Survey Research Overview
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Questions and Answers

When is conducting survey research most appropriate?

Survey research is most appropriate when we have limited knowledge about a large group or population and require generalizable data. It's ideal for studying a large sample efficiently.

What are the key tasks involved in experimental research?

  • Manipulating the independent variable
  • Controlling for confounds
  • Measuring the dependent variable
  • All of the above (correct)

    • Match the following types of reliability with their definitions.

    Intracoder Reliability = Consistency of one coder over time Intercoder Reliability = Agreement among multiple coders

    What is the primary purpose of content analysis?

    <p>The primary purpose of content analysis is to provide a descriptive account of the content under investigation.</p> Signup and view all the answers

    Which of the following statistical tests is used for categorical variables?

    <p>Chi-square</p> Signup and view all the answers

    Correlation indicates a cause-and-effect relationship between variables.

    <p>False</p> Signup and view all the answers

    What is the purpose of a null hypothesis?

    <p>A statement suggesting no relationship or effect exists between variables. It serves as the baseline against which the research findings will be compared to determine if there is sufficient evidence to reject it.</p> Signup and view all the answers

    Which of the following is a type of error in hypothesis testing?

    <p>Both A and B</p> Signup and view all the answers

    Explain the concept of internal validity in experimental research.

    <p>Internal validity in experimental research refers to the confidence we have that the independent variable is the sole cause of the observed changes in the dependent variable. It ensures that alternative explanations or confounding factors are ruled out.</p> Signup and view all the answers

    A study with high internal validity will always have high external validity.

    <p>False</p> Signup and view all the answers

    What is a manipulation check, and why is it important?

    <p>A manipulation check is a measure that is used to confirm that the independent variable was manipulated effectively in the way that was intended by the researchers.</p> Signup and view all the answers

    Which of the following is NOT a strength of experimental research?

    <p>Generalizability to real-world contexts</p> Signup and view all the answers

    What are some ways to improve the response rate of a survey?

    <p>There are several ways to improve the response rate of a survey, including personalizing invitations, offering incentives, sending follow-up reminders, and ensuring the survey is simple and relevant to the audience.</p> Signup and view all the answers

    Cronbach's Alpha is a measure of external validity.

    <p>False</p> Signup and view all the answers

    Which of the following is NOT a type of content analysis measure?

    <p>Hypothesis Testing</p> Signup and view all the answers

    What is the role of intercoder reliability in content analysis?

    <p>In content analysis, intercoder reliability refers to the agreement between multiple coders who are independently coding the same content. It ensures consistency and accuracy in the coding process, minimizing bias and ensuring that the results are not influenced by individual coder preferences.</p> Signup and view all the answers

    What are some common decisions made when selecting a sampling strategy for content analysis?

    <p>Common decisions made when selecting a sampling strategy for content analysis include choosing a timeframe, deciding on the type of media to analyze, and identifying the smallest element of content that will be analyzed (unit of analysis).</p> Signup and view all the answers

    Probability samples are best suited for analyzing specific contexts, such as a single news event.

    <p>False</p> Signup and view all the answers

    What is the unit of analysis in content analysis?

    <p>The unit of analysis in content analysis refers to the smallest element of the content that is analyzed. It can be anything from individual words or phrases to complete articles, videos, or social media posts.</p> Signup and view all the answers

    Content analysis is limited to analyzing written texts.

    <p>False</p> Signup and view all the answers

    What are some of the critical elements of a research paper's introduction section?

    <p>A research paper's introduction section should define the research problem, state the rationale, and provide background information by presenting relevant theories and prior research to establish the study's foundation.</p> Signup and view all the answers

    What is the role of statistical tests in research?

    <p>Statistical tests are used to analyze data and test hypotheses to determine whether the results of a study are statistically significant, meaning they are unlikely to have occurred by chance.</p> Signup and view all the answers

    How are ANOVA and t-tests different?

    <p>ANOVA (Analysis of Variance) is used to compare means across more than two groups, while a t-test is used to check means between just two groups.</p> Signup and view all the answers

    Study Notes

    Survey Research

    • When to use surveys: Surveys are appropriate when little is known about a large population or when generalizable data is needed. They are efficient for large samples.
    • Comparison to other methods:
      • Survey vs. Content Analysis: Surveys collect original data from respondents while content analysis examines existing media or documents.
      • Survey vs. Experiment: Surveys describe or explore relationships (correlation) without manipulation. Experiments test causality by controlling variables.
      • Survey vs. In-depth interview: Surveys prioritize breadth of coverage while in-depth interviews provide rich, qualitative insights.
    • Common survey approaches:
      • Face-to-face interviews: High response rate but costly and time-consuming.
      • Telephone surveys: Fast and cost-effective but declining landline use reduces effectiveness.
      • Mail surveys: Useful for geographically dispersed populations but have lower response rates.
      • Group administration: Efficient for specific groups (e.g., classrooms).
      • Online surveys: Cost-effective and accessible but prone to low response rates and sampling bias.
    • Strengths of surveys:
      • Efficient for large samples.
      • Can be generalized if probability sampling is used.
      • Economically sound.
      • Non-intrusive and convenient.
    • Weaknesses of surveys:
      • Limited depth.
      • Potential sampling errors (nonresponse, self-selection bias).
      • Difficulty in ensuring honest responses due to social desirability or recall issues.

