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Mixed Design ANOVA A mixed design ANOVA is a statistical technique used to analyze data when two or more independent variables are involved, one of which is categorical (between-subjects) and the other is continuous (within-subjects). Characteristics:  Two or more independent variables:      Between-subjects variable: Categorical variable with discrete levels.      Within-subjects variable: Continuous variable with multiple measurements for each participant.  Dependent variable: Measures the outcome or response of interest. Types of Mixed Design ANOVAs:  Simple: Two-way mixed design with one between-subjects variable and one within-subjects variable.  Factorial: Complex mixed design with multiple between-subjects variables and multiple within-subjects variables. Assumptions:  Independence: Data points are independent of each other.  Normality: Dependent variable is normally distributed within each cell.  Homogeneity of va...

 Qualitative vs Quantitative Research Method  null Qualitative Research Method:  Goal: To provide in-depth insights and understanding of phenomena.  Focus: On non-numerical data, such as observations, interviews, and focus groups.  Sample size: Typically small, often 20-50 participants.  Data analysis: Interpretive and subjective, involving the researcher's own perspectives.  Advantages:      Provides rich and detailed information.      Facilitates the exploration of complex phenomena.      Can reveal underlying patterns and insights.  Disadvantages:      Subjective and prone to researcher bias.      Findings may not be generalizable to a larger population.      Time-consuming and labor-intensive. Quantitative Research Method  Goal: To measure and quantify phenomena.  Focus: On numerical data, such as surveys, experiments, and data analysis.  ...

  Inductive vs  Deductive research method  null Inductive Research Method:  Purpose: To generate theories or hypotheses from observations.  Process:      Collect specific observations.      Identify patterns and generalizations in the observations.      Formulate a theory or hypothesis that explains the patterns.  Characteristics:      Data-driven      Focuses on detailed observations      Aims to identify broader patterns or principles Examples:  Studying a sample of patients to identify common characteristics of a particular disease.  Observing the behavior of animals to develop theories about species interactions. Deductive Research Method:  Purpose: To test existing theories or hypotheses through observations.  Process:      Start with a theory or hypothesis.      Formulate predictions based on the theory.   ...

 One-Way MANCOVA (Multivariate Analysis of Covariance) Purpose: To compare the means of multiple dependent variables across multiple independent groups, while controlling for the effect of one or more covariates. Assumptions:  Normality of dependent variables  Homogeneity of variances and covariances  Linearity of relationships between dependent variables and covariates  Independence of observations Procedure: 1. Compute unadjusted group means: Calculate the mean values of the dependent variables for each independent group. 2. Test for homogeneity of variances and covariances: Perform a Box's M test or a Levene's test to check if the variances and covariances are equal across groups. 3. Test for linearity: Plot the residuals against the predicted values to assess whether the relationship between the dependent variables and the covariates is linear. 4. Test for independence: Check that the observations are independent and not clustered within groups. 5. Run the o...