New Semester
Started
Get
50% OFF
Study Help!
--h --m --s
Claim Now
Question Answers
Textbooks
Find textbooks, questions and answers
Oops, something went wrong!
Change your search query and then try again
S
Books
FREE
Study Help
Expert Questions
Accounting
General Management
Mathematics
Finance
Organizational Behaviour
Law
Physics
Operating System
Management Leadership
Sociology
Programming
Marketing
Database
Computer Network
Economics
Textbooks Solutions
Accounting
Managerial Accounting
Management Leadership
Cost Accounting
Statistics
Business Law
Corporate Finance
Finance
Economics
Auditing
Tutors
Online Tutors
Find a Tutor
Hire a Tutor
Become a Tutor
AI Tutor
AI Study Planner
NEW
Sell Books
Search
Search
Sign In
Register
study help
business
communication research
Communication Research Asking Questions Finding Answers 4th Edition Joann Keyton - Solutions
8. R provides information about the amount of variance of the dependent variable explained by the independent variables separately or in common.
7. Regression is particularly well suited for communication research because it tests the relationship among naturally occurring variables.
6. Regression is an extension of correlation; however, multiple regression can test for the influence of multiple independent or predictor variables on the dependent or criterion variable.
5. In a correlation, researchers rely on r to describe the amount of variance shared between the two variables.
4. A correlation coefficient must be inter- preted for its direction and its strength or magnitude.
3. Causation cannot necessarily be established with correlation.
2. A correlation is a simple description of the degree to which two variables are related.
1. The degree to which the following assump- tions are met determine the degree to which findings from the tests can be generalized from the sample to the population: (a) sig- nificance level of the test is based on prob- ability theory, (b) data are assumed to come from a normally distributed
7. In the discussion section, look for the researcher's interpretation of the regres- sion results. To what degree are the statistically significant results practical or relevant to the issue being studied? Do you agree with the researcher's interpre- tation of the separate and common variance
6. Examine the beta weights to indicate the individual contribution or influence of each independent variable on the dependent variable.
5. If the p of the F is greater than .05, retain the null hypothesis. Any relationships reported are due to chance or variables and are not related enough to be statisti- cally significant.
4. If the p of the F is .05 or less, accept the relationships in the regression test. The relationships found are statistically significant. Determine if the relationships found are the relationships predicted by the hypothesis.
3. In the results section, look for the specific test results. You must find the F, R, and the significance level, or p. Also look for B.
2. From information presented in the method section, verify that each variable in the regression hypothesis is measured continuously.
1. Identify the research hypothesis or research question. Develop the related null hypothesis or statement of no relationship.
6. In the discussion section, look for the researcher's interpretation of the correla- tion. To what degree are the statistically significant results practical or relevant to the issue being studied? Independently come to your own conclusion. Do you agree or disagree with the researcher?
5. If the p of the r is greater than .05, retain the null hypothesis. Any relationship reported is due to chance, or the variables are not related enough to be statisti- cally significant.
4. If the p of the r is .05 or less, accept the relationship in the test of correlation. The relationship found is statistically significant. Determine if the relationship found is the relationship predicted by the hypothesis.
3. In the results section, look for the specific test results. You must find the r and the significance level, or p.
2. From information presented in the method section, verify that each variable in the correlation hypothesis is measured continuously.
1. Identify the research hypothesis or research question. Develop the related null hypothesis or statement of no relationship.
7. Identify structural equation modeling as tests of relationships.
6. Interpret research findings developed from results of correlation and regression.
5. Differentiate among the assumptions and functions of correlation and regression.
4. Develop a hypothesis or research question and select the appropriate statistical test of relationship (correlation or regression).
3. Know which assumptions of inferential sta- tistics your research project meets and which assumptions it does not meet.
2. Use the four analytical steps to design and interpret research designs and statistical findings.
1. Explain the difference between tests of differ- ences and tests of relationships.
14. Factorial ANOVA can accommodate three or for independent variables.
13. Both main effects and interaction effects are possible in a two-way ANOVA.
12. A two-way ANOVA tests for the effects of two categorical independent variables on a continuous level dependent variable.
