Module 3: Intro to Data Concepts

Module 3

Introduction to Data Concepts

Start

Special Acknowledgement

Section 1: Basic Data Concepts

Section 2: Qualitative Data

AGENDA

Section 3: Quantitative and Mixed Data

Objectives

Section 1: Basic Data Concepts

By the end of this section, you will be able to:

Describe the types of data utilized in healthcare and public health

Define Informatics

Describe the DIKW Pyramid

Name the steps in the Information Value Cycle (IVC Model)

OBJECTIVES

Differentiate the various levels of measurement

Define common data terminology

Registries & Surveillance

Clinical & Genomic Data

HealthData Resources

Social-Environmental (SDOH)

Claims & Billing

Behavioral & Survey Data

Geospatial Data

Informatics

“The science of how to use data, information, and knowledge (to improve human health and the delivery of healthcare services.)”

American Medical Informatics Association (AMIA)

Informatics Disciplines

Public Health Informatics

Medical Informatics

• Patient Care
• EHRs, Lab, Prescriptions
• Enhance Clinical decisions and quality care
• Telemedicine, e-prescribing Images

• Community Health
• Registries, Census
• PH Officials
• GIS, Big Data
• Improve PH Policy and Programs

American Medical Informatics Association (AMIA)

https://www.youtube.com/c/AMIAInformaticshttps://www.youtube.com/watch?v=Zy22Uf4NnC8

Information Value Cycle (IVC)

Is Data Value Created?

1. Plan: Data Governance
2. Capture: Data Standards & Sources
3. Manage: Organize, Store, Secure
4. Analyze: Statistical Methods
5. Use: Program, Policy & Decisions
6. Evaluate: Ongoing Improvement

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Use this side of the card to provide more information about a topic. Focus on one concept. Make learning and communication more efficient.

Use this side of the card to provide more information about a topic. Focus on one concept. Make learning and communication more efficient.

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CAPTURE

PLAN

EVALUATE

Title

USE

ANALYZE

MANAGE

Write a brief description here

Data Information Knowledge Wisdom (DIKW) Pyramid

Wisdom

Knowledge

Information

Data

Data: Levels of Measurement

Nominal (Named) Mode, Freq, Counts, Percentage

Ordinal (Ordered) + Median, Min/Max, range, Percentiles, Likert

Ratio (Named, Ordered, Proportionate Intervals, plus Absolute Zero) + All prior methods, advance analytics

Interval (Proportionate intervals) + mean, variance, SD

Name the Level of Measurement for each Maternal Health Example:

Maternal Boby Temperature in Celsius Degree during labor?

Type of delivery method (vaginal, c-section, assisted)?

Birth weight of the infant measured in grams?

Trimester of Pregnancy (first, second, third)?

Blood type of the mother (A, B, AB, O)?

Rating maternal pain level scale 1-10 in labor?

Mother’s age at delivery in years?

Gestational age at Delivery in weeks?

Maternal hemoglobin level (g/dL)?

Continuous (Any Value)

Types of Variables

Discrete (Fixed Variable)

CONCEPT MAP

Categorical (Category/Groups)

Roles of Variables

Dependent (The Outcome)

Data Terminology

Data Quality

Descriptive Statistics

Data Distribution

• Mean
• Median
• Mode
• Standard
• Deviation

• Outlier
• Missing
• Data

• Normal
• Distribution
• Skewed
• Kurtosis

Mean= Sum All Observations/N Ex: 2+4+3/3=3 Median= Order data, total N, Middle Ex: 5, 11, 88, 91, 100 Median=88 If even number, average middle numbers Mode: Most Frequency Observation Ex: 2, 7, 2, 9, 8, 2, 8, 2 Mode=2 SD: Spread of the Data

Knowledge Check

Knowledge Check

Match the Information Value Cycle Step to its function:

A. Data Standards B. Governance C. Data Security D. Data Visualization E. Overall Improvement

1. Plan
2. Capture
3. Manage
4. Analyze
5. Evaluate

Objectives

Section 2: Qualitative Data

By the end of this section, you will be able to:

Name the what, how and why of Qualitative Data

Describe the strengths and weaknesses of Qualitative Data

Compare Inductive to Deductive Qualitative Coding Methods

OBJECTIVES

List available software available to analyze Qualitative data

First, Problem (or) Hypothesis Then, Methodology (Data Collection)

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Research & Evaluation: Data

Qualitive Data

Title

Mixed Data

Quantative Data

Write a brief description here

What is Qualitative Data

“Non-numeric data that captures meaning, experience, or perspective” (Patton, 2015) Emphasis on “Depth” over Breadth

Words

Video

Images

Observations

Data Forms:

How is Qualitative Data Collected?

Focus Groups

Document or Media Analysis

Open-ended Survey Questions

In-depth Interviews

Observations

Why use Qualitative Data?

Context

Meaning

Lived Experience

Not easily measured

New Field of Study

Allow Voices to be heard, Quotes

Inductive vs. Deductive Data Coding

Inductive Themes emerge from the data

Deductive Uses a predefined framework or theory to guide coding

Example Inductive vs. Deductive Data Coding

What are the lived experiences and challenges faced by mothers during the postpartum period?

Inductively Data Coding: coding of themes that emerge

Deductive Data Coding: coding the data to an existing framework, such as WHO’s Social Determinants of Health of Maternal Health

Qualitative Data​

Qualitative: Data Analysis Techniques

Strengths

• Rich Information
• Detailed Understanding about a topic
• Supports Exploratory and Formative Studies
• Triangulates Quantitative data

Thematic Analysis Grounded Theory Content Analysis Framework Analysis Narrative Analysis

Weaknesses

• Very time-consuming to collect and analyze
• Smaller Sample, limited Generalizable
• Subjective interpretation
• Potential for Bias

Qualitative Software Options

NVivo

MAXQDA

ATLAS.ti

Taguette (Open-Source)

Artificial Intelligence (AI)

Dedoose

Knowledge Check

Objectives

Section 3: Quantitative Data

By the end of this section, you will be able to:

Name the what, how and why of Quantitative Data

Describe the strengths and weaknesses of Quantitative Data

OBJECTIVES

List available software available to analyze Quantitative data

Describe Mixed Method approach

What is Quantitative Data?

