youtube video

Introduction to Research Methods

  • Research methods can be challenging; students may need multiple exposures to fully understand topics.

  • Importance of concepts like types of validity when discussing experimental designs.

Overview of Content

  • The video aims to cover essential aspects of research methods based on the OCR specification.

  • Simplified breakdown of the content to aid in comprehension.

Variables

Types of Variables

  1. Independent Variable (IV)

    • Defined as the variable that is manipulated by the researcher to observe its effect on another variable.

    • Example: In a study examining the effect of temperature on math scores, the IV is temperature (hot vs. cold room).

  2. Dependent Variable (DV)

    • This is what the researcher measures in the experiment.

    • Example: In the temperature and math scores study, the DV is the math scores achieved by students.

  3. Co-variables

    • Used in correlational studies; these are similar to an IV and DV but measure the relationship between two factors without manipulation.

    • Example: In a correlation study, if we examine the relationship between hours studied and test scores, both hours studied and test scores are co-variables.

  4. Extraneous Variables

    • Variables that might affect the results of the study but are not controlled by the researcher.

    • Example: Noise levels in different rooms could be an extraneous variable impacting math scores in the hot vs. cold room experiment.

Hypotheses

Types of Hypotheses

  1. Alternate Hypothesis

    • Asserts that there is a significant difference or relationship.

    • Template example: "There will be a significant difference in math scores between students tested in a hot room versus a cold room."

  2. Null Hypothesis

    • Asserts that there is no significant difference or relationship.

    • Example: "There will be no significant difference in math scores between students in hot rooms and cold rooms."

Types of Data

Data Categories

  1. Qualitative Data

    • Descriptive data that can be analyzed for patterns, typically more detailed.

    • Example: Responses from interviews or open-ended questions.

  2. Quantitative Data

    • Numerical data that can be measured and compared easily.

    • Example: Test scores or survey responses on a numerical scale.

  3. Primary Data

    • Data collected firsthand by the researcher.

    • Example: Results from a survey conducted by the researcher.

  4. Secondary Data

    • Data collected by someone else that is used by the researcher.

    • Example: Information obtained from books or existing studies.

Strengths and Weaknesses

  • Quantitative Data: Strength: Easier to compare and analyze.

  • Qualitative Data: Strength: Provides detailed insights into participants' thoughts and feelings.

  • Primary Data: Strength: More reliable as it’s collected firsthand.

  • Secondary Data: Strength: Saves time and cost; can access a broader scope of data.

Sampling Methods

Participant Selection

  • Target Population: The larger group researchers aim to study.

    • Example: Year 11 students in the UK.

  • Sample: The smaller group selected from the target population for the study.

    • Example: 30 students from a local school.

Types of Sampling Techniques

  1. Random Sampling

    • Each participant in the target population has an equal chance of being selected.

  2. Opportunity Sampling

    • Participants are selected based on who is available at the time of the study.

  3. Self-Selected Sampling

    • Participants volunteer for the study, often leading to bias as only certain types of individuals may respond.

    • Example: Posting advertisements for participants and receiving responses.

Experimental Design

Types of Experimental Design

  1. Independent Measures Design

    • Each participant is exposed to only one condition of the experiment.

    • Example: Half of students take the math test in the hot room, while the other half take it in the cold room.

  2. Repeated Measures Design

    • Each participant experiences all conditions of the experiment.

    • Example: The same students take the math test in both the hot and cold rooms, at different times.

Strengths and Weaknesses of Designs

  • Independent Measures: Weakness: Individual differences can skew results.

  • Repeated Measures: Weakness: Order effects can influence performance.

Reliability of Research

Types of Reliability

  1. Internal Reliability

    • Consistency of results within the study itself; high reliability if the same results are achieved under similar conditions.

  2. External Reliability

    • Consistency of results across different times or places.

  3. Inter-rater Reliability

    • Agreement among different observers measuring the same phenomenon. High inter-rater reliability is indicated by consistent results from different raters.

Validity of Research

Types of Validity

  1. Ecological Validity

    • The extent to which findings can be generalized to real-life settings.

  2. Population Validity

    • The extent to which sample results apply to the wider population.

  3. Construct Validity

    • The extent to which a study accurately measures the theoretical construct it aims to study.

