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An Introduction to Research Methods in Mass Media

15 Appendix

15.1 Glossary of Terms

2. Introduction to Research in Mass Communications

2.1 Overview of Research Methods

  • Artifact: Objects or media used in research to represent specific phenomena.
  • Attribute: Characteristics or qualities that can be measured or observed in research.
  • Coding: The process of systematically categorizing qualitative data to identify patterns.
  • Content: The substance of communication is often analyzed in media research to understand trends, effects, and implications.
  • Content Analysis: A method for systematically analyzing the content of media messages to identify patterns, themes, and implications.
  • Mixed Methods: An approach combining qualitative and quantitative research to comprehensively understand a topic.
  • Qualitative Research: Research focused on understanding the meaning and context behind media messages and audience experiences.
  • Quantitative Research: Research that involves collecting and analyzing numerical data to identify patterns, correlations, and causations..

2.2 Research Ethics and the IRB Process

  • Anonymity: Ensuring that participants cannot be identified based on the information they provide.
  • Confidentiality: Protecting the identity and data of participants from unauthorized disclosure.
  • Consent Forms: Documents that outline the study’s purpose, procedures, risks, and benefits to participants, used to obtain informed consent.
  • Debriefing: Providing participants with full information about the study after their participation, especially if deception was used.
  • Harm: The potential risks to participants that researchers must assess and minimize.
  • Incentive: Compensation or rewards offered to participants for their time, which should not coerce participation.
  • Informed Consent: Ensuring that participants understand the study’s purpose, procedures, risks, and benefits before agreeing to participate.
  • Observer-as-Participant: A research role where the researcher interacts with the subjects while observing them.
  • Observer Effect: The impact that a researcher’s presence can have on the subjects being studied.

3. Developing Research Questions and Hypotheses

3.1 Formulating Research Questions

  • Alternative Hypothesis (H1): A statement proposing a potential effect or relationship between variables, opposing the null hypothesis.
  • Concept: An abstract idea representing a phenomenon in research (e.g., “media influence,” “audience engagement”).
  • Null Hypothesis (H0): A statement that there is no effect or relationship between variables; serves as a baseline for testing.
  • Operational Definition: The process of defining how a concept will be measured in a specific study.
  • Research Question: The specific query that guides the direction of the study.

3.2 Measurement and Variables

  • Construct Validity: Ensures that the test measures the concept it is intended to measure.
  • Dependent Variable (DV): The variable that is measured and affected by changes in the independent variable.
  • Independent Variable (IV): The variable that is manipulated or categorized to observe its effect on the dependent variable.
  • Interval Level: Numerical data with equal intervals between values but no true zero point (e.g., temperature scales, Likert-type scales).
  • Measurement Error: The difference between the observed value and the true value of what is being measured.
  • Nominal Level: A classification of data into distinct categories without any order (e.g., gender, ethnicity, type of media).
  • Ordinal Level: A classification of data with a meaningful order but without consistent intervals (e.g., ranking of favorite TV shows).
  • Ratio Level: Numerical data with equal intervals and a true zero point (e.g., income, hours spent watching TV).
  • Reliability: The consistency of a measurement tool in producing the same results under the same conditions.
  • Validity: The extent to which a measurement tool accurately measures what it is intended to measure.

4. Designing Quantitative Research

4.1 Research Design

  • Between-Subjects Design: A research design where different participants are assigned to different groups, each group exposed to a different level of the independent variable.
  • Control Group: A group of participants that does not receive the experimental treatment, serving as a baseline for comparison.
  • Convenience Sampling: A sampling method where participants are selected based on their availability, though it may not produce a representative sample.
  • Cross-Sectional Design: A research design that involves observing a specific population at a single point in time.
  • Longitudinal Design: A research design that involves observing the same participants over a period of time to study changes and developments.
  • Random Sampling: A sampling method where every member of the population has an equal chance of being selected.
  • Stratified Sampling: A sampling method that involves dividing the population into subgroups (strata) and then randomly sampling from each group to ensure representation.
  • Within-Subjects Design: A research design where the same participants are exposed to all levels of the independent variable, allowing for direct comparison within the same group.

4.2 Data Collection Techniques

  • Complete Observer: A method where the researcher observes without interacting or participating in the environment.
  • Coding: The process of categorizing and tagging content to identify patterns, themes, or trends within qualitative data.
  • Closed-Ended Questions: Questions that provide respondents with a set of predefined responses to choose from.
  • Direct Observation: A method that involves systematically watching and recording behaviors or events as they occur naturally.
  • Latent Content: The underlying meanings or themes in media content that are not immediately obvious.
  • Likert-Type Item: A statement to which respondents indicate their level of agreement on a scale (e.g., strongly disagree to strongly agree).
  • Manifest Content: The tangible, observable elements of media content, such as the number of times a word appears in a text.
  • Open-Ended Questions: Questions that allow respondents to answer in their own words, providing richer data.
  • Participant Observation: A method where the researcher actively engages in the environment or group being studied while observing behaviors.

