PUBLIKASI RISET ANALISIS KONTEN
KUALITATIF
Sabtu, 24 Februari 2024
FADHILA YONATA, M.PD.
•DOSEN STAIN SULTAN ABDURRAHMAN KEPULAUAN RIAU
•MAHASISWA DOKTOR UNNES - ILMU PENDIDIKAN BAHASA
•EDITOR-IN-CHIEF SALEE JOURNAL, JERCS JOURNAL
•EDITOR JURNAL SINTA (PIONEER JOURNAL, JOURNAL OF EDUCATIONAL REVIEW AND CULTURAL STUDIES)
•REVIEWER: SAGE OPEN (Q2), CAMTESOL (INT.
CONFERENCE), TEFLIN (INT. CONFERENCE), SOME NATIONAL JOURNALS
IG: fadilyonata
Email: [email protected]
WA: 081364516151
WEBINAR AGENDA
Concept
DEFINITION
ASPECTS
DESIGN
SAMPLE
STEP
REPORT Potential
research focus
Marrying multiple
design
Published articles
Coding process
Important parts in writing
Making inference from the data (Downe-Wamboldt, 1992)
Many words of the text are classified into
fewer content categories (Weber, 1990)
MARRYING
MULTIPLE DESIGNS
Content analysis as a data collection and analysis method
Content analysis as a research method
Quantitative Content analysis Multimodal Content analysis Qualitative Content analysis Automated Content analysis
Critical Content analysis
CONTENT ANALYSIS
Manifest textual elements (Words, phrases, etc.)
QUALITATIVE CONTENT ANALYSIS
Random sampling
Deductive (Test hypotheses)
Manifest and latent textual elements (Themes)
statistical significances Unique themes
Purposive sampling
Inductive-Deductive (Mixed)
QUALITATIVE CONTENT
ANALYSIS
The crucial point is that
quantitative approaches are not as accurate as interpretive approaches when it comes to understanding communication (Kuckartz & Radiker, 2023).
QCA is a method for
systematically describing the meaning of qualitative material.
It is done by classifying material as instances of the categories of a coding frame. (Schreier, 2012)
The systematic analysis of the meaning of material in need of interpretation by assigning it to the categories of a category
system (Stamann et al., 2016)
QCA as a qualitative method
with a systematic approach and a claim to produce inter-
subjectively valid results.
TYPICAL DATA
Interviews of all kinds (narrative interviews, problem-centred interviews, online interviews, telephone interviews, etc.)
Focus groups and group discussions
Documents (e.g., files of the youth welfare office, annual reports, sustainability reports of companies) Observation protocols
Field notes
Film recordings (e.g., classroom interaction, educational behaviour) Videos (e.g., from the internet)
Pictures, drawings, and photos
Answers to open questions in surveys
Data from social media (e.g., from Twitter and Facebook, YouTube comments, posts in online forums, comments on newspaper reports).
(Learning) diaries.
Articles in newspapers, magazines, and other media Speeches and debates (e.g., in parliament)
Podcasts
Internet data (e.g., blog posts, company websites) Scientific publications
Textbooks.
STEPS
01 02 03 04 05 06 07
PREPARE DATA
DEFINE UNIT OF ANALYSIS
DEVELOP CATEGORIES AND A CODING SCHEME TEST THE CODING SCHEME
CODE ALL TEXT
ASSESS CODING CONSISTENCY
DRAW CONCLUSION FROM THE CODED DATA
INSTRUMENT
Category system (Kuckartz
& Radiker, 2023)
Category manual
Coding guide Definitions of categories
Instructions for coders
INDUCTIVE-DEDUCTIVE-
MIXED
CATEGORIES
The coding scheme for the coding of visual representations in science textbooks is usually based on the different typologies as proposed by Hegarty et al. (1991), Leivas Pozzer and Roth (2003), and Novick (2006). According to Hegarty et al. (1991) visual representations or diagrams in science textbooks are categorized into three types, namely, Iconic, Schematic and Charts & Graph. To these three categories, a recent category called Augmented Reality was added by Novick (2006), extending the number to four.
Leivas Pozzer and Roth (2003) categorized illustrations in science textbooks into five categories—
Photographs, Naturalistic Drawings, Maps & Diagrams, Graphs & Tables and Equations. Biology textbooks are made of many other categories of visuals other than those mentioned in the above typologies, and therefore categorization based on these typologies will not make the classification exhaustive. For example, visuals such as cryoelectromicrograph images, molecular visualization software generated images, computer generated images, space- filling model images, photomicrographs, genetic crosses, pedigree charts, phylogenetic trees, cladograms, symbolic equations, structural formulae and biological process diagrams can nowhere be placed under any of the above mentioned categories. Therefore, the researchers felt the need to develop a new typology and taxonomy for categorizing the visuals in biology textbooks. The coding scheme used in this study is based on this new typology and taxonomy, which is appropriate for biology textbooks. Under the new typology and taxonomy, visual representations in biology textbooks can be categorized into 19 different categories. Table 5 represents the categories of visual representations under the new coding scheme used in this study. The definitions and meanings of these categories are provided under the rules of coding.
1
2
3
Reliability problems (Weber, 1990)
AMBIGUITY OF
WORD MEANINGS
CATEGORY DEFINITIONS
CODING RULES
1
2
3
Reliability concerns (Krippendorf, 1980)
STABILITY
REPRODUCIBILITY
ACCURACY
The results are invariant (One person)
Standard coding or norm
Content classification produces the same results by more than one coder
QUALITATIVE CONTENT
ANALYSIS
IN ACTION
Participant demographic
inclusion criteria
Duration/ exact time range
Instrument Sample of questions
Category system Member
checking for
trustworthiness
Research ethics with human
QUALITATIVE CONTENT
ANALYSIS
IN ACTION
Instrument:
Reflection writing
Participant demographic Research ethics with humans
Category system
Developing categories inductively
THANK YOU
Comments and questions are welcomed