Biases in Research

Types of Bias

  1. Gender Bias

    • Unequal representation of genders in research studies.

  2. Cultural Bias

    • Over-reliance on a single culture, potentially skewing results.

  3. Age Bias

    • Preferences for certain age groups over others, which may not represent the broader population.

  4. Experimental Bias

    • Researcher influences results to support their theories.

  5. Observer Bias

    • Researchers' preconceptions influence their observations and interpretations.

  6. Bias in Questioning

    • Leading questions that influence participants' responses.

Ethical Guidelines in Research

Ethical Considerations (cant do cant do with participants acronym)

  1. Informed Consent: Participants must be fully aware of the research intent and methods.

  2. Deception: Should be minimized; any necessary deception must be justified.

  3. Confidentiality: Participants' identities must be protected; use of code names may be employed.

  4. Debriefing: A follow-up discussion after the experiment to ensure participants understand the research and feel psychologically well.

  5. Right to Withdraw: Participants must be able to withdraw from the study without penalty at any time.

  6. Protection from Harm: Participants should not leave the study in a worse physical or mental state than when they entered.

Experimental Methods vs Experimental Designs

Types of Experimentation

  1. Lab Studies: Highly controlled settings where the IV is manipulated.

  2. Field Studies: Natural environments where the researcher still manipulates the IV. (staircase piano experiment)

  3. Natural Experiments: Variables naturally occur and cannot be controlled by the researcher.

Strengths and Weaknesses

  • Lab Studies: Strength: Control reduces extraneous variables; Weakness: Low ecological validity.

  • Field Studies: Strength: High ecological validity; Weakness: More extraneous variables may impact results.

  • Natural Experiments: Strength: Ethical investigation of naturally occurring phenomena; Weakness: Difficult to establish cause and effect due to lack of control.

Interviews and Questionnaires

Interviews

  • Structured Interviews: Researcher asks predetermined questions, ensuring uniformity.

  • Unstructured Interviews: Questions evolve based on participants' responses, allowing for in-depth exploration.

  • Strengths: Access to personal thoughts and feelings; Weaknesses: Risk of social desirability bias.

Questionnaires

  • Allow for the gathering of self-reported attitudes and behaviors, typically with closed (fixed answers) or open questions (自由回答).

  • Closed questions yield quantitative data; open questions provide qualitative data.

  • Strengths: Can gather data quickly from large samples; Weaknesses: Potential for social desirability bias.

Observational Studies

Types of Observations

  1. Naturalistic Observations: Conducted in real-world settings where behavior occurs naturally.

  2. Controlled Observations: Conducted in environments manipulated by the researcher.

  3. Overt vs. Covert Observations: Participants are aware or unaware they are being observed, respectively.

  4. Participant vs. Non-Participant: Researchers join or do not join the group being studied.

Strengths and Weaknesses of Observations

  • Naturalistic: Strength - High ecological validity; Weakness - Difficulty establishing cause and effect.

  • Controlled: Strength - High control, reduced extraneous variables; Weakness - May lack ecological validity due to artificial settings.

  • Overt: Strength - Ethical clarity; Weakness - Risk of participant behavior alteration due to awareness of observation.

  • Covert: Strength - Natural behavior; Weakness - Ethical concerns due to lack of informed consent.

Case Studies

  • Involve extensive investigation of individuals or small groups, often over a long duration.

  • Provide rich qualitative data but may not be generalizable to larger populations.

  • Example cases: Clive Wearing and Freud’s The Wolfman highlight unique contexts.

Correlational Studies

Understanding Correlations

  • Measure the relationship between two co-variables without manipulating them.

  • Types of correlations:

    1. Positive Correlation: As one variable increases, so does the other (e.g., exercise and happiness).

    2. Negative Correlation: As one variable increases, the other decreases (e.g., exercise frequency and depression).

    3. Zero Correlation: No relationship between the variables.

  • Important: Correlations cannot establish causal relationships.

Conclusion and Study Tips

  • Repetition and practice through P paper questions advised for mastery of research methods.

  • Encouragement to re-watch segments of the video for consolidation of knowledge.

  • A blocket (game) presented as an additional study tool to reinforce concepts learned.