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Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Statistical Methods for Analyzing Epidemiological Data01:25

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Data Collection by Observations01:08

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Statistical Software for Data Analysis and Clinical Trials01:12

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Strategies for Assessing and Addressing Confounding01:25

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Statistical Analysis System (SAS)01:14

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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
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Related Experiment Video

Updated: Apr 25, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Leveraging Administrative Data for Program Evaluations: A Method for Linking Data Sets Without Unique Identifiers.

Andrea L Lorden1, Tiffany A Radcliff2, Luohua Jiang3

  • 1Department of Health Policy and Management, School of Public Health, Texas A&M Health Science Center, College Station, TX, USA lorden@sph.tamhsc.edu.

Evaluation & the Health Professions
|August 21, 2014
PubMed
Summary
This summary is machine-generated.

Linking community wellness program participants to Medicare data is crucial for evaluating program effectiveness. This study successfully linked 78% of participants using fuzzy matching, enabling better understanding of health care utilization changes.

Keywords:
Medicareadministrative datafuzzy matchinglinkageprogram evaluation

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Area of Science:

  • Public Health
  • Health Services Research
  • Gerontology

Background:

  • Community-based wellness programs often forgo collecting Social Security Numbers (SSNs) to enhance participant privacy and encourage engagement.
  • Assessing the effectiveness of these programs frequently relies on analyzing changes in healthcare utilization patterns.
  • Medicare administrative claims data serve as a vital resource for tracking healthcare utilization among individuals aged 65 and older.

Purpose of the Study:

  • To describe and validate a method for linking community wellness program participant data to Medicare administrative claims data.
  • To demonstrate the feasibility of evaluating program effectiveness when unique identifiers like SSNs are unavailable.
  • To facilitate the use of Medicare claims data for research on chronic disease self-management programs.

Main Methods:

  • Utilized fuzzy matching techniques to link participant records from the National Study of the Chronic Disease Self-Management Program to Medicare claims data.
  • Employed linking variables including participant name, date of birth, gender, address, and ZIP code.
  • Successfully established connections between participant information and administrative healthcare utilization records.

Main Results:

  • Achieved a linkage rate of 78% between program participants and their corresponding Medicare claims data.
  • Demonstrated the effectiveness of fuzzy matching in overcoming the absence of unique identifiers.
  • Confirmed the utility of linking non-identifiable participant data to administrative datasets for research purposes.

Conclusions:

  • Linking program participant information to Medicare administrative data without unique identifiers is feasible and valuable for program evaluation.
  • This methodology allows researchers to leverage Medicare claims data to assess the impact of wellness programs on healthcare utilization.
  • The fuzzy matching approach provides a robust solution for researchers needing to connect sensitive participant data with administrative health records.