Research Methodology Quiz

Research Methodology

• Research Design - A comprehensive strategy that guides the researcher through the various stages of a study.
• Research Design - Quantitative designs are typically pre-established. Having a well-defined design helps researchers understand the data they are analyzing and interpreting.
• Five Quantitative Research Designs:
  • Descriptive
  • Correlational
  • Ex Post Facto
  • Quasi-experimental
  • Experimental
• Research Sampling - The process of selecting a representative group from a population to be studied.
• Population - The entire group of individuals, objects, or elements that share common characteristics under investigation.
• Two Types of Population:
  • Target Population - The actual group of interest in the study.
  • Accessible Population - The subset of the population that the researcher has reasonable access to.
• Sample - A smaller, representative group selected from the population for the study.
• Sample Size Heuristics:
  • Descriptive Research - 10-20% of the population.
  • Comparative Research - 15 subjects or groups.
  • Survey - 800 individuals.
  • Correlational - 100-200 individuals.
  • Ex Post Facto - 30+ individuals.
  • Experimental - 30 or more individuals.
• Sample Size Formulas:
  • Formulas provide a more precise way to determine sample size based on variables like population size and desired level of confidence.
  • The Cochran formula is particularly useful for large sample sizes.
• Power Analysis:
  • Statistical Power - The probability of rejecting the null hypothesis when it is false.
  • Ideal statistical power for a study is 80%.
  • Power analysis helps determine the sufficient sample size to detect a specific effect size.
  • Effect Size - The magnitude of the difference between the experimental and control groups.
• Probability Sampling Techniques:
  • Simple Random Sampling - Every member of the accessible population has an equal chance of being selected. Methods include fishbowl technique, roulette wheel, or using a table of random numbers.
  • Stratified Random Sampling - The population is first divided into strata based on specific characteristics, then samples are randomly selected from each strata. This is useful when studying variables like gender, grade level, etc.
  • Cluster Sampling - Used for large-scale, geographically dispersed populations. The population is divided into clusters, and then a predetermined number of clusters are randomly sampled.
  • Systematic Sampling - Samples are selected at regular intervals (e.g., every 5th individual) until the desired sample size is reached.
• Research Participants - Individuals who voluntarily participate in research studies after providing informed consent.
• Research Instruments - Tools for gathering data, like:
  • Performance tests
  • Questionnaires
  • Interviews
  • Observation checklists
• Techniques in Collecting Data:
  • Observation - Gathering data by observing behaviors or events, either directly or using devices.
  • Survey - Using questionnaires or interviews to collect numerical, categorical, or ordinal responses.
  • Experiment - Testing the effects of a treatment or intervention on participants.
• Three Phases in Gathering Data:
  • Before Data Collection:
    • Prepare research instruments
    • Obtain necessary permissions
    • Determine sample size and respondents
    • Secure informed consent (for participants 18 years or older) or parental consent (for minors).
    • Pilot test instruments (if needed).
  • During Data Collection:
    • Clearly instruct respondents.
    • Administer the research instrument or implement the intervention.
    • Carefully collect and record responses.
  • After Data Collection:
    • Summarize data in tabular form.
    • Analyze data to answer research questions.
• Data Analysis - The process of organizing, summarizing, and interpreting data to answer research questions.
• Statistical Treatment - Specific statistical methods and formulas used to analyze research data.
• Descriptive Statistical Techniques:
  • Summary of data that provides an overview of the sample, including:
    • Frequency distributions
    • Measures of central tendency (mean, median, mode)
    • Standard deviation
• Inferential Statistics:
  • Used to draw inferences about the population based on the sample data.
  • Includes techniques for:
    • Testing hypotheses
    • Making predictions
    • Generalizing findings
    • Drawing conclusions
• Types of Statistical Analysis of Variables:
  • Univariate Analysis - Examining a single variable.
  • Bivariate Analysis - Examining the relationship between two variables (e.g., independent and dependent variables).
  • Multivariate Analysis - Examining the relationship between multiple variables.
• Parametric Tests vs Non-Parametric Tests:
  • Parametric Tests - Make assumptions about the distribution of the population data (often assuming a normal distribution).
  • Non-Parametric Tests - Distribution-free, and can be used with non-normal variables.