Title

Dissertations Content Analysis Data Set, "Diving Deep into Dissertations" article, College & Research Libraries

Authors

Mandy J. Swygart-Hobaugh M.L.S., Ph.D., Georgia State UniversityFollow
Raeda Anderson, Georgia State UniversityFollow
Denise George, Georgia State UniversityFollow
Joel Glogowski, Georgia State UniversityFollow

Document Type

Dataset

Publication Date

2021

Abstract

We present findings from an exploratory quantitative content analysis case study of 156 doctoral dissertations from Georgia State University that investigates doctoral student researchers’ methodology practic es (used quantitative, qualitative, or mixed methods) and data practices (used primary data, secondary data, or both). We discuss the implications of our findings for provision of data support services provided by the Georgia State University Library’s Res earch Data Services (RDS) Team and subject liaison librarians in the areas of instructional services, data software support and licensing advocacy, collection development, marketing/outreach, and professional development/expansion.

Comments

Associated article:

Swygart-Hobaugh, M., Anderson, R., George, D., & Glogowski, J. (2022). Diving deep into dissertations: Analyzing graduate students’ methodological and data practices to inform research data services and subject liaison librarian support. College & Research Libraries, 83(6), 887-904. https://doi.org/10.5860/crl.83.6.887

Data and documentation files for transparency and replication purposes:

1. Diss_Study_CRLarticle_Codebook_and_Output.pdf --- Contains
(1) article citation, abstract, and author contact,
(2) codebook with variables descriptions and frequency distributions,
(3) output for Tables 2, 3, 4, 5 of the article.

2. Diss_Study_CRLarticle_SPSS_Data.sav --- Raw data file – IBM SPSS Statistics format (SPSS 28 version)

3. Diss_Study_CRLarticle_Excel_Data.xlsx --- Raw data file (data as numeric values and as value labels) – Microsoft Excel format

4. Diss_Study_CRLarticle_NVivo_Text_Search_Queries_for_Software.xlsx -- Queries with 3 columns:
(1) Query Name
(2) Query Criteria
(3) Date Ran and Additional Notes

NOTE: NVivo Project File cannot be shared because some of the included dissertations are embargoed against open distribution.

DOI

https://doi.org/10.57709/10gx-g651

Recommended Citation

Swygart-Hobaugh, M., Anderson, R., George, D., & Glogowski, J. (2022). Dissertations Content Analysis Data Set, "Diving Deep into Dissertations" article, College & Research Libraries (November 2022, vol. 83, issue 6) [Data set].

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License