Reading and Evaluating Scientific Research (LO)

Question 1

Identify the qualities of good scientific research

Answer 1

1. Objectivity: Unbiased and impartial results.
   2.Reliability: Consistency in findings when research is repeated.
2. Validity: The extent to which the research measures what it claims to.
3. Replicability: The ability for others to replicate the study with similar results.
4. Transparency: Clear reporting of methods and findings.
5. Ethics: Research conducted in accordance with ethical guidelines.

Question 2

Identify examples of over-generalization

Answer 2

• Drawing broad conclusions from a small, specific sample.
• Example: Conducting a study on a group of college students and claiming the findings apply to all age groups.
• Another example: Observing one person from a particular culture and assuming everyone in that culture behaves the same way.

Question 3

Differentiate different forms of reliability

Answer 3

• Test-Retest Reliability: Consistency of results when the same test is repeated on the same sample after some time.
• Inter-Rater Reliability: The degree to which different observers or raters agree on their observations.
• Internal Consistency: The consistency of results across items within a test (e.g., using Cronbach’s alpha).
• Parallel-Forms Reliability: Consistency between different versions of the same test designed to measure the same construct.

Question 4

Differentiate reliability from validity

Answer 4

• Reliability: Consistency of a measure—whether it produces stable results over time or across observers.
• Validity: Accuracy of a measure—whether it actually measures what it is intended to measure.
• Example: A bathroom scale might consistently give the same weight (reliable), but if it’s calibrated incorrectly, it won’t give the true weight (invalid).

Question 5

Identify and differentiate examples of bias in research

Answer 5

• Sampling Bias: When the sample is not representative of the population (e.g., only studying wealthy individuals to represent a general population).
• Confirmation Bias: When researchers favor information that supports their preconceptions.
• Observer Bias: When the researcher’s expectations influence their interpretation of the data.
• Publication Bias: When studies with positive or significant findings are more likely to be published than studies with null results.

Question 6

Identify and differentiate techniques that reduce bias

Answer 6

1. Anonymity
2. Confidentiality
3. Inform Participants
4. Single-blind Study
5. Double-blind study

Question 7

Identify how the publication process facilitates good science

Answer 7

• Peer Review: Ensures research is critically evaluated by experts before publication.
• Replication Requirements: Publishing clear methods allows others to replicate and verify results.

Question 8

Differentiate between strong and weak forms of evidence

Answer 8

• Strong Evidence: Based on well-designed experiments, large sample sizes, and is replicable (e.g., randomized controlled trials, meta-analyses).
• Weak Evidence: Based on anecdotal accounts, small sample sizes, or poorly controlled studies.
• Example: A well-designed clinical trial is stronger evidence than a single case report.

Question 9

Identify and differentiate types of descriptive research

Answer 9

• Case Studies: In-depth analysis of a single individual or group.
• Naturalistic Observation: Observing behavior in its natural setting without interference.
• Surveys/Questionnaires: Collecting data from a large group of people to describe attitudes or behaviors.
• Archival Research: Analyzing existing data sets or records.
• Example: Survey research can describe political attitudes in a population, but doesn’t explain why people hold those attitudes.

Question 10

Differentiate between strong and weak correlations

Answer 10

• Strong Correlation: When two variables have a high degree of association (closer to +1 or -1).
• Weak Correlation: When the association between two variables is low (closer to 0).
• Example: A correlation of +0.8 (strong positive) vs. a correlation of +0.2 (weak positive).

Question 11

Differentiate between positive and negative correlations

Answer 11

• Positive Correlation: As one variable increases, the other also increases (e.g., height and weight).
• Negative Correlation: As one variable increases, the other decreases (e.g., stress and immune function).
• Example: Hours studied and exam scores are positively correlated, while exercise frequency and body weight may show a negative correlation.

Question 12

Identify the problems with inferring causation from correlation

Answer 12

• Correlation does not imply causation: Just because two variables are correlated doesn’t mean one causes the other.
• Third Variable Problem: A third factor may influence both correlated variables (e.g., ice cream sales and drowning incidents both increase in summer due to hot weather).
• Directionality Problem: It can be unclear which variable is influencing the other.

Question 13

Generalize and apply understanding of correlations to one’s life

Answer 13

• Recognize that correlations in daily life (e.g., exercise and mood improvement) don’t always mean one causes the other.
• Example: If you notice that getting more sleep is correlated with better concentration, you can’t be certain that sleep causes better concentration—other factors, like overall health, may be involved. However, improving sleep may still be a good strategy to test and potentially enhance focus.

Question 14

Identify the qualities of experimental research

Answer 14

• Manipulation: The researcher manipulates one or more independent variables.
• Control: Control over extraneous variables to avoid confounding factors.
• Random Assignment: Participants are randomly assigned to different groups to avoid selection bias.
• Replication: The experiment can be repeated to verify results.
• Causal Inference: The ability to infer cause-and-effect relationships due to the controlled conditions.

