Research Ethics Overview

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Questions and Answers

What is a principle established by the Belmont Report?

Randomization

Data Sharing

Confidentiality

Beneficence (correct)

Which of the following best describes informed consent?

Informed consent is not needed for minimal risk research.

Participants must provide consent without any prior knowledge.

Consent from participants is optional during the study.

Participants need to be informed of procedure, risks, and benefits. (correct)

What should researchers do before deception is used in a study?

Explain deception during the pre-study briefing.

Provide a thorough debriefing afterward. (correct)

Ensure all team members are aware of the deception.

Obtain permission from participants.

What is one focus of the Tri-Council Policy Statement in Canada?

Ensuring respect for persons and justice. Signup and view all the answers

Why is replication important in scientific research?

It helps to verify the accuracy of results. Signup and view all the answers

What does scholarly integrity require from researchers regarding data?

Sharing data with others for verification. Signup and view all the answers

In planning a research study, what is the purpose of a study rationale?

To justify the necessity and relevance of the research. Signup and view all the answers

Which element is crucial in the publication process of a research study?

Adhering to APA style guidelines. Signup and view all the answers

What is the primary purpose of manipulating the independent variable in an experiment?

To test causal relationships between variables Signup and view all the answers

What distinguishes a single-factor multi-level design from a single-factor two-level design?

It manipulates the independent variable at three or more levels Signup and view all the answers

Which source of inspiration prompts applied research in real-world challenges?

Practical problems Signup and view all the answers

How can extraneous variables be controlled in an experiment?

By holding them constant or using random assignment Signup and view all the answers

What is the primary purpose of reviewing existing research literature before conducting a new study?

To find if your question has already been answered Signup and view all the answers

What is a confounding variable?

A variable that systematically differs along with the independent variable Signup and view all the answers

What is the main role of a control condition in an experiment?

To ensure participants do not receive the intervention Signup and view all the answers

What type of article summarizes previous research and often introduces new frameworks?

Review article Signup and view all the answers

Which database is primarily used for psychology research and covers over 100 years of studies?

PsycINFO Signup and view all the answers

Which design allows all participants to experience every condition of the experiment?

Within-subjects design Signup and view all the answers

Which validity type ensures the results of an experiment can be generalized to other contexts?

External validity Signup and view all the answers

What aspect should researchers evaluate to determine the interestingness of a research question?

Whether it fills gaps in the literature Signup and view all the answers

What characterizes a hypothesis in contrast to a theory?

It makes specific predictions Signup and view all the answers

What is the importance of counterbalancing in experiments?

To control for carryover effects among conditions Signup and view all the answers

What is the focus of empirically testable questions in research?

Examining single variables or their relationships Signup and view all the answers

In theory testing, what is the hypothetico-deductive method primarily concerned with?

Deriving hypotheses from theories and testing them Signup and view all the answers

What does a low p-value (p < α) indicate about the null hypothesis (H0)?

It supports the rejection of H0. Signup and view all the answers

What is alpha (α) commonly set at in hypothesis testing?

0.05 Signup and view all the answers

Which statement about Type I error is true?

It reflects a 5% risk of rejecting H0 when it is true. Signup and view all the answers

What does statistical significance imply about the results of a hypothesis test?

The results are not influenced by random chance. Signup and view all the answers

When conducting a one-sample t-test, what is the null hypothesis (H0)?

H0: μ = μ0 Signup and view all the answers

How is the degrees of freedom calculated for a one-sample t-test?

df = N - 1 Signup and view all the answers

What is the purpose of a critical value in hypothesis testing?

To determine if the test statistic is extreme enough to reject H0. Signup and view all the answers

What distinguishes a one-tailed test from a two-tailed test?

One-tailed tests examine an effect in only one direction. Signup and view all the answers

What does a significance level of α = 0.05 indicate?

A 5% chance of a Type I error Signup and view all the answers

Which of the following best defines a Type II error?

Failing to reject the null hypothesis when it is false Signup and view all the answers

What is the purpose of conducting a power calculation in research?

To determine the probability of avoiding a Type II error Signup and view all the answers

What does the File Drawer Problem imply about published research?

