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Archival Research

Archival Research

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...

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Cross-Sectional Research

Cross-Sectional Research

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Psychology as a Science

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Psychology, as a scientific discipline, aims to understand the mind and behavior through rigorous and systematic methods. The foundation of psychological research is evidence-based, relying heavily on the scientific method to derive and validate knowledge. This structured approach ensures that findings are reliable, valid, and applicable to broader contexts.
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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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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

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Published on: December 9, 2022

Research data management needs assessment for social sciences graduate students: A mixed methods study.

Xuan Zhou¹, Zhihong Xu², Ashlynn Kogut³

¹Department of Research Data Services, University Libraries, Texas State University, San Marcos, Texas, United States of America.

|February 23, 2023

Summary

Social science graduate students need better research data management (RDM) training. Most lack confidence and awareness of RDM services, highlighting key areas for improved instruction.

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Area of Science:

Social Sciences
Information Science
Education

Background:

Research data management (RDM) is crucial for social science research due to data complexity and privacy concerns.
Existing RDM training inadequately addresses the specific needs of social science graduate students.
A gap exists in graduate student preparedness and confidence regarding RDM practices.

Purpose of the Study:

To investigate the RDM awareness, preparation, confidence, and challenges faced by social science graduate students.
To identify specific training needs for effective RDM among this population.
To inform the development of student-centered RDM training programs.

Main Methods:

A mixed-methods approach was employed, combining surveys and interviews.
Surveys assessed RDM preparedness and training needs in 98 graduate students.
Interviews explored RDM awareness, knowledge, and challenges in 10 randomly selected students.

Main Results:

Participants reported low confidence in RDM, with U.S. citizens showing higher confidence than international students.
Awareness of on-campus RDM services, data repositories, and data sharing was generally low.
Identified training needs include data documentation, organization, version control, open-source data analysis, and data security/preservation.

Conclusions:

Findings underscore the necessity of targeted RDM training for social science graduate students.
RDM training should cover data documentation, organization, versioning, analysis with open-source tools, and preservation.
Understanding student backgrounds is essential for designing effective, student-centered RDM instruction.