Informed Consent

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Data Analysis and Reporting

Chapter 15

Data Management

  • Includes coding, cleaning, and organizing data into a usable format (preparing for analysis)

  • Coding – assigning labels so data can be read and understood by a computer (e.g., 1=yes, 2=no)

  • Cleaning – values are valid and consistent (e.g., 1=true, 2=false, there should be no 3s); Also, need to deal with missing data

Data Analysis

  • Begins with being able to identify the variables

  • Variables – a characteristic or attribute that can be measured or observed (Creswell, 2002)

  • Types of variables: independent (controlled or cause or exert some influence) and dependent (are outcome variables that are being studied)

  • Also, the level(s) of data collected are important
  • Nominal
  • Ordinal
  • Numerical (interval and ratio)

Data Analysis (cont.)

  • Descriptive statistics – used to organize, summarize and describe characteristics

  • Inferential statistics – concerned with relationships and causality to make generalizations about a population based on a sample

  • Analyses
  • Univariate (1 variable)
  • Bivariate (2 variables)
  • Multivariate (More than 2 variables)

Examples of Evaluation Questions Answered

Univariate Data Analyses

  • One variable at a time

  • Summary counts (frequency distributions)

  • Measures of central tendency – e.g., mean, median, and mode

  • Measures of spread or variation – e.g., range, standard deviation, variance

Bivariate Analyses

  • Can be non-statistical comparisons
  • Example of non-statistical comparisons (eyeballing the data)

Male Female

Yes 35 62

No 50 46

Bivariate Analyses (cont.)

  • Hypotheses
  • Null: statement of no significant difference
  • Type I error – rejecting the null hypothesis when it is true
  • Type II error – failing to reject the null hypothesis when it is not true (accepting a false null hypothesis)
  • Level of significance (alpha level) – probability of making a type I error; e.g., p<.01
  • Alternative: opposite of the null

  • Statistical significance – “refers to whether the observed differences between the two or more groups are real or not, or whether they are chance occurrences” (McDermott & Sarvela, 1999, p. 300)

  • Practical significance – measures the meaningfulness of the program regardless of statistical significance

Bivariate Analyses (cont.)

  • Statistical tests
  • Chi-square (nominal/ordinal data)

  • t-test (numerical)
  • Dependent (one group twice)
  • Independent (two groups once)

  • ANOVA (numerical) - means of ≥ 2 groups

  • Correlations (numerical) - strength of relationship)

Correlations

positive

negative

greater

Multivariate Data Analyses

  • Analyses to study three or more variables

  • Multiple regression - predicting by using several variables (numerical)
  • Stepwise
  • Logistic
  • General linear

Application of Data Analyses

  • Case #1

Application of Data Analyses (cont.)

  • Case #2

Interpreting the Data

  • Have objectives been achieved?
  • Laws, ideals, regulations, or ethical principles violated?
  • Assessed needs reduced?
  • What was the value of accomplishments?
  • Ask others to provide judgment on failures, strengths, weaknesses
  • Compare results to other results
  • Compare results to expectations of performance or standards
  • Interpret in light of evaluation procedures uses

Evaluation Reporting

  • Evaluation report is presented to the stakeholders

  • Evaluation report is essential and can provide the following:
  • A critical analysis of the results
  • A tangible product
  • Evidence that program or materials were carefully developed
  • A record of the activities
  • Assistance to others who may be interested in developing a similar program
  • A foundation for evaluation activities in the future

Designing the Written Report

  • Abstract or Executive Summary

  • Introduction - program description; goals and objectives

  • Methods/Procedures

  • Results

  • Conclusions/Recommendation

Presenting Data

  • Tables and graphs can: 1) help illustrate certain findings, 2)make reports easier to read, and 3) understand findings, be self-explanatory

  • Use graphic displays that are appropriate for the results
  • Horizontal bar charts – focus attention on different categories
  • Vertical bar charts – focus attention to change over time
  • Cluster bar charts – to contrast one variable from others
  • Line graphs – to plot data and show trend data
  • Pie charts – to show distribution

How and When to Present the Report

  • Planners/evaluators must consider the logistics of presenting the evaluation findings; should be discussed with decision makers

  • A few suggestions:
  • Give key decision makers advanced information of the findings

  • Maintain anonymity of individuals, institutions, and organizations; use sensitivity to avoid judging or labeling; maintain confidentiality according to wishes of administrators; maintain objectivity (Windsor et al., 2004)

  • Choose ways of reporting that meet the needs of stakeholders

Increasing the Utilization of the Results

  • Plan the study with stakeholders in mind
  • If the program changes after the planning stage, so should the evaluation
  • Focus the evaluation on conditions about the program that the decision makers can change
  • Reports should be written in a clear, simple manner
  • The decision on making recommendations should be based on how clear the data are
  • Disseminate the results to all stakeholders using a variety of methods
  • Integrate the evaluation findings with other research and evaluation about the program
  • Provide high-quality research