It skews the literature toward significant results Signup and view all the answers

What is generally considered an adequate level of statistical power?

0.80 Signup and view all the answers

Which of the following is a criticism of null hypothesis testing?

It can lead to rigid adherence to the p < 0.05 rule Signup and view all the answers

Which approach involves using effect sizes to convey the strength of relationships?

Alternative Approaches Signup and view all the answers

What does the replicability crisis refer to in research?

The challenge in reproducing previously published findings Signup and view all the answers

Study Notes

Research Ethics

Nuremberg Code (1947): Established ethical principles for research involving humans, following the atrocities of Nazi medical experiments. Key principles include voluntary consent, informed participants, and minimizing harm.
Declaration of Helsinki (1964): Expanded on the Nuremberg Code, requiring research to be based on written protocols and reviewed by independent committees.
Belmont Report (1978): Established principles of Respect for Persons, Beneficence, and Justice as the foundation for ethical human research.
Tri-Council Policy Statement (Canada): Code of ethics for research involving humans, emphasizing respect for persons, concern for welfare, and justice.

Informed Consent: Participants must be aware of the research procedure, potential risks, and benefits before participating.
Deception: Researchers should use deception sparingly and only when necessary for the research question.
Debriefing: After the study, researchers must inform participants about the true purpose of the research and minimize any harm caused by deception.

Scholarly Integrity

Data Fabrication or Plagiarism: Researchers must ensure all data is authentic and properly attributed.
Sharing Data: Data should be made available to other researchers for verification and replication purposes.
Peer Review: Maintaining confidentiality during the review process and conducting ethical reviews are crucial for scholarly integrity.

Planning a Research Study

Research Topic: Begin with a clear research question that is grounded in existing literature and real-world observations.
Study Rationale: Justify the need for your research and explain how it contributes to scientific knowledge.
Research Design: Develop a comprehensive and ethical plan, considering potential risks and obtaining necessary approvals.
Data Collection and Analysis: Adhere to the pre-registered research plan and document any deviations.
Publication: Follow APA style guidelines when publishing research and participate in the peer review process.

Replication and Open Science

Replication: Repeating studies helps confirm findings and increases confidence in research conclusions.
Open Science: Embraces sharing data and research materials to encourage collaboration and transparency in research.

Scientific Cycle

Research literature: Serves as a source of inspiration for new research and a destination to disseminate findings.
Sources of research questions:
- Informal observations: Everyday behavior, news, or books can spark research questions.
- Practical problems: Real-world challenges in fields like education or law prompt applied research.
- Previous research: Building on existing studies is a common source of new research questions.

Reviewing the Research Literature

Purpose of reviewing literature:
- Determine if a research question has already been answered.
- Gather ideas for research methodology.
- Place a study within the context of existing research.

Types of Sources

Empirical reports: Original studies with detailed methods, results, and conclusions.
Review articles: Summarize previous research on a topic, often introducing new frameworks or conducting meta-analyses.

Database for Research

PsycINFO: The primary database for psychology, encompassing over 100 years of research.
Google Scholar: A valuable tool for finding a wide range of research across disciplines.

Generating Good Research Questions

Empirically Testable Questions: Focus on single variables or relationships that can be investigated scientifically.

Evaluating Research Questions

Interestingness: Does the question fill a gap in the existing literature and have practical importance?
Feasibility: Consider available resources, time constraints, participant access, and researcher skills.

Developing a Hypothesis

Theory vs. Hypothesis:
- Theory: A broader explanation or framework.
- Hypothesis: A specific prediction derived from a theory.
Hypothetico-deductive method: Involves deriving hypotheses from theories, testing them empirically, and revising theories based on the results.

Experimentation

Experiment: A controlled study designed to investigate causal relationships between variables.
Independent Variable (IV): The variable manipulated by the researcher to observe its effects on the dependent variable.
Dependent Variable (DV): The variable measured by the researcher to assess the effects of the independent variable.
Single-factor two-level design: Only one variable is manipulated, with two conditions representing different levels of the independent variable.
Single-factor multi-level design: One variable is manipulated, but with three or more conditions.
Extraneous Variables: Unwanted variables that could influence the dependent variable.
Confounding Variables: Variables that change systematically with the independent variable, providing alternative explanations for the findings.