“Quantitative data are numerical values used to measure and compare variables. It deals with counts, percentages, means, and statistical analysis.” Data Types: Discrete Continuous

How is it collected?

Structured Surveys, Close-ended questions

Standardized Assessments or Tests

Measurable Outcomes

Checklists

EHR Data

Administrative

Secondary Data Sources: Maternal Health

• World Health Organization (WHO)
• Demographic and Health Surveys (DHS)
• UNICEF Data Warehouse
• CDC Wonder
• National Vial Statistical System (NVSS)
• World Bank Data (Maternal Health Indicators)

Why use Quantitative Methods?

Generalizability

Statistical Comparisons

Replicability

Objectivity

Testing Hypotheses

Evaluating Interventions

Qualitative Data​

Quantitative: Data Analysis

Strengths

Descriptive Statistics -mean, median, mode, range, SD Inferential Statistics -t-tests, ANOVA, regression, chi-square Epidemiological Methods -Rates, Ratios, Proportions, 2x2 tables Other (Big Data) -Machine learning, Classifications

• Larger Sample Sizes
• Statistical Modeling and Detection
• Less prone to Researcher
• Bias Cause-effect allowed

Weaknesses

• Overlook depth and context
• Might not explain ‘why’
• Closed-end limit rich data
• Need rigorous Instrument design and validation

Quantitative Software Options

Mixed Methods

• SPSS
• STATA
• R / Rstudio
• SAS
• Excel
• Epi Info
• Artificial Intelligence (AI)

Mixed

Qualitive

Quantitive

Knowledge Check

Module 3 Summary

Introduction to Data Concepts

For this module, we:

Section 1: Overview of Data

• Data Sources and Informatics

• DIKW and IVC Models

• Data Measurement

Section 2: Qualitative Data

• Strengths and Weaknesses of Qualitative Data

• Common Data Collection methods

• Inductive vs. Deductive coding

Summary

Section 3: Quantitative Data

• Strengths and Weaknesses of Quantitative Data

• Common Data Collection methods

• Mixed Methods

Optional: deeper dive

Part 2 (24 min.)

Part 1 (30 min.)

Chief Health Strategists: How Public Health Leaders Can Be Successful Working Across the Health Landscape, Part II

Chief Health Strategists: How Public Health Leaders Can Be Successful Working Across the Health Landscape, Part I​

References

Gravetter, F. J., & Wallnau, L. B. (2020). Statistics for the Behavioral Sciences (11th ed.). Cengage. Rowley, J. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163–180. O’Carroll, P. W., Yasnoff, W. A., Ward, M. E., Ripp, L. H., & Martin, E. L. (Eds.). (2003). Public health informatics and information systems. New York, NY: Springer-Verlag. Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). SAGE Publications. Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design (4th ed.). SAGE. Braun, V. and Clarke, V (2002). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. Guest, G., MacQueen, KM and Namey, EE (20212). Applied Thematic Analysis. SAGE Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.

Kurtosis: Describes Peakedness or tailedness (Sharp Peak, Flat Peak, Normal Distribution)

Just as the field of health encompasses multiple disciplines and professions, so too does the field of data. The study of data involves both a science and an art, whereby raw information is systematically transformed into actionable knowledge and, ultimately, wisdom to guide decision-making. Informatics is a discipline with multiple definitions, and it is most commonly described as “the science of how to use data, information, and knowledge.” When applied to the health domain, this definition can be extended to emphasize its purpose: “to improve human health and the delivery of healthcare services.” As a field, informatics sits at the intersection of information science, computer science, and decision sciences, and it applies across diverse sectors, including healthcare, business, and education.

The American Medical Informatics Association, referred to as AMIA, provides informatics trainings, certifications, conferences and networking opportunities within the cross-discipline of health (both medical and public health) and computer/information science. You can visit their website to learn more. Here are two quick videos of ‘why informatics’ and the ‘voices’ of those that work in this discipline.

While the Information Value Cycle, or IVC, provides a framework for planning and evaluating an information system, and it also addresses its core components of data, technology, and users. However, the DIKW Pyramid framework emphasizes the progressive value that emerges as data are transformed through successive stages. Each layer of the pyramid represents a deeper level of interpretation and application, moving from raw inputs to actionable outcomes. At the foundation is ‘data,’ which represent the rawest and most unprocessed form of facts which includes numbers, letters, or symbols and that, in isolation, lack inherent meaning or context. When data are processed, organized, and contextualized, they become ‘information,’ enabling a clearer understanding of what the data represent. The next layer, ‘knowledge,’ is achieved when information is further interpreted through analysis, experience, or education, allowing for the recognition of patterns, relationships, and insights. At the highest level is ‘wisdom,’ which reflects the capacity to apply knowledge in practical contexts to guide judgment, decision-making, and action. This model is particularly useful in health research and practice, as it illustrates the ultimate purpose of data collection and analysis: not merely to generate statistics, but to transform raw data into knowledge and wisdom that can inform clinical practice, shape health policy, and improve patient and population outcomes. When applied to maternal health, for example, raw data on prenatal visits can be processed into information on access to care, analyzed to generate knowledge about disparities in maternal outcomes, and ultimately applied as wisdom to design policies that improve equity in prenatal and delivery services.

Skewness: Describes asymmetry (Tail to right or left)