🔬Communication Research Methods Unit 2 – Quantitative Methods in Communication Research

Key Concepts and Terminology

• Quantitative research involves collecting and analyzing numerical data to test hypotheses, measure variables, and identify patterns or relationships
• Variables are characteristics or attributes that can be measured or manipulated in a study
  • Independent variables (IVs) are manipulated by the researcher to observe their effect on the dependent variable
  • Dependent variables (DVs) are measured to determine the effect of the independent variable
• Hypotheses are testable predictions about the relationship between variables
• Operationalization is the process of defining variables in measurable terms
• Reliability refers to the consistency of a measure, ensuring that it produces similar results under consistent conditions
• Validity is the extent to which a measure accurately reflects the concept it is intended to measure (face validity, construct validity, criterion validity)
• Generalizability is the extent to which findings from a sample can be applied to the larger population

Quantitative Research Design

• Experimental designs involve manipulating one or more independent variables to observe their effect on the dependent variable while controlling for other variables
  • True experiments require random assignment of participants to conditions and manipulation of the independent variable
  • Quasi-experiments lack random assignment but still manipulate the independent variable
• Non-experimental designs, such as surveys and observational studies, do not involve manipulation of variables but instead measure variables as they naturally occur
• Cross-sectional designs collect data from a sample at a single point in time
• Longitudinal designs collect data from the same sample at multiple points over an extended period
• Between-subjects designs compare different groups of participants, each exposed to a different level of the independent variable
• Within-subjects designs expose each participant to all levels of the independent variable, allowing for comparisons within individuals

Data Collection Methods

• Surveys involve administering a set of questions to a sample of participants to gather self-reported data (Likert scales, multiple-choice questions)
• Interviews are one-on-one conversations between a researcher and participant, allowing for in-depth exploration of a topic (structured, semi-structured, unstructured)
• Observations involve systematically watching and recording behavior in natural or controlled settings (participant observation, non-participant observation)
• Experiments manipulate one or more independent variables in a controlled setting to observe their effect on the dependent variable
• Content analysis systematically analyzes the content of media messages (newspapers, social media posts, advertisements)
• Physiological measures record biological responses (heart rate, skin conductance, brain activity) to stimuli or situations
• Archival research involves analyzing existing data, such as public records or previously collected datasets

Sampling Techniques

• Probability sampling ensures that every member of the population has an equal chance of being selected, allowing for generalization to the larger population
  • Simple random sampling selects participants at random from the population
  • Stratified random sampling divides the population into subgroups (strata) and then randomly selects participants from each stratum
  • Cluster sampling divides the population into clusters (geographic areas) and then randomly selects clusters and participants within those clusters
• Non-probability sampling does not ensure equal chances of selection and may limit generalizability
  • Convenience sampling selects participants based on their availability and willingness to participate
  • Purposive sampling selects participants based on specific characteristics or criteria relevant to the research question
  • Snowball sampling recruits initial participants who then refer other potential participants, allowing for the sample to grow

Statistical Analysis Basics

• Descriptive statistics summarize and describe the main features of a dataset (mean, median, mode, standard deviation)
• Inferential statistics use sample data to make inferences or draw conclusions about the larger population
• Hypothesis testing involves comparing sample data to what is expected under the null hypothesis to determine if the results are statistically significant
  • Null hypothesis ($H_0$) states that there is no significant difference or relationship between variables
  • Alternative hypothesis ($H_1$ or $H_a$) states that there is a significant difference or relationship between variables
• $p$-value is the probability of obtaining the observed results if the null hypothesis is true; a small $p$-value (typically $p < .05$) suggests that the results are statistically significant and the null hypothesis can be rejected
• Confidence intervals provide a range of values within which the true population parameter is likely to fall with a certain level of confidence (95% CI)
• Effect size measures the magnitude or strength of a relationship or difference between variables (Cohen's $d$, Pearson's $r$)

Advanced Statistical Techniques

• $t$-tests compare means between two groups or conditions to determine if the difference is statistically significant (independent samples $t$-test, paired samples $t$-test)
• Analysis of Variance (ANOVA) compares means across three or more groups or conditions (one-way ANOVA, factorial ANOVA)
  • $F$-statistic is used to determine if there are significant differences between group means
  • Post-hoc tests (Tukey's HSD, Bonferroni correction) are used to determine which specific group means differ significantly from each other
• Correlation examines the relationship between two continuous variables (Pearson's $r$ for linear relationships, Spearman's $\rho$ for non-linear or ordinal data)
• Regression predicts the value of a dependent variable based on one or more independent variables
  • Simple linear regression uses one independent variable to predict the dependent variable
  • Multiple regression uses two or more independent variables to predict the dependent variable
• Factor analysis identifies underlying factors or dimensions that explain the correlations among a set of variables
• Structural equation modeling (SEM) tests complex relationships among multiple variables, including latent variables that are not directly observable

Data Visualization and Reporting

• Tables present numerical data in a structured format, allowing for easy comparison of values across categories or conditions
• Bar graphs compare values across categories using horizontal or vertical bars (grouped bar graphs, stacked bar graphs)
• Line graphs display trends or changes in a variable over time or across a continuous variable
• Scatterplots show the relationship between two continuous variables, with each data point representing an observation
• Pie charts illustrate proportions or percentages of a whole, with each slice representing a category
• Error bars indicate the variability or uncertainty around a point estimate (standard error, confidence intervals)
• Effective data visualization should be clear, accurate, and visually appealing, using appropriate colors, labels, and scales
• Research reports should include an introduction, method section (participants, materials, procedure), results section (descriptive and inferential statistics, tables and figures), discussion section (interpretation of findings, limitations, implications), and references

Ethical Considerations in Quantitative Research

• Informed consent ensures that participants are fully informed about the purpose, procedures, and potential risks of the study and voluntarily agree to participate
• Confidentiality protects participants' identities and ensures that their data is kept secure and anonymous
• Anonymity goes beyond confidentiality by ensuring that even the researcher cannot link data to specific participants
• Deception involves intentionally withholding information or providing false information to participants; it should be minimized and only used when necessary for the study's validity
• Debriefing informs participants about the true nature of the study after their participation and provides an opportunity to ask questions or express concerns
• Researchers must balance the potential benefits of the study with the risks to participants and ensure that the benefits outweigh the risks
• Institutional Review Boards (IRBs) review and approve research proposals to ensure that they adhere to ethical guidelines and protect the rights and welfare of participants
• Researchers must be transparent in reporting their methods, results, and potential conflicts of interest to maintain the integrity of the research process