Experimental Design

Treatment and Control Conditions:
- Treatment condition: Participants receive the intervention or manipulation.
- Control condition: Participants do not receive the intervention or manipulation.
Random Assignment: Participants are assigned to conditions randomly to control for extraneous variables.
Block randomization: Ensures equal distribution of participants across conditions.
Matched Groups Design: Participants are matched on relevant characteristics before being randomly assigned to conditions.
Within-Subjects Design: Participants experience all conditions of the experiment, reducing variability within the data.
Carryover effects: The effect of one condition may influence performance in subsequent conditions.
Counterbalancing: Presenting conditions in different orders for different participants to control for carryover effects.

Experimentation and Validity

Four Big Validities:
- Internal Validity: The extent to which the independent variable caused changes in the dependent variable.
- External Validity: The generalizability of the findings to other people, settings, and situations.
- Construct Validity: The accuracy of the measurement of the intended concept.
- Statistical Validity: The proper use of statistical techniques to analyze the data.

Understanding p-values

Definition: The probability of observing the sample result or a more extreme result if the null hypothesis is true.
Interpreting p-values:
- Low p-value (p < α): Reject the null hypothesis; the results are statistically significant.
- High p-value (p ≥ α): Fail to reject the null hypothesis; there is insufficient evidence to conclude a relationship.

Significance Levels

Alpha (α): The threshold for determining statistical significance, typically set at 0.05.
Type I Error: Rejecting the null hypothesis when it is actually true (5% risk with α = 0.05).

Sample Size and Relationship Strength

Effects of sample size:
- Large sample sizes: Can lead to statistical significance even if the relationship is weak.
- Small sample sizes: May not detect significant relationships even if they exist.

Statistical vs. Practical Significance

Statistical significance: Indicates that the results are unlikely to have occurred by chance.
Practical significance: Refers to the real-world importance or usefulness of the findings.

Common Null Hypothesis Tests

t-Test: A widely used test for examining statistical relationships between means.
One-Sample t-Test: Compares a sample mean to a hypothetical population mean.
Degrees of Freedom: The number of independent values in a dataset that can vary.
Critical Values: Thresholds for determining if the test statistic is extreme enough to reject the null hypothesis.
One-Tailed Test: Tests for an effect in one direction (greater than or less than).
Two-Tailed Test: Tests for an effect in either direction (greater than or less than).

Type II Error

Definition: Retaining the null hypothesis when it is false.
Causes: Often due to insufficient statistical power in the research design.

File Drawer Problem

Definition: The tendency for studies with non-significant results to remain unpublished, leading to a bias toward significant findings in the published literature.
Implication: Increases the likelihood of overrepresenting Type I errors in published research.

Statistical Power

Definition: The probability of correctly rejecting the null hypothesis (avoiding Type II error).
Ways to increase power:
- Increase the effect size (strength of the relationship).
- Increase the sample size.

Criticisms of Null Hypothesis Testing

Misinterpretations:
- Confusing the p-value with the probability that the null hypothesis is true.
Logic Issues:
- Rigid adherence to p < 0.05 for significance can distort research findings.
Informational limitations:
- Rejecting the null hypothesis only indicates a non-zero relationship, but does not specify the nature of the relationship.

Alternative Approaches

Effect Size Measures: Accompany null hypothesis tests with effect sizes (e.g., Cohen's d, Pearson's r) to indicate the strength of relationships.
Confidence Intervals: Provide a range of values for the population parameter with a given confidence level.
Bayesian Statistics: Incorporates prior probabilities about hypotheses, updating these based on observed data.

Replicability Crisis

Definition: The difficulty in replicating previously published research findings.

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Description

Explore the key principles of research ethics, including the Nuremberg Code, Declaration of Helsinki, and Belmont Report. This quiz highlights the importance of informed consent and the appropriate use of deception in human research. Test your understanding of ethical standards in conducting research with human participants.