All Matches

Solution Library

Expert Answer

Textbooks

Search Textbook questions, tutors and Books

Oops, something went wrong!

Change your search query and then try again

Result Not Found

Books FREE
Tutors
Study Help
Hire a Tutor
AI Study Help New

Search
Sign In
Register

study help
business
communication research

Questions and Answers of Communication Research

3. Is an adequate description of the research method and procedures given?
2. Does the summary and evaluation of literature in the literature review put the current study into historical and scientific perspective?
1. Does the introduction contain a statement of the problem and some justification for its importance?
12. Submit your paper for review to a communication association convention.
11. Use the revision process to enhance the quality of the written research report.
10. Use APA style for direct and indirect citations and for developing the reference list.
9. Finish the research report with an appro- priate title, title page, abstract, and list of references.
8. Recommend future research ideas and methods.
7. Identify the limitations of your study and interpret the limitations with respect to your findings.
6. Write a discussion section that provides interpretations and implications of the research findings.
5. Write a results section that presents the findings in a straightforward manner.
4. Write a method section describing the participants, research procedures, and variables.
3. Review and revise, if necessary, the research questions and hypotheses presented in your study.
2. Review and revise, if necessary, the problem statement.
1. Review and revise your literature review to ensure that the literature review aligns with the designed and tested study.
13. Coding of interaction elements is based on the element itself, and what happens before and after it.
12. Interaction analysis focuses on the features or functions of the stream of conversational elements.
11. Interaction analysis, especially suitable for interpersonal and group communi- cation, codes the ongoing conversation between two or more individuals into categories.
10. Computer software is available to assist the researcher in the coding process.
9. Content analysis can be used to identify frequencies of occurrence, differences, trends, patterns, and standards.
8. Validity issues for content coding rest primarily with the appropriateness and adequacy of the coding scheme.
7. At least two trained coders code the selected content; interrater reliability must be calcu- lated for both unitizing and coding decisions.
6. Virtually any communication phenomena can be content analyzed; codable ele- ments include words or phrases, complete thoughts or sentences, themes, paragraphs or short whole texts, characters or
5. Coding schemes can be developed from existing theory or other published research findings, or coding schemes can emerge from the data.
4. Content analyses are often reported and analyzed using frequency counts and chi-square.
3. Category schemes allow researchers to code the manifest and latent meanings to text.
2. Content analysis is the most basic meth- odology for analyzing message content; it integrates the data collection method and analytical technique in a research design to reveal the occurrence of
1. Content analysis and interaction analysis are two quantitative methods for analyzing communication texts.
10. Assess the utility of the coding results with respect to the research questions and hypotheses.
9. Assess the validity of a coding scheme.
8. Reliably apply the coding scheme.
7. Reliably identify units of analysis.
6. Identify suitable texts or messages to be coded and analyzed.
5. Assess the appropriateness and adequacy of a category scheme.
4. Explain the basic processes for conducting a research study using interaction analysis.
3. Identify appropriate uses of interaction analysis.
2. Explain the basic processes for conducting a content analysis.
1. Differentiate between manifest and latent content in content analysis.
10. Structural equation modeling (SEM) allows a researcher to test whether a theoretical model (or hypothesized associations among multiple independent and dependent variables) is statistically
9. The beta weight, or , provides information about the direction and strength of influ- ence for each independent variable.
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
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
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
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?
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
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
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
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
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
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
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
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
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
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?
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
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
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
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?
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
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
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.

Showing 1600 - 1700 of 2029

First
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

Services

Sitemap
Fun
Definitions
Become Tutor
Study Help Categories
Recent Questions
Expert Questions

Company Info

Security
Copyrights
Privacy Policy
Terms & Conditions
SolutionInn Fee
Scholarship

Get In Touch

About Us
Contact Us
Career
Jobs
FAQ
Campus Ambassador

Secure Payment

Download Our App

© 2024 SolutionInn. All Rights Reserved