION when researchers need an unbiased, representative sample from a population. Also known as random sampling, every member of the population of interest has an equal chance of being selected, regardless of convenient or motivation to volunteer. |
| Nonprobability Sampling: | Techniques involve nonrandom sampling and results in biased sample. |
| Simple Random Sampling (Probability Sampling Techniques): | Considered the most basic form, the people's names that are on the "selected balls" will make up the sample. Another way to create a ____ _______ ______ would be to assign a number to each individual in a population and then select certain ones. |
| Simple Random Sampling (Probability Sampling Techniques) (EX:) | Researchers using a software to generate random numbers. Or, when pollsters need a random sample, they program computers to randomly select telephone numbers or home addresses from a database or eligible people. |
| Systematic Sampling (Probability Sampling Techniques): Difficult and time consuming! | Using a computer or random # table, the researcher starts by selecting two random #'s, say 4 and 7. If the population of interests is a roomful of students, the researcher would start with the 4th person in the room and then count off until desired size. |
| Cluster Sampling (Probability Sampling Technique): | Is an option where people are already divided into arbitrary groups. Clusters of participants within a population of interests are randomly selected, and then all individuals in each selected cluster are used. |
| Cluster Sampling (Probability Sampling Technique) (EX:) | Randomly sample students in NY state, for example, could start with a list of 952 public schools (clusters) in that state, randomly select 100 of those high schools (clusters) and then include every student from each of those 100 schools in the ex. |
| Multistage Sampling (Probability Sampling Technique): | Two random samples are selected. A random sample of clusters and then a random sample of people within those clusters. |
| Multistage Sampling (Probability Sampling Technique) (EX:) | From high school sample, instead of including all students at each school, the researcher would select a random sample of students from each of the selected schools. |
| Cluster and Multistage Sampling INFO: | Both samples are EASIER than sampling from ALL NY high schools and BOTH still produce a representative sample since they both have random selection. |
| Stratified Random Sampling (Probability Sampling Technique): | Another multistage technique, in which the researcher purposefully selects particular demographic categories, or strata, and then randomly selects individuals within each of the categories, proportionate to their assumed membership in population. |
| Oversampling (Probability Sampling Technique): | The researcher intentionally overrepresents one or more groups. |
| Weighting (Additional Notes): | To control for bias, if they determine the final sample contains fewer members of a subgroup, than it should (ex. fewer young adults), they adjust the data so responses from members of underrepresented count more and overrepresented count less. |
| Random Sampling: | Researchers create a sample using some random method, such as drawing names from a hat or using a random digital phone dialer, so that each member of the population has an equal chance of being in the sample. - Increases external validity |
| Random Assignment: | Is used ONLY in experimental designs! When researchers want to place participants into two different groups (ex Treatment group and comparison group), they usually assign them at random. - Increases internal validity = was caused by independent variable |
| Settling for an Unrepresentative Sample (Nonprobability Sampling Technique): | In cases where external validity is NOT VITAL to a study's goal, researchers might be content with a nonprobability sample technique depending on the TYPE of study. Convivence sampling is most common, but there's other techniques. |
| Purposive Sampling (Nonprobability Sampling Technique): | If researchers want to study only certain kinds of people, they recruit only those particular participants. When this is done in a random way, it is called ______ _______. |
| Purposive Sampling (Nonprobability Sampling Technique) (EX): | A Study to see the effectiveness of a specific intervention to quit smoking. If researchers recruit the sample of smokers by posting flyers at a local tobacco store, that action makes it a _ b/c only smokers will participate and aren't randomly selected. |
| Snowball Sampling (Nonprobability Sampling Technique): | One variation on purposive sampling that can help researchers find rare individuals, which participants are asked to recommend a few acquaintances for the study. |
| Snowball Sampling (Nonprobability Sampling Technique) (EX): | Asking people in a study for Crohn's disease that HAVE the disease to recruit people from their support groups. |
| Quota Sampling: | Similar to stratified random sampling, researchers identify subsets of the population of interest and then sets a target number for each category in the sample. NEXT, researcher samples from population of interest nonrandomly until quotas are filled. |
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