PSYC300 Exam 2 Study Guide Chapter 6 – Sampling and Surveys • Descriptive research – research designed to answer questions about the current state of affairs. • Survey – a series of self-report measures administered either through an interview or a written questionnaires that collect descriptive information about a group of people’s attitudes, opinions, habits, etc. - Can collect a wide variety of information in a short period of time. • Interviews – questions are read to the respondent in person or over the phone. - An advantage of in person interviews is that a close relationship between the researcher and respondent can be established. This can lead to more open and honest responding. - An advantage of phone interviews is that they are cheaper, less time consuming, and efficient. • Unstructured interviews – the interviewer talks freely with the person being interviewed about many topics. - Focus group – a face-to-face unstructured interview in which a number of people are interviewed at the same time and share ideas both with the interviewer and with each other. Provide information about a group of people. • Structured interviews – uses quantitative fixed-format items. This allows better comparison of the responses across different individuals because all factors are controlled for each respondent. • Questionnaires – are sets of fixed-format, self-report items that are usually completed without supervision. - Receive more honest answers. - Less influenced by the characteristics of the experimenter and social desirability. - The way you word or order the questions is going to yield different results. - Response rate – the percentage of people who actually complete the questionnaire and return it to the investigator. • The General Social Survey – a collection of over 1,000 items given to sample U.S. citizens. Can be compared over time. • Population – the entire group of interest. • Sample – the people who actually participate in research that are used to represent the population of interest. • Census – measuring each individual from the population of interest. • Sampling – selection of people to participate in a research project. - Use the results to make inferences about the entire population. • Sampling frame – complete list of all the people in the population.• Representative sample – the sample has the same important characteristics of the entire population. - Increasing the size of a sample makes it more likely that the sample will be representative of the population. • Probability sampling – each person in the population has a known chance to be a part of the research sample. This increases the likelihood that the sample is representative and increases the ability to use the sample to draw inferences about the population. - Simple random sampling – each person in the population has an equal chance of being selected to be in the sample. The researcher randomly selects from the frame a sample of a given number of people. - Systematic random sampling – when the names on a sampling frame are in a random sequence and you use a random number to pick out people for a sample. Ex: If you want to sample 1 out of 50 students from a population of 5,000, you pick a random number between 1 and 50 and sample the person on that list with that number. You create the rest of the list by taking every 50th person on the list after the initial person. - Oversampling – when the researcher makes a sample that has a large proportion of some strata than what is actually represented in the population. Used to provide a large enough sample of the strata of interest. - Stratified sampling – randomly sampling from different subgroups (sex, race, age, religion). - Cluster sampling – breaking the population down into a set of smaller groups (clusters) so there are sampling frames and then randomly choose some of the clusters for inclusion in the sample. Ex: Divide the US into regions, and then select states from each region, counties from each state, and universities from each county. We could then draw a random sample from this list. • Sampling bias – the sample is not representative because the probability with which the individuals were sampled is not known. The sampling frame of interest might not be accurate because members of the population are missing. Sometimes there is no sampling frame. • Nonprobability Samples - Snowball sampling – one or more individuals from the population are contacted and these individuals are used to lead the researcher to other population members. This can be very high in bias. Ex: locating homeless people. - Convenience samples – when the researcher samples individuals who are readily available without any attempt to make the sample representative of a population. Ex: sampling collegestudents in your class. They can only be used to test hypotheses, not to draw inferences about a population. • Frequency distribution – number/percentage of individuals that fall into the categories of interest. - Graphs nominal variables. - Displayed on a bar graph (bars don’t touch). • Grouped frequency distribution – combining adjacent values into a set of categories to examine the frequencies of each of the categories. - Displayed on a histogram (bars touch) or frequency curve (frequencies indicate with lines instead of bars). - A disadvantage is that grouping values together into categories results in the loss of some information. • Stem and leaf plot – graphically summarizing data so the original data values can still be seen. • Descriptive statistics – numbers that summarize the pattern of scores observed on a measured variable. - Distribution – the pattern of scores observed on a measured variable. - Central tendency – the point in the distribution around which the data are centered. Includes the mean, median, and mode. - Dispersion – the spread of the data. • Measures of Central Tendency - Mean – the average of all the scores, most influenced by outliers. - Median – the score at the center of the distribution. - Mode – the value that occurs most frequently in the distribution. - Normal distribution – when the data is mostly centered near the center of the distribution and the distribution is symmetrical and bell-shaped. - Skewed distribution – distributions that are not symmetrical