5. Data Analysis and Statistical Techniques

5.1 Descriptive Statistics

  • Mean: The arithmetic average of a set of numbers, calculated by adding all the values together and dividing by the number of values.
  • Median: The middle value in a data set when the values are arranged in ascending or descending order.
  • Mode: The most frequently occurring value in a data set.
  • Range: The difference between the highest and lowest values in a dataset.
  • Standard Deviation: A measure of the amount of variation or dispersion in a set of values, indicating how much individual data points differ from the mean.
  • Variance: The square of the standard deviation, representing the average of the squared differences from the mean.

5.2 Inferential Statistics

  • ANCOVA (Analysis of Covariance): Combines ANOVA with regression, adjusting for the effects of covariates to compare group means.
  • ANOVA (Analysis of Variance): A statistical test used to compare the means of three or more groups to determine if at least one mean is different.
  • Chi-Square Test: A test used to examine the association between categorical variables.
  • Correlation: A measure of the strength and direction of the relationship between two variables.
  • Logistic Regression: A type of regression used when the dependent variable is binary (e.g., yes/no, success/failure).
  • One-Tailed Test: A hypothesis test that examines the direction of the effect.
  • p-Value: The probability that the observed results are due to chance, given that the null hypothesis is true.
  • Pearson’s r: A measure of linear correlation between two variables, ranging from -1 to 1.
  • Regression Toward the Mean: The phenomenon where extreme measurements tend to be closer to the mean on subsequent measurements.
  • t-Test: A statistical test used to compare the means of two groups to determine if they are significantly different.
  • Two-Tailed Test: A hypothesis test that examines for any difference, regardless of direction.
  • Type I Error: The error made when a true null hypothesis is incorrectly rejected (a false positive).
  • Type II Error: The error made when a false null hypothesis is not rejected (a false negative).

5.3 Advanced Statistical Techniques

  • Confounds: Variables that might affect the dependent variable but are not the focus of the study, potentially leading to incorrect conclusions.
  • Factor Analysis: A technique used to reduce a large number of variables into a smaller set of factors, identifying underlying relationships between variables.
  • Interaction: Occurs when the effect of one independent variable on the dependent variable differs depending on the level of another independent variable.
  • MANOVA (Multivariate Analysis of Variance): An extension of ANOVA that allows for the comparison of multiple dependent variables across groups.
  • Statistical Control: Techniques used to hold constant the effects of confounding variables while examining the relationship between independent and dependent variables.

6. Data Management and Visualization

6.1 Using jamovi and RStudio

  • Coding in R: Writing and executing scripts in R, a programming language used for statistical computing and data analysis.
  • Coding of Data: Categorizing and assigning numerical or categorical labels to data for analysis.
  • Data Accuracy: Ensuring that the data used in analysis is accurate, reliable, and free of errors.
  • Data Import and Export: The process of bringing data into RStudio from various sources (e.g., CSV files) and exporting results for further use.
  • Dataset Variability: The spread or dispersion of data points within a dataset.
  • Descriptive Analysis: Using statistical tools to describe the basic features of the data, such as calculating means, medians, and standard deviations.
  • Error Handling in R: Identifying, diagnosing, and correcting errors in R code
  • Inferential Analysis: Drawing conclusions about a population based on sample data, typically through hypothesis testing.

6.2 Data Visualization

  • Adobe Express: A tool for creating infographics and other visual content with pre-designed templates and easy customization options.
  • Charts and Graphs: Visual representations of data, such as bar charts, line graphs, and pie charts, used to make complex information easier to understand.
  • Customizing Visualizations: Tailoring the appearance and elements of a visualization to communicate the data better and match the intended audience.
  • Designing Infographics: Combining data and visual design to communicate complex information quickly and clearly.
  • ggplot2: A powerful R package used for creating complex and customized visualizations.
  • Histograms: Visual representations that show the distribution of a dataset by displaying the frequency of data points within specified intervals (bins).
  • Interactive Visualizations: Visualizations that allow users to interact with the data, exploring different aspects by engaging with the visual elements.

