SEM × Claude AI

Research Guide 2026

SEM × Claude AI
คู่มือวิจัยฉบับสมบูรณ์

หลักการ Structural Equation Modeling และการใช้งาน Claude AI / Claude Cowork เพื่อยกระดับประสิทธิภาพการวิจัยเชิงวิชาการ

5หัวข้อหลัก

SEMหลักการ & แนวคิด

AIClaude Workflow

PSUระดับปริญญาเอก

SECTION 01

SEM คืออะไร?

🔬

นิยาม

Structural Equation Modeling (SEM) คือเทคนิคทางสถิติขั้นสูงที่ใช้วิเคราะห์ความสัมพันธ์เชิงสาเหตุระหว่างตัวแปร ทั้งที่สังเกตได้ (observed) และตัวแปรแฝง (latent)

🎯

วัตถุประสงค์หลัก

ทดสอบว่าโมเดลเชิงทฤษฎีที่ผู้วิจัยสร้างขึ้นนั้น สอดคล้องกับข้อมูลเชิงประจักษ์ (empirical data) หรือไม่ และมีระดับความสอดคล้องเพียงใด

⚡

จุดเด่น

วิเคราะห์ได้พร้อมกันหลายสมการ จัดการ measurement error ได้ดี และสามารถทดสอบทั้งทางตรงและทางอ้อมในโมเดลเดียวกัน

// SEM — โครงสร้างพื้นฐาน

INPUT

ทฤษฎี

Conceptual Model

→

MEASUREMENT

CFA

วัดตัวแปรแฝง

→

STRUCTURAL

Path

ความสัมพันธ์

→

OUTPUT

Model Fit

ประเมินผล

📊

CB-SEM (Covariance-Based)

เหมาะสำหรับ ทดสอบทฤษฎี ที่มีฐานรากชัดเจน ต้องการข้อมูลจำนวนมาก ใช้ซอฟต์แวร์ LISREL, AMOS, lavaan

🔮

PLS-SEM (Partial Least Squares)

เหมาะสำหรับ สำรวจเชิงทฤษฎี ข้อมูลน้อยได้ ไม่ต้องการ normality ใช้ SmartPLS, WarpPLS

🌐

BSEM (Bayesian SEM)

ผสาน prior knowledge เข้ากับข้อมูล เหมาะกับตัวอย่างขนาดเล็ก และโมเดลที่ซับซ้อนมาก

💡 หลักการสำคัญ

SEM ไม่ได้ "พิสูจน์" ว่าโมเดลถูกต้อง แต่ทดสอบว่าข้อมูลสอดคล้องกับโมเดลที่เสนอหรือไม่ (Model-Data Fit) ผู้วิจัยต้องมีทฤษฎีรองรับก่อนเสมอ

SECTION 02

ส่วนประกอบของ SEM

ส่วนประกอบ	ความหมาย	ตัวอย่าง	สัญลักษณ์
Latent Variable (ตัวแปรแฝง)	ตัวแปรที่ไม่สามารถวัดได้โดยตรง ต้องอนุมานจากตัวแปรสังเกต	ทัศนคติ, แรงจูงใจ, ความพึงพอใจ	วงรี ○
Observed Variable (ตัวแปรสังเกต)	ตัวแปรที่วัดได้โดยตรง เป็น indicator ของ latent	คะแนน Likert, การวัดทางกายภาพ	สี่เหลี่ยม □
Exogenous Variable (ตัวแปรอิสระ)	ตัวแปรต้น ไม่มีลูกศรชี้เข้าในโมเดล	ตัวแปรนำ, ปัจจัยเชิงสาเหตุ	ξ (xi)
Endogenous Variable (ตัวแปรตาม)	ตัวแปรปลาย มีลูกศรชี้เข้าอย่างน้อย 1 เส้น	ผลลัพธ์, พฤติกรรม	η (eta)
Factor Loading (น้ำหนักองค์ประกอบ)	ความเข้มข้นของความสัมพันธ์ระหว่าง indicator กับ latent	ค่า ≥ 0.50 ถือว่าดี, ≥ 0.70 ดีมาก	λ (lambda)
Path Coefficient (สัมประสิทธิ์เส้นทาง)	ค่าที่แสดงทิศทางและขนาดของความสัมพันธ์	ทั้ง + และ - บอกทิศทาง	β, γ
Error / Residual	ความคลาดเคลื่อนจากการวัด ต้องคำนึงถึงในโมเดล	ความแปรปรวนที่อธิบายไม่ได้	ε, δ, ζ