Question 15

Identify and differentiate among independent, dependent and confounding variables

Answer 15

• Independent Variable (IV): The variable that is manipulated by the researcher to observe its effect (e.g., the type of treatment).
• Dependent Variable (DV): The variable that is measured to see if it is affected by changes in the IV (e.g., response to treatment).
• Confounding Variable: An uncontrolled variable that could influence the DV, potentially skewing results (e.g., participants’ prior knowledge or experience).

Question 16

Generalize and apply learning to novel examples of experimental research

Answer 16

Example: Suppose a researcher wants to test the effect of sleep on memory. The IV would be the amount of sleep participants get (e.g., 4 hours vs. 8 hours), the DV would be their performance on a memory test, and potential confounding variables could be participants’ stress levels or caffeine intake.

Question 17

Identify and differentiate between- and within- subjects designs

Answer 17

• Between-Subjects Design: Different participants are assigned to different groups, each experiencing only one condition (e.g., one group gets Treatment A, another gets Treatment B).
• Within-Subjects Design: The same participants experience all conditions, allowing the researcher to compare their performance across conditions (e.g., each participant tries both Treatment A and Treatment B).

Question 18

Identify the pros and cons associated with between- and within- subjects designs

Answer 18

• Between-Subjects Design:
  
  • Pros: Avoids carryover effects (e.g., practice or fatigue) and is simpler to implement.
  • Cons: Requires more participants and introduces potential for individual differences between groups.
• Within-Subjects Design:
  
  • Pros: Requires fewer participants and reduces individual differences since the same participants are tested in all conditions.
  • Cons: May have carryover effects, where experiencing one condition influences performance in another.

Question 19

Identify and differentiate between experimental and quasi-experimental research

Answer 19

• Experimental Research: Involves random assignment of participants to groups, which allows for stronger control over variables and causal inference.
• Quasi-Experimental Research: Lacks random assignment (e.g., participants are grouped based on pre-existing characteristics like gender or age), making it harder to establish cause-and-effect relationships due to potential confounding variables.

Question 20

Identify the requirements of ethical research in psychology

Answer 20

1. Informed Consent: Participants must be fully informed about the study and consent to participate.
2. Confidentiality: Participants’ data must be kept confidential.
3. Debriefing: After the study, participants must be informed about the true nature and purpose of the research.
4. Protection from Harm: Researchers must minimize physical and psychological harm.
5. Right to Withdraw: Participants can withdraw from the study at any time without penalty.
6. Ethical Review: The study must be approved by an ethics board or institutional review board (IRB).

Question 21

Identify and differentiate types of distributions

Answer 21

• Normal Distribution: A bell-shaped, symmetrical curve where most values cluster around the mean.
• Skewed Distribution: A distribution that leans to one side, either positively (right skew) or negatively (left skew).
• Bimodal Distribution: A distribution with two peaks, indicating two modes or frequent values.
• Uniform Distribution: All outcomes are equally likely, resulting in a flat distribution.

Question 22

Differentiate between measures of central tendency

Answer 22

• Mean: The average of all data points (sum of values divided by the number of values).
• Median: The middle value when the data is ordered.
• Mode: The value that appears most frequently in the data set.

Question 23

Identify when it is appropriate to use each measure of central tendency

Answer 23

• Mean: Best used with interval or ratio data that is normally distributed (e.g., average income).
• Median: Best when dealing with skewed data or when outliers are present (e.g., median house prices).
• Mode: Best when dealing with categorical data or when identifying the most common category (e.g., favorite color).

Question 24

Identify and differentiate cases of high vs low variability

Answer 24

• High Variability: The data points are spread out over a wide range, indicating less consistency (e.g., test scores range from 50 to 100).
• Low Variability: The data points are clustered closely together, indicating more consistency (e.g., test scores range from 85 to 90).

Question 25

Identify when it is appropriate to make claims of statistical significance

Answer 25

• When the p-value is below a pre-determined threshold (typically p < 0.05), meaning that the results are unlikely to have occurred by chance.
• Statistical significance indicates that there is a low probability the observed effect is due to random variation, but it doesn’t necessarily imply practical importance.

Question 26

Identify the limitations of statistical hypothesis testing

Answer 26

• p-values only indicate whether an effect exists, not the size or importance of the effect.
• Type I Error: Finding a statistically significant result when there is no real effect (false positive).
• Type II Error: Failing to detect a significant effect when one actually exists (false negative).
• Hypothesis testing is sensitive to sample size; large samples can yield statistically significant results with small, practically insignificant effects.

Question 27

Differentiate between p-values and effect size

Answer 27

• p-value: Indicates the probability that the observed data would occur by chance if the null hypothesis is true. A lower p-value suggests that the findings are less likely to be due to random chance.
• Effect Size: Measures the magnitude or strength of the relationship between variables, providing information on the practical significance of the findings (e.g., Cohen’s d for the size of the difference between groups).