Exhaustive Study Guide for Research Methods in Psychology (Topic 11)

Topic 11: Research Methods - How Do You Carry Out Psychological Research?

• Topic Overview: Research methods encompass the techniques and strategies used by psychologists to investigate human behavior and mental processes.

• Assessment Requirements (Section C): Knowledge of research methods is assessed across two primary papers:

  • Paper 1: Students are required to apply their knowledge of research methods and study conduction to new contexts. Specific details about studies will be provided within the questions.

  • Paper 2: This paper assesses understanding in depth. The contexts used for questions will draw from the compulsory topics covered in Paper 1.

  • Application: In examinations, students may be tasked with designing a research study or evaluating existing study designs based on stimulus material, utilizing terms outlined in Section 11.1.

Subject Content: 11.1 Designing Psychological Research

• 11.1.1 Identification of Variables: Students must be able to identify:

  • a. Independent Variable ($IV$).

  • b. Dependent Variable ($DV$).

  • c. Extraneous variables (including situational and participant variables).

• 11.1.2 Control of Variables: Students must understand the influence of extraneous variables and suggest methods of control:

  • a. Use of standardised procedures.

  • b. Counterbalancing.

  • c. Randomisation.

  • d. Single-blind techniques.

  • e. Double-blind techniques.

• 11.1.3 Null Hypothesis: Proficiency in writing a null hypothesis.

• 11.1.4 Alternative Hypothesis: Proficiency in writing an alternative hypothesis.

• 11.1.5 Sampling Methods: Understanding different methods of sampling, including their respective strengths and weaknesses.

Understanding Variables

• Definition: Variables are factors that can affect an experiment and vary. They are subject to change, observation, manipulation, or measurement.

• Independent Variable ($IV$):

  • Considered the Cause.

  • It stands alone and is not changed by other measured variables.

  • The researcher intentionally manipulates the $IV$ to observe effects.

• Dependent Variable ($DV$):

  • Considered the Effect.

  • This is the variable that is measured in the investigation.

  • It depends on other factors; specifically, changes in the $DV$ should ideally result only from changes in the $IV$.

• Control Variables: These are variables that must be kept constant to ensure the experiment is a fair test.

• Operationalization of Variables: This is the process of defining variables precisely so they can be accurately manipulated, measured, quantified, and replicated. It involves turning abstract concepts into measurable indicators.

  • Example: Social Anxiety: Social anxiety cannot be measured directly. It must be operationalized through:

    • Self-rating scores on a specific social anxiety scale.

    • Number of behavioral incidents of avoiding crowded places.

    • Intensity of physical anxiety symptoms in social settings.

Examples of Variables and Operationalization

• Example 1: Memory in School Children:

  • Aim: Investigating memory capacity in children.

  • Independent Variable ($IV$): Age.

  • Operationalized IV: Two levels: Level 1 ($5-7$\,years) and Level 2 ($9-10$\,years).

  • Operationalized DV: The specific number of items a child remembers after viewing a tray containing a maximum of $25$\,objects.

• Example 2: Plant Growth:

  • Independent Variable ($IV$): Type of liquid used for watering.

  • Conditions (Levels): Level A (Cola) and Level B (Orange Juice).

  • Dependent Variable ($DV$): The amount of growth recorded.

Extraneous and Confounding Variables

• Extraneous Variables: Any variables other than the $IV$ that have the potential to affect the outcome ($DV$) of a study. If not controlled, they add error and may negatively affect (confound) subsequent data.

  • Situational Variables: Factors in the environment or situation where the experiment occurs. Examples include:

    • Noise levels.

    • Lighting conditions.

    • Room temperature.

    • Time of day (e.g., testing cognitive abilities in the morning vs. evening).

    • Weather.

  • Participant Variables: Natural characteristics or individual differences of the participants. Examples include:

    • Age and Gender.

    • Mood and physical health.

    • Intelligence and memory levels.

    • Motivation and level of previous experience.

    • Cultural background.

• Confounding Variables: Variables that systematically affect all conditions of the $IV$ or randomly affect one condition, making it impossible to determine if the $IV$ caused the change in the $DV$.

• Control Status:

  • Controlled Variables: Extraneous variables that the experimenter has identified and managed (e.g., Bandura controlled for pre-existing aggression by having teachers/experimenters score children beforehand).

