Stats Topic 1 Flashcards
Correlational Research
Correlational designs examine relationships between two or more variables without manipulating them. They are useful in identifying patterns and predicting outcomes but cannot establish causation.
Variable Selection
Identifying appropriate independent and dependent variables.
Operational Definitions
Clearly defining how variables are measured.q
Data Collection Methods
Surveys, observations, or archival data
Pearson’s Correlation Coefficient (r)
Measures the strength and direction of the relationship between two continuous variables.
Spearman’s Rank Correlation
Used for ordinal data or non-normally distributed data.
Chi-Square Test of Association
Examines relationships between categorical variables
Correlation ≠ Causation
A strong correlation does not imply that one variable causes the other.
Third Variable Problem
A lurking variable may be influencing both correlated variables.
Directionality Problem
It is unclear whether variable A influences B or vice versa.
Univariate Analysis
- Measures of Central Tendency: Mean, median, and mode.
- Measures of Dispersion: Range, variance, and standard deviation.
- Data Visualization: Histograms and box plots to summarize distributions.
Bivariate Analysis
- Correlation Analysis: Pearson’s or Spearman’s correlation coefficients.
- Association Tests: Chi-square test for categorical data.
- Scatterplots: Graphical representation of relationships between two continuous variables.
Statistical Assumptions
- Normality of data for parametric tests.
- Linearity in correlation analysis.
- Independence of observations.
Measurement Data (Quantitative)
Numerical values (e.g., reaction times, scores).
Categorical Data (Qualitative)
Labels or categories (e.g., gender, preference).
Methods of Data Collection
- Self-Report Surveys: Questionnaires for subjective data.
- Observational Studies: Recording behaviors in natural or controlled settings.
- Archival Data: Utilizing existing records.
Strengths and Limitations of Research Methods
Method Section Components
- Participants: Describe sample size, demographics, and recruitment methods.
- Materials: Specify tools used for data collection (e.g., surveys, physiological measures).
- Procedure: Explain step-by-step how the study was conducted.
- Analysis Plan: Describe the statistical tests used (e.g., Pearson’s r, chi-square).
Results Section Components
- Descriptive Statistics: Summarize data using means, standard deviations, or frequencies.
- Inferential Statistics: Report correlation coefficients or association tests.
- Graphs and Tables: Include scatterplots or contingency tables for visualization.
- Significance Testing: Report p-values and confidence intervals.