CONCEPT

ขั้นตอนการทำ SEM

01📚

ทบทวนวรรณกรรม

สร้างกรอบทฤษฎีและตั้งสมมติฐาน

02✏️

สร้าง Conceptual Model

กำหนดตัวแปรและความสัมพันธ์

03📏

พัฒนาเครื่องมือวัด

สร้าง indicators ของแต่ละ construct

04🔢

เก็บข้อมูล

n ≥ 200 (CB-SEM) หรือ n ≥ 50 (PLS)

05⚙️

CFA + SEM

ประมาณค่า ตรวจสอบ fit

06📊

รายงานผล

ตีความและสรุปผลการวิจัย

⚠️ ข้อควรระวัง

ต้องทำ CFA ก่อนเพื่อตรวจสอบ Measurement Model ให้ผ่านเกณฑ์ทั้ง Convergent validity (AVE ≥ 0.50, CR ≥ 0.70) และ Discriminant validity ก่อนที่จะทดสอบ Structural Model

SECTION 03

ดัชนีความสอดคล้อง (Fit Indices)

χ²/df

Chi-square / Degree of freedom

เกณฑ์: ≤ 3.00 (บางตำรา ≤ 5.00)

ค่ายิ่งน้อยยิ่งดี แสดงว่าโมเดลสอดคล้องกับข้อมูล

CFI

Comparative Fit Index

เกณฑ์: ≥ 0.95 (ยอมรับ ≥ 0.90)

เปรียบเทียบโมเดลกับ null model ค่ายิ่งสูงยิ่งดี

TLI

Tucker-Lewis Index

เกณฑ์: ≥ 0.95 (ยอมรับ ≥ 0.90)

คำนึงถึง parsimony ลงโทษโมเดลที่ซับซ้อนเกิน

RMSEA

Root Mean Square Error of Approximation

เกณฑ์: ≤ 0.06 (ยอมรับ ≤ 0.08)

วัดความคลาดเคลื่อน ค่ายิ่งน้อยยิ่งดี

SRMR

Standardized Root Mean Square Residual

เกณฑ์: ≤ 0.08

ค่าเฉลี่ยของ residual ที่ standardized แล้ว

GFI / AGFI

Goodness of Fit Index

เกณฑ์: ≥ 0.90

ใช้กับ CB-SEM (LISREL/AMOS) เป็นหลัก

📋 เกณฑ์ Reliability & Validity

Cronbach's Alpha ≥ 0.70 | CR ≥ 0.70 | AVE ≥ 0.50 = Convergent Validity | √AVE > r กับตัวแปรอื่น = Discriminant Validity

AVE = Σλ² / (Σλ² + Σδ)

λ = factor loading, δ = measurement error variance

สถานการณ์	การดำเนินการ	เครื่องมือ
Fit ไม่ผ่านเกณฑ์	ดู Modification Indices (MI) พิจารณาแก้ไขโมเดลตามทฤษฎี	AMOS / lavaan
Factor Loading ต่ำ (<0.50)	พิจารณาตัด indicator นั้นออก หรือปรับคำถาม	ปรับโมเดล
AVE ต่ำ (<0.50)	ตัด indicator ที่อ่อนออก หรือเพิ่ม indicator ใหม่	ทบทวนเครื่องมือ
โมเดลผ่านทุกเกณฑ์	รายงานผล Structural Model (path coefficients, p-value)	รายงานผล

SECTION 04

Claude AI กับงานวิจัย SEM

📖

ทบทวนวรรณกรรม

ช่วยสรุปแนวคิด ทฤษฎี และงานวิจัยที่เกี่ยวข้อง อธิบายความสัมพันธ์ระหว่างตัวแปร และแนะนำ constructs สำหรับ SEM

✏️

สร้างกรอบแนวคิด

ช่วยออกแบบ Conceptual Model พัฒนาสมมติฐานการวิจัย และตรวจสอบความสอดคล้องของทฤษฎีกับโมเดล

📝

พัฒนาแบบสอบถาม

ช่วยสร้างข้อคำถาม (indicators) สำหรับแต่ละ construct แปลและปรับภาษา ตรวจสอบ face & content validity

💻

เขียนโค้ดวิเคราะห์

ช่วยเขียน R (lavaan), Python (semopy), Mplus syntax สำหรับ CFA และ SEM พร้อม interpret ผลลัพธ์

📊

ตีความผลวิเคราะห์

อธิบาย fit indices, path coefficients, indirect effects และช่วยเขียนรายงานผลวิชาการทั้งไทยและอังกฤษ

