Writing and Publishing Your Thesis, Dissertation & Research:A Guide for Students in the Helping Professions by Paul Heppner and Mary J. Heppner Chapter 12: Conducting Quantitative Analyses and Presenting Your Results- Where To Begin: The Chapter Road Map
- Chapter Preliminary Components of the Results Chapter
**Data Screening**- (1) Check the accuracy of the data entry
- (2) Check missing data
- (3) Check outliers
- (4) Examine the normality of distributions [
**Skewness**involves the symmetry of the distribution;**Kurtosis**refers to the peakedness of the distribution and has three general types of curves (a. platykurtic (relatively flat), leptokurtic, and mesokurtic (relatively peaked)]. - (5) Determine the appropriateness of the data transformations.
**Preliminary Analysis**- (1) Check if scores on the dependent variable(s) are different across demographic variables
- (2) Assess the reliability coefficients and/or factor structure of the measures [Internal consistency, reliability, factor intercorrelations]
- (3) Analyze and report intercorrelations among variables [Multicollinearity refers to the potential adverse effects of correlated independent variables on the estimation of regression statistics; Zero-order correlations between the PSI (Problem-Solving Inventory) and NA (Negative Affectivity)]
**Descriptive Analysis**(mean and standard deviation)
- Writing Statistical Analyses to Describe the Results
**Chi-Square x**2**
Used when the research question is aimed at examining the frequency of a certain categorical or discontinuous variable (e.g., sex, race) or, more technically, the extent in which an observed or actual frequency count differs from the expected frequency count - Writing the Results Using Chi-Square
**t Tests t**
Used when a research wants to compare the mean differences on a dependent variable [which should be a continuous variable (e.g., reading scores)] between two groups [i.e., the independent variable, which should be a discrete (or categorical) variable (e.g., sex)]. - Writing the Results Using t Tests
**Analysis of Variance (ANOVA)**
Used when a researcher wants to examine the mean differences of two or more levels of an independent variable on one dependent variable. __One-Way ANOVA__(examine mean differences across multiple levels of one independent variable on one dependent variable)- Writing the Results Using One-Way ANOVA
__Two-Way ANOVA__(examine mean differences across multiple levels of two independent variable on one dependent variable)__Analysis of Covariance (ANCOVA)__- Used when a researcher wants to control for the effects of a third variable that potentially is confounded with the effect of independent variable(s).
- Writing the Results Using Two-Way ANCOVA
__Repeated Measures ANOVA__- Writing the Results Using Repeated Measures ANOVA
**Three Types of Multiple Regression**__Simultaneous Regression__- Used when a researcher is interested in examining the unique effects of each predictor on a criterion at the same time.
- Writing the Results Using Simultaneous Regression
__Stepwise Regression__- Used to identify which predictors account for the most variance, as well as the significance level for each predictor.
- Writing the Results Using the Stepwise Regression
__Hierarchical Regression__- Used when a researched is interested in the effects of the specific order of the predictors as determined by a priori theory or hypotheses.
- Writing the Results Using the Hierarchical Regression
- Testing Moderating Effect in a Hierarchical Multiple Regression
- Writing the Results of Testing a Moderator in a Hierarchical Multiple Regression
**Multivariate Analysis of Variance (MANOVA)**- Used when a researched wants to compare the mean differences among two or more groups on multiple dependent variables simultaneously.
- Writing the Results Using a MANOVA
**Cluster Analysis**- Used when a researcher is interested in grouping members (e.g., people or objects) on the basis of their common characteristics.
- Writing the Results Using Cluster Analysis
**Factor Analysis**__Exploratory Factor Analysis__(internal consistency, convergent validity)- Writing the Results Using Exploratory Factor Analysis
__Confirmatory Factor Analysis__(reliability, concurrent validity)- Writing the Results Using Confirmatory Factor Analysis
**Structural Equation Modeling (SEM)**- Used when a researcher wants to test the plausibility of a theory or a model about the predictive relationships among variables (e.g., variable A->B->C; note arrow represents hypothesized predictive effects)
- Writing the Results Using Structural Equation
__Modeling__(SEM)
Chapter 13: Qualitative Results: The Meaning-Making Process- Grounded Theory
- Consensual Qualitative Research
- Phenomenology/Hermeneutics
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