11. A one-way ANOVA tests for significant dif- ferences in the continuous level dependent variable based on categorical differences 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.
9. Analysis of variance, or ANOVA, compares the influence of two or more groups of the in- dependent variable on the dependent variable.
8. A t-test can be two-tailed, in which any dif- ference found is accepted, or one-tailed, in which the direction of the difference is spec- ified by the research question or hypothesis.
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 variables.
5. Four analytical steps assist the researcher through statistical interpretation of tests of differences: (1) conducting the statistical test to determine if differences exist; (2) charac- terizing the differences found as expected or not expected; (3) assessing differences for statistical
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 as- signment of participants to groups.
2. The function of inferential statistics is to draw conclusions about a population by ex- amining the sample.
1. Chi-square, t-test, and ANOVA are statisti- cal tests of difference.
6. In the discussion section, look for the researcher's interpretation of F. To what degree are the statistically significant results practical or relevant to the issue being studied? Independently come to your own conclusion. Do you agree or disagree with the researcher?
5. If the p of the F is greater than .05, retain the null hypothesis. Any differences reported are due to chance or are not different enough to be statistically significant.
4. If the p associated with the F is .05 or less, accept the alternative hypothesis. The differences found are statistically significant. Determine if the differences found are the differences predicted by the hypothesis. If planned comparisons were not directed by the hypothesis, what post hoc
3. In the results section, look for the specific test results. You must find the F and the significance level, or p. Also look for the mean scores of the dependent variable for each category or group on each independent variable.
2. From information presented in the method section, verify that each independent variable is at the nominal, or categorical, level. Identify the number and type of categories or groups for each variable. Verify that the dependent variable is continuous level data (interval or ratio).
1. Identify the research hypothesis or research question. Does the hypothesis or research question include planned comparisons among categories of the in- dependent variable? Develop the related null hypothesis or statement of no differences.
6. In the discussion section, look for the researcher's interpretation of the t-test. To what degree are the statistically significant results practical or relevant to the issue being studied? Independently come to your own conclusion. Do you agree or disagree with the researcher?
5. If the p of the t is greater than .05, retain the null hypothesis. Any differences re- ported are due to chance or are not different enough to be statistically significant.
4. If the p associated with the t is .05 or less, accept the differences in this t-test. The differences found are statistically significant. Determine if the differences found are the differences predicted by the hypothesis.
3. In the results section, look for the specific test results. You must find the t and the significance level, or p. Also look for the mean scores of the dependent variable for each category or grouping of the independent variable.
2. From information presented in the method section, verify that the independent variable in the t-test hypothesis is at the nominal, or categorical, level. Identify the two categories or groups for this variable. Verify that the dependent vari- able is continuous-level data (interval or ratio).
1. Identify the research hypothesis or research question. Is the hypothesis or research question directional or nondirectional? Develop the related null hypothesis or statement of no differences.
6. In the discussion section, look for the researcher's interpretation of the chi- square. To what degree are the statistically significant results practical or rel- evant to the issue being studied? Independently come to your own conclusion. Do you agree or disagree with the researcher?
4. If the p of the x is .05 or less, accept the differences in the chi-square test. The differences found are statistically significant. Determine if the differences found are the differences predicted by the hypothesis. 5. If the p of the x' is greater than .05, retain the null hypothesis. Any
3. In the results section, look for the specific test results. You must find the x and the significance level, or p.
2. From information presented in the method section, verify that each variable in the chi-square hypothesis is at the nominal or categorical level. Identify the cat- egories or groups for each variable.
1. Identify the research hypothesis or research question. Develop the related null hypothesis or statement of no differences.
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 re- searchers accept or reject results as valid when the opposite is true.
16. Hypothesis testing is an act of decision making accepting the alternative hypothesis 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 statistical 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, me- dian, 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 curve.
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 abil- ity unless theoretical models have been developed 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 questionnaires is internal reliability, or the degree to which multiple questions or items consistently 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.
Showing 1700 - 1800
of 2089
First
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Step by Step Answers
Get 50% OFF
Claim Now