7. Writing and Presenting Research

7.1 Writing the Research Report

  • Abstract: A concise summary of the research report, typically 150-250 words, highlighting the purpose, methods, key findings, and conclusions.
  • Appendix: Supplementary materials that support the research but are not essential to the main text, such as survey instruments or detailed tables.
  • Avoiding Bias: Writing objectively, presenting data and interpretations without personal bias, and acknowledging alternative perspectives.
  • Clarity: Writing in a way that is easy to understand, avoiding jargon, and making the research accessible to a broad audience.
  • Conciseness: Expressing ideas in as few words as necessary without sacrificing meaning or detail.
  • Discussion Section: The section that interprets the results, explaining their implications, limitations, and how they fit into the existing body of research.
  • Introduction: The section of a research report that sets the context, stating the research question, its significance, and the study’s objectives.
  • Literature Review: A synthesis of existing research on the topic, identifying gaps and situating the current study within the broader academic context.
  • Method Section: The section that details how the research was conducted, including descriptions of participants, materials, procedure, and data analysis.
  • Results Section: The section that presents the findings of the study, typically using tables, graphs, and statistical analysis.
  • Reference List: The section of the research report that provides full citations for all sources cited, formatted according to APA style.

7.2 Presenting Research Findings

  • Blog Posts: Short, informal pieces of writing that allow researchers to communicate their findings to a broader audience in an accessible and engaging way.
  • Feature Article: A detailed and well-researched piece of writing that provides an in-depth look at a specific topic or research finding.
  • Future Directions: Discussing the implications of the research and potential areas for further study or application.
  • Infographic: A visual representation of information, data, or knowledge intended to present complex information quickly and clearly.
  • Narrative Construction: Organizing the presentation logically, with a clear beginning, middle, and end, to guide the audience through the research story.
  • Social Media Strategies: Using platforms like Twitter, LinkedIn, and Instagram to disseminate research findings and engage with the public.
  • Visual Aids: Tools such as slides, charts, and diagrams used to convey information clearly and engage the audience during presentations.

8. Special Topics in Research Methods

8.1 Ethical Issues in Emerging Media Research

  • Internet Panels: A research method where participants regularly complete online surveys or participate in online studies, requiring careful navigation of privacy concerns.
  • Internet Surveys: Surveys conducted online, which offer convenience but also raise privacy issues such as data breaches and unauthorized access.
  • Nonreactive Measures: Data collection methods where participants are not aware they are being studied, reducing the likelihood of behavior alteration due to the researcher’s presence.
  • Sampling Bias: A bias that occurs when the sample is not representative of the population, often a challenge in online and social media research due to self-selection and platform-specific demographics.
  • Social Desirability: The tendency of respondents to answer questions in a manner that they believe will be viewed favorably by others.

15.2 Assignments

  • Introduction Post: Answer a series of questions to introduce yourself to the class. Also, share a photo of yourself.
  • Annotated Manuscript: Students must find a research article related to mass communication or mass media, highlight sections of it that may be relevant to a future research article, and annotate those highlighted sections.
  • Team Contract: List the names of your team members and the roles they will play in the group project. Also, list the expectations for each team member.
  • IRB Certification: Complete the CITI training on human subjects research. Submit the PDF certificate of completion.
  • Meet with Professor: You will meet with the professor to discuss your project and receive feedback.
  • Scale Selection: Identify a scale that your team plans to use in your project. Provide a brief description of the scale and why you chose it.
  • Topic Justification: Justify why your team chose the topic for your project. This justification should include a basic overview of some of the literature that discusses your topic. You must also identify your research questions or hypotheses (at least 2).
  • Research Design: Describe the research design that your team plans to use in your project. This should include a description of the independent and dependent variables, the sample, and the data collection method.
  • IRB Proposal: Submit a draft of your IRB proposal. This will be graded independently from the actual IRB submission. You must submit the IRB proposal before you can begin data collection.
  • Create a Project [R]: Create a project in RStudio that includes a .Rmd file with 3 basic R commands that read data into the environment.
  • Import + Clean Data [R]: Import a pre-selected dataset into RStudio. You should then remove entries with missing data. Finally, convert the scale items into an average score.
  • Data Analysis [R]: You are provided with a data set in a .csv file. You must import the data in RStudio and conduct a series of basic data analyses. You will complete the analyses and explain the results in an RMarkdown file.
  • Data Visualization [R]: You are provided with a data set in a .csv file. You must import the data in RStudio and create a series of visualizations. You will create the visualizations and explain the results in an RMarkdown file.
  • Data Visualization [AE]: You are to take your visualizations from your previous assignment and create new visualizations using Adobe Express that meet specific criteria.
  • Project Draft: Submit a draft of your project. This draft should express the current state of your project. It should identify areas that are incomplete and express the path to completion.
  • Quizzes (4): You will have 4 quizzes throughout the semester. These quizzes will cover specific book materials. All quizzes are due at the same time.
  • Project: The final project is a group project that can be (1) a feature article, (2) an infographic with an accompanying white paper, or (3) a scripted video package. The information must follow a traditional research order. The project must be submitted as a group. The submission includes individual reviews of the partner contract.