LEt's LEARN: QUANTITATIVE AND QUALITATIVE APPROACH TO RESEARCH PROJECT

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

x**2 (a, N=b) = c, p<d
 

• 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