Experimental Design in Research
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Questions and Answers

What is the dependent variable (DV)?

  • A variable being measured (correct)
  • A group that receives the IV
  • A group that doesn't receive the IV
  • A variable being manipulated
  • Randomization helps to control for extraneous variables.

    True

    The process of making inferences about a population based on a sample is known as _____ statistics.

    inferential

    What is a null hypothesis (H0)?

    <p>No significant difference or relationship.</p> Signup and view all the answers

    What is generally used to determine if the null hypothesis should be rejected?

    <p>p-value</p> Signup and view all the answers

    Interview-administered surveys are completed by the respondents themselves.

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

    What is reliability in research design?

    <p>Ensuring consistent results.</p> Signup and view all the answers

    Which step in the research process involves reviewing existing research?

    <p>Literature review</p> Signup and view all the answers

    The significance level is the predetermined _____ threshold.

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

    Match the following survey types with their descriptions:

    <p>Self-administered surveys = Respondents complete themselves Interviewer-administered surveys = Interviewers ask questions and record responses Online surveys = Online questionnaires</p> Signup and view all the answers

    Study Notes

    Experimental Design

    • Types of Experimental Designs:
      • Laboratory experiments: controlled environment, artificial setting
      • Field experiments: natural setting, real-world conditions
      • Quasi-experiments: lacks random assignment, but attempts to control variables
    • Key Components:
      • Independent variable (IV): variable being manipulated
      • Dependent variable (DV): variable being measured
      • Control group: group that doesn't receive the IV
      • Experimental group: group that receives the IV
    • Experimental Design Principles:
      • Randomization: randomly assigning participants to groups
      • Control: controlling for extraneous variables
      • Manipulation: manipulating the IV

    Data Analysis

    • Types of Data Analysis:
      • Descriptive statistics: summarizing and describing data
      • Inferential statistics: making inferences about a population based on a sample
    • Common Data Analysis Techniques:
      • Measures of central tendency (mean, median, mode)
      • Measures of variability (range, variance, standard deviation)
      • Correlation analysis
      • Hypothesis testing (see below)
    • Data Visualization:
      • Graphs (bar, line, scatter plots)
      • Charts (histograms, box plots)

    Hypothesis Testing

    • Null and Alternative Hypotheses:
      • Null hypothesis (H0): no significant difference or relationship
      • Alternative hypothesis (H1): significant difference or relationship
    • Test Statistics and p-values:
      • Test statistic: numerical value calculated from sample data
      • p-value: probability of obtaining the test statistic by chance
      • Significance level: predetermined probability threshold (e.g., 0.05)
    • Hypothesis Testing Steps:
      1. State the null and alternative hypotheses
      2. Choose a significance level
      3. Calculate the test statistic and p-value
      4. Compare the p-value to the significance level
      5. Reject or fail to reject the null hypothesis

    Survey Research

    • Survey Types:
      • Self-administered surveys: respondents complete themselves
      • Interviewer-administered surveys: interviewers ask questions and record responses
      • Online surveys: online questionnaires
    • Survey Design Principles:
      • Sampling: selecting a representative sample from a population
      • Questionnaire design: creating clear, unbiased questions
      • Data collection: collecting data through surveys
    • Survey Data Analysis:
      • Frequency distributions: summarizing response frequencies
      • Crosstabulations: analyzing relationships between variables
      • Scale analysis: analyzing responses to Likert scales or other rating scales

    Research Design

    • Research Design Types:
      • Experimental design (see above)
      • Quasi-experimental design (see above)
      • Non-experimental design (e.g., correlational, survey research)
    • Research Design Principles:
      • Internal validity: ensuring the study measures what it claims to measure
      • External validity: ensuring the study's results can be generalized
      • Reliability: ensuring consistent results
    • Research Design Considerations:
      • Sample size and selection
      • Data collection methods
      • Research setting (laboratory, field, online)