    Reliability and Validity

    • Reliability: Consistency of measurement; ensured by pretesting, clear questions, and training.
    • Validity: Accuracy of measurement; whether it measures what it's intended to. Improved by well-constructed questions and representative sampling.

    Questionnaire Construction

    • Common problems: Double-barreled questions, biased or leading terms, poorly defined terms, overly complex recall, or double negatives.
    • Good practices: Using clear, concise questions, avoiding leading or biased language, and avoiding absolutes like "always" or "never." Ensure a balance in statements (e.g., agree/disagree). Use Gade's Top 10 list as a guide.

    Levels of Measurement

    • Nominal: Categories without a logical order (e.g., gender, marital status).
    • Ordinal: Ordered categories with unequal intervals (e.g., Likert scales).
    • Interval: Ordered with equal intervals; no true zero (e.g., temperature in Celsius).
    • Ratio: Ordered with equal intervals and a true zero (e.g., age, income).

    Survey Response Rate

    • Improvement strategies:
      • Personalize invitations.
      • Offer incentives.
      • Send follow-up reminders.

    Statistical Analysis

    • Descriptive vs. Inferential Statistics:
      • Descriptive Statistics: Summarize characteristics of the dataset.
        • Types: Central tendency (mean, median, mode); Dispersion (range, standard deviation); Frequency and proportion.
      • Inferential Statistics: Used to make inferences about a population from a sample. Tests relationships or differences between variables.
    • Null Hypothesis: Suggests no relationship or effect exists between variables.
    • P-value: The probability that the observed results occurred by chance.
      • If p < 0.05, reject the null hypothesis (statistically significant).
      • If p > 0.05, fail to reject the null hypothesis (not statistically significant).

    Hypothesis Testing

    • Variables:

      • Independent Variable (IV): Manipulated or categorized factor.
      • Dependent Variable (DV): Measured outcome.

    • Statistical Tests:

      • Parametric: Require normal distribution (e.g., t-test, ANOVA).
      • Non-parametric: Require no assumption of normal distribution (e.g., Chi-square).
      • Example uses: T-test for comparing means between two groups; Chi-square for analyzing differences among categorical variables. Correlation examines relationship strength and direction; regression predicts one variable; ANOVA compares means across multiple groups.

    • Hypothesis Testing Errors:

      • Type I Error (False Positive): Rejecting a true null hypothesis.
      • Type II Error (False Negative): Failing to reject a false null hypothesis.

    Content Analysis

    • Definition: A research technique for objective, systematic and quantitative description of manifest content of communication.

    • Characteristics:

      • Objectives: Clear, defined categories to ensure different coders agree;
      • Systematic: Procedures used consistently;
      • Quantitative: Focus on numerical representation.
      • Manifest Content: Analyzes explicit content only.
      • Objectives and Purpose: Describe content, infer gatekeeping or effects, test hypotheses.

    • Sampling Issues:

      • probability (generalizable) and non-probability (specific contexts)

    • Measures: Content analyzed can examine factors like manifest content (length, number of sources), trends over time, framing, visual representations.

    • Reliability:

      • Intracoder: Consistency of one coder over time.
      • Intercoder: Agreement amongst multiple coders. Improved via training sessions.

    • Testing Methods: Krippendorff's Alpha (0.7 or above is acceptable)

    Experimental Research

    • Definition: Determines causality by manipulating independent variables (IVs) and measuring their effects on dependent variables (DVs), while controlling for confounds.
    • When appropriate: To establish cause-and-effect relationships.
    • Key tasks: Manipulate the IV and control for confounds (other variables).
    • Internal Validity: Ensuring only the IV caused the effect (e.g., by randomization).
    • External Validity: Generalizability to other populations or settings.
    • Threats to internal validity: Selection bias, history, testing, maturation.
    • Threats to external validity: Artificiality, limited sample diversity.
    • Basic experimental designs: Posttest-only with control group; pretest-posttest with control group; Solomon four-group design; factorial design.

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    Survey Research Study Guide PDF

    Description

    This quiz covers the fundamentals of survey research, outlining when to use surveys and how they compare to other research methods. Learn about common survey approaches and their respective advantages and disadvantages in this informative quiz.

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