🔍

ตรวจสอบงานวิจัย

ช่วย proof-read บทความ ตรวจสอบความถูกต้องของการนำเสนอผล และให้คำแนะนำปรับปรุงคุณภาพ

PROMPTS

ตัวอย่าง Prompt ที่ใช้ได้ผล

// PROMPT 01 — สร้างกรอบแนวคิด

ฉันกำลังทำวิจัย SEM เกี่ยวกับ [หัวข้อวิจัย] กรุณาช่วยออกแบบ Conceptual Model โดย (1) ระบุ Latent Variables หลัก (2) เสนอ indicators อย่างน้อย 3 ข้อต่อ construct (3) อ้างอิงทฤษฎีที่รองรับแต่ละ path และ (4) เสนอสมมติฐานที่สอดคล้อง

// PROMPT 02 — ตีความ Fit Indices

ผลการวิเคราะห์ SEM ของฉัน: χ²/df = 2.35, CFI = 0.96, TLI = 0.94, RMSEA = 0.058, SRMR = 0.062 กรุณา (1) ประเมินว่าโมเดลผ่านเกณฑ์หรือไม่ (2) อธิบายความหมายของแต่ละค่า (3) แนะนำวิธีรายงานผลในบทความวิชาการ

// PROMPT 03 — เขียน R code (lavaan)

ช่วยเขียน R code สำหรับ lavaan เพื่อทดสอบ CFA ของ construct [ชื่อ construct] ที่มี indicators: x1, x2, x3, x4, x5 พร้อมแสดงผล factor loadings, AVE, CR และ Cronbach's alpha

// PROMPT 04 — เขียนบทวิจัย

จากผลการวิเคราะห์ SEM ที่แนบมา กรุณาช่วยเขียน บทที่ 4 ผลการวิเคราะห์ข้อมูล ภาษาไทยวิชาการ ครอบคลุม (1) ผล CFA (2) ค่า fit indices (3) ผล Structural Model และ (4) การทดสอบสมมติฐาน พร้อมตารางและคำอธิบาย

🎯 เคล็ดลับการใช้ Claude ให้มีประสิทธิภาพ

ใส่บริบทให้ครบ: ระบุสาขาวิชา, วัตถุประสงค์, กลุ่มตัวอย่าง, และซอฟต์แวร์ที่ใช้ ยิ่ง Context ชัด ผลลัพธ์ยิ่งตรงประเด็น และ Iterate — ถามต่อยอดจากคำตอบเดิม

SECTION 05

Claude Cowork สำหรับงานวิจัย

🖥️

Claude Cowork คืออะไร?

Desktop application ที่ให้ Claude AI ทำงานร่วมกับไฟล์และโปรแกรมบนเครื่องโดยตรง เหมาะสำหรับ non-developers ที่ต้องการ automate งานประจำ

📁

จัดการไฟล์วิจัย

อ่าน แก้ไข และสร้างไฟล์ Word, Excel, PDF โดยตรง จัดระเบียบโฟลเดอร์งานวิจัย และ batch process เอกสารจำนวนมาก

⚡

Automate Workflow

สร้าง workflow อัตโนมัติ เช่น แปลง SPSS เป็น CSV สร้างรายงานจากผลวิเคราะห์ หรือ compile บทความจากหลายไฟล์

WORKFLOW

กระบวนการวิจัย SEM ด้วย Claude Cowork

1

เตรียมข้อมูล (Data Preparation)

อัปโหลดไฟล์ Excel/CSV → Claude Cowork ช่วย clean data, recode ตัวแปร, ตรวจสอบ missing values และสร้างไฟล์พร้อมวิเคราะห์

2

เขียนและรัน R/Python Script

Claude Cowork สร้างสคริปต์ lavaan/semopy อัตโนมัติ รัน CFA และ SEM และบันทึกผลในรูปแบบพร้อมรายงาน

3

สร้างรายงานอัตโนมัติ

นำผล output → Claude Cowork แปลงเป็น Word ภาษาไทย พร้อมตาราง ผล fit indices และการตีความตามมาตรฐานวิชาการ

4

จัดการเอกสารวิทยานิพนธ์

รวม draft จากหลายบท แก้ไข font TH SarabunPSK และ layout ตามระเบียบมหาวิทยาลัย สร้าง TOC และ reference list

5

ตรวจสอบและปรับปรุง

Claude Cowork อ่านไฟล์ draft ทั้งหมด เปรียบเทียบกับ template มหาวิทยาลัย และสร้าง checklist ข้อที่ต้องแก้ไข