  • Uncontrolled Variables: Factors that cannot be successfully eliminated by the experimenter (e.g., the weather), which consequently lower the validity of findings.

Experimenter Variables

• Definition: These occur when the presence or behavior of the researcher affects the outcome.

• Types:

  • Demand Characteristics: Occurs when participants respond in a way they think the researcher wants (to please or upset them). This is controlled via Single-blind studies (where participants are unaware of the expected behavior). Example: The Bandura Study used a one-way mirror so children didn't know they were observed.

  • Experimenter Bias: Occurs when a researcher wants a specific outcome and gives "signals" (e.g., smiling when a participant does the desired action). This is controlled via Double-blind studies (where neither the participant nor the experimenter knows which group the participant is in). Example: Clinical drug trials.

Advantages and Disadvantages of Controlling Variables

• Advantages:

  • Establishing Cause and Effect: More control over extraneous variables ensures the change in the $DV$ is due to the $IV$ (e.g., Bandura Study on Aggression).

  • Predictable Behavior: Participants are more likely to behave in ways predicted by the hypothesis (e.g., Baron-Cohen Study Eyes Test).

  • Benchmark of Normality: Controls allow for a baseline comparison between the experimental and control groups (e.g., Andrade Doodling Study).

• Disadvantages:

  • Reductionism: Behavior rarely exists in isolation; controlling too many variables ignores other factors (e.g., aggression is not just social learning but also genetics; Bandura Study).

  • Low Ecological Validity: High control often creates artificial environments, making results less applicable to real life and increasing demand characteristics (e.g., Dement and Kleitman Laboratory experiments).

  • Participant Suspicion: Conducting many trials to control for participant variables (like in repeated measures designs) can make participants suspicious.

Aims and Hypotheses

• Aim: States the purpose of the study. It is written beforehand and does not predict an outcome. Example: "To determine the effect of classical music on intelligence."

• Hypothesis: A testable statement predicting the outcome, including the difference between levels of the $IV$ or the relationship between variables. Example: "Babies who listen to Mozart classical music will have higher IQ test scores than babies who do not listen to Mozart."

• Forms of Hypotheses:

  • Null Hypothesis ($H_0$): States there is no relationship between variables and that any observed results are due to chance. Example: "There will be no IQ score difference between babies who listen to classical music and those who do not."

  • Alternative Hypothesis ($H_1$): States there is a significant relationship/effect.

Types of Alternative Hypotheses

• Directional (One-Tailed): Predicts the specific direction of the results (used when results are predictable).

  • Example: "Babies who listen to classical music have higher IQ scores than babies who do not."

  • Example: "Smoking causes cancer."

• Non-directional (Two-Tailed): Predicts a difference exists but does not specify the direction (used when results are unpredictable).

  • Example: "There will be a significant IQ score difference between babies who listen to classical music and those who do not."

  • Example: A coin flip prediction: it could land on either heads or tails.

Class Exercise: Naomi's Library Observation

• Scenario: Naomi is observing helping behavior at a library entrance with a heavy door. She records the gender of people who help those carrying many books.

• Question 1: State what is meant by a situational variable.

  • Answer: Something about the environment that affects the $DV$ other than the $IV$.

• Question 2: Suggest how one situational variable could affect results.

  • Busyness: If the library is busy, people might help because they see others helping, not because of gender.

  • Time of Day: At the start of the day, people may be fresh/energetic and more likely to help. At the end of the day, they may be tired and less likely to help.

  • Weather: If it is raining, people may be less likely to stop and help to avoid getting wet.

Class Exercise: Mahi's Music and Emotions Study

• Scenario: Mahi is studying the effect of music on emotions using four categories: Angry, Bored, Happy, Relaxed.

• Operationalization Suggestions:

  • Angry: Frowning.

  • Bored: Fidgeting or doodling.

  • Happy: Smiling.

  • Relaxed: Sitting in a slouched or lazy position.

• Potential Misinterpretations:

  • Angry: A participant might frown because they misheard something, not because of the music.

  • Bored: A participant might fidget because they are in pain or itchy, or doodle because they are artistic, not because they are bored of the music.

  • Happy: A participant might smile because of a private good memory.

  • Relaxed: A participant might slouch due to bad posture or heat, rather than the music.