Evidence map›Paper›PMID 41415726›Full record

ArticleCogent mental health2024

Are we listening to every word? Using multiple analytic methods to examine qualitative data.

Ryan L Boyd, Nicholas R Morrison, Sarah D Horwitz, Rachel Maciag, Emma Travers-Hill, Youngsuk Kim

Abstract read
In one paragraph

Article in Cogent mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ryan L BoydDepartment of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA.ORCID https://orcid.org/0000-0002-1876-6050
Nicholas R MorrisonDepartment of Psychology, Westfield State University, Westfield, MA, USA.ORCID https://orcid.org/0000-0002-7268-1170
Sarah D HorwitzMassachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
Rachel MaciagCambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK.
Emma Travers-HillSchool of Psychology, University of Kent, Canterbury, Kent, UK.ORCID https://orcid.org/0000-0001-7589-4043
Youngsuk KimMassachusetts General Hospital/Harvard Medical School, Boston, MA, USA.ORCID https://orcid.org/0000-0001-6481-2532

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Psychological researchers are increasingly striving to enhance methodological integrity, including in qualitative methods. Although computerized text analysis tools originally emerged as a potential replacement for manual coding approaches, recent studies have underscored the unique yet complementary value of employing several methods. The current study applies two text analysis methods across one qualitative dataset to explore whether each method yields information not clearly evidenced by the other, nor through traditional thematic analysis. Interviews exploring the experiences of paraprofessionals delivering Brief Psychological Interventions (BPIs) were analyzed through Linguistic Inquiry and Word Count (LIWC) and the Meaning Extraction Method (MEM). Results revealed LIWC, MEM, and thematic analysis to be complementary in nature, each providing unique insights that could be missed by implementing any one method alone. Moreover, text analyses can serve as a form of validation for more traditional qualitative approaches while also revealing otherwise indiscernible relationships and patterns within texts.

Indexed as

LIWCMeaning Extraction Methodqualitative methodstext analysisthematic analysis

Identifiers

PMID41415726
PMCPMC12710751

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.