Spring 2026 SOWK 460w Week 13 - Interpreting Findings Analyzing and Understanding Data for Program Evaluation

Spring 2026 SOWK 460w Week 13 - Interpreting Findings Analyzing and Understanding Data for Program Evaluation

title: Spring 2026 SOWK 460w Week 13 - Interpreting Findings Analyzing and Understanding Data for Program Evaluation date: 2026-04-13 14:22:26 location: Heritage University tags:

• Heritage University
• BASW Program
• SOWK 460w presentation_video: > “” description: >

Week 13 is synchronous, with class on Monday (4/13). Students read Royse (2023) Chapter 12, which covers understanding statistical data, finding patterns, and analyzing outcome data. This week we turn our attention to data analysis and interpretation. During class, students will apply these concepts by analyzing real survey data and working in small groups to interpret and present findings. The agenda for the in-person class includes:

• What is the purpose of data analysis
• Scales of measurement
• Types of calculation
• Practical application of interpreting findings
• How we implement it for program evaluation

Learning Objectives this week

• Describe methods used to understand and interpret data.
• Apply principles of effective data visualization to represent data.
• Create charts using real data to practice presenting information.

The Blind Men and the Elephant (1 of 2)

I want to start off our discussion regarding data telling about a story you might have heard before, that of the blind men and an elephant which originated in the Indian subcontinent…

[Whole Group Activity] Listen to “The Blind Men and the Elephant” by John G. Saxe (Read by Tom O’Bedlam)

• One touched the tusk and said this feels like a spear
• One touched the trunk and said this feels like a snake
• One touched the ears and said this feels like a fan
• One touched it’s legs and thought that it was like tree trunks
• One touched it’s torso and thought it was like a brick wall
• One touched it’s tail and thought it was like a rope

The Blind Men and the Elephant (2 of 2)

Understanding data from a program evaluation is complicated and can be really challenging. In many ways it can be a bit like the blind men touching the elephant.

Week 13 Plan

Agenda

• What is the purpose of data analysis
• Scales of measurement
• Types of calculation
• Practical application of interpreting findings
• How we implement it for program evaluation

Learning Objectives

• Describe methods used to understand and interpret data.
• Apply principles of effective data visualization to represent data.
• Create charts using real data to practice presenting information.

Purpose of Data Analysis

The purpose of analyzing data is to obtain usable and useful information. The analysis, irrespective of whether the data is qualitative or quantitative, may:

• Describe and summarize the data
• Identify relationships between variables
• Compare variables
• Identify the difference between variables
• Forecast outcomes

Scales of Measurement

Many people are confused about what type of analysis to use on a set of data and the relevant forms of pictorial presentation or data display. The decision is based on the scale of measurement of the data. These scales are nominal, ordinal and numerical.

• Nominal scale: the data can be classified into a non-numerical or named categories, and the order in which these categories can be written or asked is arbitrary.
• Ordinal scale: the data can be classified into non-numerical or named categories an inherent order exists among the response categories. Ordinal scales are seen in questions that call for ratings of quality (for example, very good, good, fair, poor, very poor) and agreement (for example, strongly agree, agree, disagree, strongly disagree).
• Numerical scale: where numbers represent the possible response categories there is a natural ranking of the categories zero on the scale has meaning there is a quantifiable difference within categories and between consecutive categories.

Types of Calculation

There are a number of different analysis methods we use for data analysis. These include:

Univariate Statistics

• Count (frequencies, N/n)
• Percentage
• Range
• Mean (average)
• Median (middle number)
• Mode (number of times)
• Standard deviation (amount of change)

Bivariate Analysis

• Coefficient of correlation (strength and direction of relationship)
  • coefficient of determination (r^2) when coefficient is multiplied by self
  • Inverse relationship (one goes up the other goes down)
  • Continuous variables (AKA interval or ratio level variables) like age, weight, etc. and use Pearson correlation
  • Ordinal data (ranked position)
  • Spearman’s rho and Kendals tau-b used for correlation of ordinal data
• Statistical significance (likelihood result is not chance)
  • p > .05 not significant
  • p < .01 significant
  • p < .001 highly significant
• t-test (practical way of comparisons between two groups).
  • Can be paired samples t-test
  • or independent samples t-test
• ANOVA (One-way Analysis of Variance - to compare means across groups) A statistical test used to determine whether there are significant differences between the means of three or more independent groups.
• Effect Size (Magnitude of difference) - A measure that describes the magnitude of the difference or relationship between variables, independent of sample size.

