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social science
positive psychology

Questions and Answers of Positive Psychology

11. One-way ANOVA tests for significant differences in the continuous level depen- dent variable based on categorical differ- ences of one independent variable.
10. Design issues to consider in using ANOVA include planned or post hoc comparisons, and between-subjects and within-subject forms.
8. A t-test can be two-tailed, in which any difference found is accepted, or one-tailed, in which the direction of the difference is specified by the research question or hypothesis. 9. Analysis of
7. The t-test is used to test hypotheses that expect to find a difference between two groupings of the independent variable on a continuous level dependent variable.
6. A one-way chi-square looks for statistically significant differences in categories within one nominal variable; contingency analysis looks for categorical differences between two or more nominal
5. Four analytical steps assist the researcher through statistical interpretation of tests of differences: (1) conducting the statisti- cal test to determine if differences exist; (2) characterizing
4. Meeting these assumptions may not always be possible; thus, some scholars use these tests of differences outside the experimental design framework.
3. Inferential statistics rely on several assump- tions: the use of probability in establishing significance levels, normal distribution of populations and samples, and random assignment of
2. The function of inferential statistics is to draw conclusions about a population by examining the sample.
1. Chi-square, t-test, and ANOVA are statisti- cal tests of difference.
5. Interpret research findings developed from results of chi-squares, t-tests, and ANOVAS.
4. Differentiate among the assumptions and functions of chi-squares, t-tests, and ANOVAS.
3. Develop a hypothesis or research question and select the appropriate statistical test of difference (chi-square, t-test, ANOVA).
2. Use the four analytical steps to design and evaluate research designs and statistical findings.
17. Type I and Type II errors occur when researchers accept or reject results as valid when the opposite is true.
16. Hypothesis testing is an act of decision making-accepting the alternative hypoth- esis or retaining the null hypothesis.
15. By convention, researchers are interested in the alternative hypothesis but statistically test the null hypothesis.
14. Hypothesis testing is based on probability sampling techniques and the stated level of significance.
13. Significance levels are set for each statisti- cal test used in a research project; generally, the probability level of .05 is accepted as the standard in communication research.
12. Researchers are responsible for the results and their interpretations, even if an expert helps them in this aspect of the research process.
11. Researchers also use frequencies and per- centages to describe their data.
10. Measures of dispersion-range and stan- dard deviation provide a description of the variability of the data.
9. Measures of central tendency-mean, median, or mode-reflect different types of average or typical data.
8. The number of cases is the number of data points.
7. Descriptive statistics-number of cases, central tendency, and dispersion-are sum- mary information about the dataset for one variable.
6. Frequency distributions and polygons are the first step in analyzing a set of scores for one variable.
5. In positively or negatively skewed distribu- tions, the curve is asymmetrical.
4. In normal distributions, one side mirrors the other; the curve is symmetrical.
3. The normal curve is a theoretical distribu- tion in which the majority of cases peak in the middle of the distribution, with progres- sively fewer cases as one moves away from the middle of the
2. From the raw data collected, researchers compute the descriptive statistics that con- vey essential summary data of the dataset as a whole.
1. Numbers are one of many tools researchers use to collect data.
13. Identify when an alternative hypothesis is accepted and when a null hypothesis is retained.
12. Explain the relationship among sampling techniques, significance levels, and hypoth- esis testing.
11. Make a decision about a hypothesis based on the stated level of significance.
10. Choose an appropriate level of significance for each statistical test used in your research project.
9. Accurately report descriptive statistics.
8. Accurately calculate descriptive statistics.
7. Use frequencies and percentages to provide a summary description of nominal data.
6. Explain the relationship between the mean and standard deviation for scores on a variable.
5. Compute the range and standard deviation for each variable in a dataset.
4. Compute and interpret the mean, median, and mode for each variable in a dataset.
3. Create a frequency distribution and polygon for each variable in a dataset.
2. Assess data for its distribution and compare it to the normal curve.
1. Explain the concept of the normal curve.
14. Survey data are collected at one point in time, which weakens their predictive ability unless theoretical models have been devel- oped before the survey data are collected.
13. After data is collected, the researcher must analyze and interpret the data as a whole, rather than focusing on the responses of any individual.
12. An aspect of reliability central to question- naires is internal reliability, or the degree to which multiple questions or items consis- tently measure the same construct.
11. Response rate, or the number of people who respond after they have been contacted to participate, should not be confused with sample size.
10. Before using the survey in a research proj- ect, it should be pilot tested, or pretested.
9. How the survey looks can affect if and how respondents will answer; it should be uncluttered and readable and respondents should be told explicitly how and where to mark their responses.