COMPARISON

Claude AI vs Claude Cowork

ด้าน	Claude AI (Claude.ai)	Claude Cowork
การเข้าถึง	Web / Mobile App	Desktop Application
การทำงานกับไฟล์	อัปโหลดทีละไฟล์	เข้าถึงไฟล์บนเครื่องโดยตรง
Automation	จำกัด	สูง — Batch processing
เหมาะสำหรับ	ถาม-ตอบ, สร้าง content, วางแผน	จัดการไฟล์, สร้างเอกสาร, automate
ทักษะที่ต้องการ	พิมพ์ prompt ได้	พิมพ์ prompt ได้ (ไม่ต้องเขียนโค้ด)
งานวิจัย SEM	ทบทวนวรรณกรรม, อธิบาย, เขียน code	จัดการข้อมูล, สร้างรายงาน, compile เอกสาร
ข้อจำกัด	ต้อง copy-paste ผล, session ไม่ต่อเนื่อง	ต้องติดตั้งบน desktop เท่านั้น

🚀 กลยุทธ์การใช้งานร่วมกัน

ใช้ Claude.ai สำหรับขั้นตอน conceptual (วางแผน, ทบทวนวรรณกรรม, ออกแบบโมเดล, ตีความผล) และใช้ Claude Cowork สำหรับขั้นตอน operational (เตรียมข้อมูล, รัน script, สร้างรายงาน) ทั้งสองเครื่องมือเสริมกัน ครอบคลุมกระบวนการวิจัยตั้งแต่ต้นจนจบ

🎓

สำหรับนักศึกษาปริญญาเอก

ใช้ Claude วางแผนงานวิจัย ทบทวน literature ภาษาอังกฤษ แปลและปรับภาษาวิทยานิพนธ์ให้เป็นภาษาวิชาการ และเตรียม defense presentation

⚠️

ข้อพึงระวัง (Ethics)

AI เป็นเครื่องมือช่วย ไม่ใช่ผู้วิจัย ต้องตรวจสอบความถูกต้องของข้อมูลและผลการวิเคราะห์เสมอ และปฏิบัติตามนโยบาย AI ของมหาวิทยาลัย

SEM × Claude AI Research Guide  |  Faculty of Islamic Sciences, PSU Pattani Campus  |  2026

Qualitative vs Quantitative Research

Items	Qualitative Research	Quantitative Research
Objective / purpose	To gain an understanding of underlying reasons and motivations To provide insights into the setting of a problem, generating ideas and/or hypotheses for later quantitative research To uncover prevalent trends in thought and opinion	To quantify data and generalize results from a sample to the population of interest To measure the incidence of various views and opinions in a chosen sample Sometimes followed by qualitative research which is used to explore some findings further
Sample	Usually a small number of non-representative cases. Respondents selected to fulfil a given quota.	Usually a large number of cases representing the population of interest. Randomly selected respondents.
Data collection	Unstructured or semi-structured techniques e.g. individual depth interviews or group discussions.	Structured techniques such as online questionnaires, on-street or telephone interviews.
Data analysis	Non-statistical.	Statistical data is usually in the form of tabulations (tabs). Findings are conclusive and usually descriptive in nature.
Outcome	Exploratory and/or investigative. Findings are not conclusive and cannot be used to make generalizations about the population of interest. Develop an initial understanding and sound base for further decision making.	Used to recommend a final course of action.

Ref: http://www.snapsurveys.com/qualitative-quantitative-research/

The Analysis of Qualitative Data

The author describes a general approach to the preparation and planning of the data analysis section of a qualitative research proposal.

Diversity in qualitative analysis: multiple approaches and methods and no one right way; but also some features common to all methods; importance of the audit trail (how does researcher get from data to conclusions?)

Analytic induction: uses induction to raise the level of abstraction and to trace out relationships between concepts

Miles and Huberman: data reduction, data display, drawing and verifying conclusions

-  data reduction – reduce data without significant loss of information

-  data display – use any type of diagram to display data as analysis proceeds

-  drawing conclusions – 13 tactics suggested (Appendix 1, p347)

-  verifying conclusions – 13 tactics suggested (Appendix 1, p347)

13 tactics suggested (Appendix 1, p347) that making conceptual/theoretical coherence.

1.      noting patterns, themes

2.      seeing plausibility

3.      clustering

4.      making metaphors

5.      counting

6.      making contrasts/comparisons

7.      partitioning variables

8.      subsuming particulars into the general

9.      factoring

10.  noting relations between variables

11.  finding intervening variables

12.  building a logical chain of evidence

13.  making conceptual/theoretical coherence

13 tactics suggested (Appendix 1, p348) of getting feedback from informants.

