2/25/26 notes

Literature Review Structure

Introduction
- Introduces the topic of the literature review.
- Clearly states the research question. Example phrasing: "Our research question is…"
- Contains a brief overview of the content covered in the literature review.
- Discusses the importance of the topic and its relevance to the field of counseling.
- Provides reasoning for the relevance of the research question to counseling practices.

Research Question: Must be explicitly stated and clear.
Hypothesis:
- Required for quantitative research.
- Not needed for qualitative research.

Should be well organized and well written.
Must utilize peer-reviewed literature, focusing on sources primarily within the last five years.
- Older sources can be included but should not be the foundation.
- The recommended number of primary sources: at least eight that are within the last six years.
Gap Analysis:
- Discuss whether existing literature has addressed the area of research.
- Identify significant gaps that warrant new research.

Older Theories and Studies:
- While older studies may be integral (e.g., long-standing theories), current data should dominate.
- Elaborate on theoretical constructs relevant to the counseling field.

Should provide information about the variables and their significance.
- Establishes the backbone supporting the research question.
- Can persuade the reader regarding the importance of the research topic.

Attention to APA format is paramount.
Reading Practices:
- Read and reread drafts aloud to ensure clarity and accuracy.
- Encourage peer collaboration for reviewing writing.

For those working in pairs, a half-page reflection on how work was divided must be included in the final submission.

Correlation Design:
- Explores relationships between two or more variables without manipulation or intervention.
- Useful for determining initial relationships prior to applying more in-depth statistical methods.
Regression Analysis:
- Moves a step beyond correlation by predicting the outcome of a dependent variable based on one or more independent variables.
- Two main types:
  - Simple Linear Regression: One independent variable predicting one dependent variable.
  - Multiple Regression: Several independent variables predicting one dependent variable.

Relationships defined using Pearson's r for correlation analysis and other statistical outputs for regressions.
Important outputs include correlation coefficient (
- R-squared value evaluating the amount of variance explained by the predictors.

p-value significance in hypothesis testing.
- Analyzing significance through set thresholds, typically 0.05 or lower.
- Key for determining if variables are significantly related.

Moderation vs. Mediation:
- Moderation: Specifies how the strength and direction of the relationship are influenced by another variable.
- Mediation: Explains how an independent variable affects a dependent variable via a third variable.

T-tests:
- Independent samples t-test for evaluating differences between two mean samples.
- Paired samples t-test for evaluating mean differences within the same sample at two different times.
ANOVA:
- Analyzing differences between three or more groups.

Internal Validity: Refers to the extent to which causal conclusions can be drawn.
- Common threats include maturation, history, instrumentation, selection bias, test effects, and mortality.
External Validity: Examines the generalizability of findings to broader contexts and populations.
- Threats could stem from artificiality of testing conditions or variations in selection across groups.

Addressing participant consent and confidentiality.
Importance of maintaining integrity and ethical standards in quantitative and qualitative research designs.