Multiple Regression (predict outcome from variables) - A statistical technique that predicts the value of a dependent variable based on two or more independent variables. Factor Analysis (identify variable groupings) - A method used to identify underlying relationships between variables by grouping them into factors based on correlations.

Analysis of Categorical (Nominal) Variables- Statistical methods used to examine relationships between variables that represent categories rather than numerical values.

• Chi-Square Analysis (test association between categories) - A statistical test used to determine if there is a significant association between two categorical variables.
• Cross tabulation (compare category frequencies) - A table that displays the frequency distribution of variables to explore relationships between two or more categorical variables.
• Logistical regression (predict binary outcomes) - A regression analysis method used to predict the probability of a binary outcome based on one or more independent variables.
• Odds ratio (compare likelihoods between groups) - A statistic that quantifies the strength of association between two events, representing the odds of an outcome occurring in one group compared to another.

(Royse, 2023)

Types of Triangulation

increasing confidence in research data, creating innovative ways of understanding a phenomenon, revealing unique findings, challenging or integrating theories, and providing a clearer understanding of the problem. (p 254)

Data Source Time, space, and person Investigator Multiple researchers Methodological Using multi-methods in investigation Theoretical Using multiple theories or hypotheses Data-Analysis Two or more methods of analyzing data

Principles of Effective Data Visualization (1 of 3)

Midway (2020) provide some good recommendations for developing effective data visualization.

In the design phase we should be:

• Diagram first, focus on message: before you make a visual, prioritize the information you want to share, envision it, and design it
• Adopt the best software for your needs: Might mean learning new tools or partnering with people who have expertise

As we move onto making the figure, consider:

• Use the correct geometry; consider showing the data: Bar charts, areas, logistic regression all show different things. Pick the one that is going to clearly display your information.

Principles of Effective Data Visualization (2 of 3)

There are a number of different types of graphical representations we might use. They all showcase different information about the data you are displaying.

Some include:

• Dot Plot: Shows individual data points.
• Boxplot: Summarizes spread and outliers.
• Violin Plot: Combines spread and density.
• Bar Chart: Displays group sizes with bars.
• Histogram: Groups continuous data into bins.
• Scatter Plot: Plots points to find patterns.
• Line Graph: Connects data points in sequence.
• Heatmap: Colors show intensity across a grid.
• Map (Geospatial Plot): Plots data geographically.
• Network Diagram: Maps links between entities.

A → Bar Chart — Displays group sizes with bars (grouped by category A, B, C).

Consider:

Principles of Effective Data Visualization (3 of 3)

Other aspects of making the figure we should consider include:

• Use an effective color scheme: Use color, but make it so can be printed grey scale if necessary.
• Include any relevant metric of uncertainty: Uncertainty is often not included in figures and, therefore, part of the statistical message is left out
• Use small multiples (if appropriate): Think about this as boxes showing changes to the diagram. Makes it really clear what is happening.
• Distinguish models from data: Make it clear what you are saying.
• Include a detailed, standalone caption: Should describe the figure.

As well we need to review the figure by

• Consider an infographic: Although it is not recommended to convert all figures to infographics, info- graphics were found20 to have the highest memorability score and that diagrams outperformed points, bars, lines, and tables in terms of memorability
• Get an independent figure review: Test, test test.

Figures are not just a scientific side dish but can be a critical point along the scientific process

Presentation Planning

• Flyer Agreement
• Light refreshments

Making Interpreting Findings Practical (1 of 2)

[Whole Group Activity] Use the survey created week six and have each individual take the survey.

Find the Survey at 2026 Spring SOWK 460w BASW Quality Assurance Survey

Making Interpreting Findings Practical (2 of 2)

[Small Group Activity] As small groups, come up with how you would want to present some of the data collected to your peers. What are some of the insights you found?

Google Doc2026 Spring SOWK 460w BASW Quality Assurance Survey - Interpretation Report

So where do we go from here?

• What kind of data have you collected
• How are you analyzing it
• Technical support