    Research Process

    • Research Process Steps:
      1. Problem formulation: identifying a research question or problem
      2. Literature review: reviewing existing research on the topic
      3. Hypothesis formulation: stating a hypothesis or research question
      4. Research design: selecting a research design
      5. Data collection: collecting data
      6. Data analysis: analyzing data
      7. Interpretation and presentation: interpreting results and presenting findings
    • Research Process Considerations:
      • Ethics: ensuring the study is conducted ethically
      • Bias: minimizing bias throughout the research process
      • Validity: ensuring the study's results are valid and generalizable

    Experimental Design

    • Laboratory experiments: controlled environment, artificial setting, allows for high control over variables
    • Field experiments: natural setting, real-world conditions, high ecological validity
    • Quasi-experiments: lacks random assignment, but attempts to control variables, often used in real-world settings
    • Independent variable (IV): variable being manipulated, can be categorical or continuous
    • Dependent variable (DV): variable being measured, can be categorical or continuous
    • Control group: group that doesn't receive the IV, used as a baseline for comparison
    • Experimental group: group that receives the IV, used to measure the effect of the IV
    • Randomization: randomly assigning participants to groups, helps to minimize confounding variables
    • Control: controlling for extraneous variables, helps to isolate the effect of the IV
    • Manipulation: manipulating the IV, helps to establish cause-and-effect relationships

    Data Analysis

    • Descriptive statistics: summarizing and describing data, includes measures of central tendency and variability
    • Inferential statistics: making inferences about a population based on a sample, uses statistical tests and models
    • Measures of central tendency: mean, median, mode, describe the "average" value of a dataset
    • Measures of variability: range, variance, standard deviation, describe the spread of a dataset
    • Correlation analysis: examines the relationship between two continuous variables
    • Data visualization: uses graphs and charts to communicate data insights, includes bar charts, line graphs, scatter plots, histograms, and box plots

    Hypothesis Testing

    • Null hypothesis (H0): no significant difference or relationship, a statement of no effect
    • Alternative hypothesis (H1): significant difference or relationship, a statement of an effect
    • Test statistic: a numerical value calculated from sample data, used to determine the probability of the null hypothesis
    • p-value: the probability of obtaining the test statistic by chance, used to determine the significance of the results
    • Significance level: a predetermined probability threshold, typically 0.05, used to determine whether to reject the null hypothesis
    • Hypothesis testing steps: state the null and alternative hypotheses, choose a significance level, calculate the test statistic and p-value, compare the p-value to the significance level, reject or fail to reject the null hypothesis

    Survey Research

    • Self-administered surveys: respondents complete themselves, often online or through mail-in questionnaires
    • Interviewer-administered surveys: interviewers ask questions and record responses, often in-person or over the phone
    • Online surveys: online questionnaires, often used for large-scale data collection
    • Sampling: selecting a representative sample from a population, helps to ensure generalizability
    • Questionnaire design: creating clear, unbiased questions, helps to minimize respondent bias
    • Frequency distributions: summarizing response frequencies, used to analyze categorical data
    • Crosstabulations: analyzing relationships between variables, used to examine correlations
    • Scale analysis: analyzing responses to Likert scales or other rating scales, used to examine attitudes and opinions

    Research Design

    • Experimental design: involves manipulating an IV and measuring the effect on a DV, high internal validity
    • Quasi-experimental design: lacks random assignment, but attempts to control variables, often used in real-world settings
    • Non-experimental design: doesn't involve manipulating an IV, often used in survey research or correlational studies
    • Internal validity: ensuring the study measures what it claims to measure, high internal validity means the study is less prone to confounding variables
    • External validity: ensuring the study's results can be generalized, high external validity means the study's results can be applied to different populations and settings
    • Reliability: ensuring consistent results, high reliability means the study's results are consistent across different measurements and observers
    • Sample size and selection: affects the study's statistical power and generalizability
    • Data collection methods: affects the quality and validity of the data
    • Research setting: laboratory, field, or online, affects the study's ecological validity and generalizability

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    Description

    Understand the different types of experimental designs, including laboratory, field, and quasi-experiments, and their key components, such as independent and dependent variables, control groups, and experimental groups.

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