8. Many closed questions can be adequately responded to using a 5-point or 7-point Likert-type response scale, and must be exhaustive as well as mutually exclusive.
7. Closed questions are complete with standard- ized response sets; respondents choose from the responses provided by the researcher.
6. Open questions allow the respondent to use his or her own words in responding to a question or statement.
5. Recall cues, or stimulus statements, are needed to direct or restrict participants' responses.
4. Existing and established questionnaires can be used in some instances; otherwise, the researcher has to develop the questionnaire.
3. Research questions or hypotheses drive the survey or questionnaire design.
2. Often self-administered, surveys can be distributed in written format through the mail, web, or e-mail, or interviewers can ask questions face-to-face or over the phone.
1. Surveys and questionnaires are the most common quantitative method used in communication research.
10. Present the data to others in an appropriate fashion.
9. Draw conclusions that do not overstate the limitations of your data or sample.
8. Analyze the data completely and appropriately.
7. Collect the data in an honest and ethical manner.
6. Pretest the method of data collection.
5. Design an uncluttered and easy to read survey.
4. Use open and closed questions appropriately.
3. Select existing or design appropriate questionnaire items and response sets.
2. Select the survey format (face-to-face, telephone, self-report, online) that will best serve the purpose of the survey.
1. Design a survey or questionnaire to answer a research question or test a hypothesis.
13. All research designs can suffer from bias from researcher effects or procedural bias.
12. Communication researchers often use descriptive designs when communica- tion phenomena do not lend themselves to experimental or quasi-experimental designs.
11. Descriptive designs are those studies that do not use random assignment of partici- pants or researcher manipulation of the independent variable; as a result of lacking these controls, these
10. Field experiments are a form of quasi- experimental research design conducted in a naturalistic setting.
9. In quasi-experiments, the researcher uses the natural variation that exists on the inde- pendent variable to assign participants to treatment and control conditions.
8. The time between the multiple measure- ments of the dependent variable in a longitudinal design is based on the commu- nication phenomena under study and the theoretical foundation of the study.
7. In the factorial design, treatment groups are based on two or more independent variables, with random assignment occur- ring on one of the variables.
6. In the pretest-posttest design, only individ- uals in the treatment group are exposed to the stimulus; the dependent variable is mea- sured for all participants prior to and after the treatment
5. In the posttest only design, the dependent variable is measured only once-after par- ticipants are exposed to the stimulus.
4. Manipulation checks should be conducted to ensure that participants perceived variation in the independent variable as the researcher intended.
3. In an experiment, the researcher controls the manipulation of the independent variable by randomly assigning participants to treat- ment or control groups; this ensures that the treatment and
2. Experimental research is used to estab- lish cause-effect relationships between or among variables, and is most often con- ducted in a laboratory.
1. There are three categories of quantitative research design: experimental forms, quasi- experimental forms, and descriptive forms.
9. Develop a research protocol to limit researcher effects and procedural bias when conducting research studies.
8. Appropriately interpret findings from descriptive research designs.
7. Interpret findings from experimental and quasi-experimental designs with respect to cause–effect relationships.
6. Conduct manipulation checks of independent variables.
5. Manipulate independent variables according to their theoretical foundation.
4. Facilitate appropriate random assignment of participants to treatment and control groups.
3. Explain the benefits of experimental forms over quasi-experimental and descriptive forms.
2. Understand the strengths and limitations of each design form as it relates to research findings, and argue for your design choices.
1. Select and develop the appropriate research design for your hypotheses or research questions.
12. Sample size is estimated from the size of the population and the level of error a researcher is willing to tolerate.
11. Types of nonprobability sampling include convenience, volunteer, inclusion and exclu- sion, snowball, networking, purposive, and quota samples.
10. Nonprobability sampling weakens the representativeness of a sample to the popu- lation because it does not rely on random sampling; however, it is used when no other sampling technique will
9. Cluster sampling is used when all members of elements of a population cannot be identi- fied and occurs in two stages: (1) the popula- tion is identified by its groups, and (2) then random
8. A stratified random sample first groups members according to categories of interest before random techniques are used.
7. In systematic sampling, every nth, for exam- ple every 14th, element is selected for the sample.
6. In a simple random sample, every person or element has an equal chance of being selected for a study.
5. Probability sampling ensures that the selected sample is sufficiently representa- tive of the population because every person or element has an equal chance of being selected.
4. Sampling error is the degree to which a sample differs from population characteristics.
3. Generalizability is the extent to which conclusions drawn from a sample can be extended to a population.

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