2  NURS 915 & 916: Applied Statistics 1 & 2

This “chapter” contains links to the presentations and materials covered in the Applied Statistics courses of the curriculum, NURS 915 and 916.

2.1 Review of Some Major Principles & Practices in Biostatistics

  • Variability & Randomness
  • Levels of Measurement
  • Descriptive & Inferential Statistics
  • Sources of Variance and the Signal-to-Noise Ratio
  • Designing and Answering Questions
  • Hypothesis Testing

Recording of Lecture

2.2 Presenting Results

  • Visualization
    • Self Sufficiency
    • Efficient Information Transfer
    • Data-to-Ink Ratio
    • Follow Conventions & Readers’ Expectations

2.2.1 Writing Results

  • Writing Results Sections
    • Tell a Story
    • Use Figures & Tables as Talking Points
    • Use Statistcs as Citations to Support Assertions

Additional information and resources are given in Chapter 3.

2.3 Introduction to Linear Models

  • Review of Underlying Concepts in Inferential Statistics
    • Additional information and resources are given in Chapter 5
  • Understanding Linear Models
    • Basic Concepts
    • Linear Models Vs. Correlations
    • Understanding the Linear Equation
    • Linear Models Vs. ANOVAs
  • Terms in Linear Models
    • Adding Terms to Models
    • Signal-to-Noise in Linear Models
    • Generalized Linear Models
  • An Example
  • Further Considerations
    • Multicollinearity
    • Independence of Cases

Recording of Lecture - A Zoom recording from a previous semester

PDF of Lecture

DOCX of Lecture

Additional information and resources—including steps to conducting them in SPSS—are given in Chapters 8 and 9

2.4 Testing Models Theoretically

  • Review of Linear Regression Model
  • Partialling out Variance
  • Combining Similar Sources of Variance
  • Ostensible & Non-Ostensible Variables
  • Model Fit

Recording 1 - A Zoom recording from a previous semester, this recording contains a review of linear models and introduction to tests of model fits.

Recording 2 - An other Zoom recording from a prior semester, this covers an explanation of ANOVAs and their qualities vs. general linear models

2.5 Analyses of Longitudinal Data

  • Longitudinal analyses, including some of their benefits and challenges
  • A brief comparison of the merits of pre-post difference scores, including pretest covariates in ANCOVAs, and repeated-measures ANOVAs.
  • An introduction to the sorts of multilevel models of change that Singer & Willett (2003) describe

Recording

Additional information and resources—including steps to conducting them in SPSS—are all currently located in Chapter 10.

2.6 Logistic Regresssion

  • Logistic regression vs. general linear regression
  • Explanation of the math
  • Testing effects & model fit
  • Types of logistic regression
  • Examples

2.7 Structural Equation Modeling

Structural equation models can be considered a sort of bridge between generalized linear regression and the factor analyses we’ll cover in NURS 925, Psychometrics.

  • Core Concepts
  • Mechanics of SEMs
  • Comparing Models
  • Example of SEMs