1.      checking for representativeness

2.      checking for researcher effects

3.      triangulating

4.      weighting the evidence

5.      checking the meaning of outliers

6.      using extreme cases

7.      following up surprises

8.      looking for negative evidence

9.      making if-then tests

10.  ruling out spurious relations

11.  replicating a finding

12.  checking out rival explanations

13.  getting feedback from informants

Coding: assigning labels to pieces of data; different types and levels of coding (first level descriptive, low inference; higher levels analytic, finding patterns and/or conceptualization and/or interpreting)

Memoing: recording all ideas (substantive, theoretical methodological, etc.) that occur during coding

Abstracting and comparing: two fundamental activities in qualitative data analysis (abstracting – conceptualizing data at higher levels; comparing – similarities and difference between pieces of data or concepts)

Grounded theory analysis: open, axial, selective coding; concept-indicator model (open coding – discovering abstract concept in the data; axial coding – discovering connections between abstract concepts; selective coding – raising the level of abstraction again to the core category)

Narratives and meaning: preserving storied character of data; multiple methods of analysis; form and content

Ethnomethodology: how shared meanings and social norms are developed, maintained; focus on everyday behaviour; central role of language; conversation analysis

Discourse analysis: field of research with different approaches; the nature of discourse; its structure and relationship to hierarchies, power, ideology

Semiotics: the science of signs; language as a sign system; how language produces meaning

Documentary analysis: social production of document; social organization of document; analysis of meaning; applying different theoretical perspectives

Computers: CAQDAS; choosing software for the analysis of qualitative data

Ref: Keith Punch, Introduction to Methods in Education, Chapter 9 The Analysis of Qualitative Data, Sage, 2009: p204

eSaminars of Nvivo for Research

Methods of Discovery: A Guide to Research Writing

by Pavel Zemliansky, Ph.D

• Chapter 1: Research Writing and Argument
• Chapter 2: Research Writing as a Process
• Chapter 3: Research and Critical Reading
• Chapter 4: Finding and Evaluating Research Sources
• Chapter 5: Acknowledging Sources and Avoiding Plagiarism
• Chapter 6: Research Writing in The Academic Disciplines
• Chapter 7: Research Writing and the Internet
• Chapter 8: Creating Autobiographies, Family, and Community Histories Through Research
• Chapter 9: Research and Writing Through Collaboration
• Download pdf
• Chapter 10: Ethnographic Research
• Chapter 11: I-Search Writing
• Chapter 12: Mixing Genres and Voices in Multigenre Writing
• Works Cited 

Ref: http://methodsofdiscovery.net/?q=node/4

Critical Discourse Analysis (CDA) approaches

The CDA approaches, it can find all the theoretical levels of sociological and socio-psychological theory (the concept of different theoretical levels is in the tradition of Merton, 1967: 39–72):

- Epistemology, i.e. theories which provide models of the conditions, contingencies and limit of human perception in general and scientific perception in particular.

- General social theories, often called ‘grand theories’, try to conceptualize the relations between social structure and social action and thus link micro- and macro-sociological phenomena.Within this level, one can distinguish between the more structuralist and the more individualist approaches. To put it very simply, the former provide top-down explanations (structure>action), whereas the latter prefer bottom-up explanations (action>structure). Many modern theories try to reconcile these positions and imply some kind of circularity between social action and social structure.

- Middle-range theories focus either upon specific social phenomena (e.g. conflict, cognition, social networks) or on specific subsystems of society (e.g. economy, politics, religion).

- Micro-sociological theories try to explain social interaction, for example the resolution of the double contingency problem (Parsons and Shils, 1951: 3–29) or the reconstruction of everyday procedures which members of a society use to create their own social order, which is the objective of ethno-methodology.

- Socio-psychogical theories concentrate upon the social conditions of emotion and cognition and, compared to micro-sociology, prefer causal explanations to a hermeneutic understanding of meaning.

- Discourse theories aim at the conceptualization of discourse as a social phenomenon and try to explain its genesis and its structure.

- Linguistic theories, e.g. theories of argumentation, of grammar, of rhetoric, try to describe and explain the pattern specific to language systems and verbal communication.

Ref: Ruth Wodak and Michael Meyer, Critical Discourse Analysis: History, Agenda,Theory, and Methodology, page 24-25

Difference Between Overt and Covert Behavior

The English language is full of words that sound similar but have very different meanings. For example, overt and covert are easy to confuse when read or heard, but they have opposite meanings. The two terms are frequently used by psychologist to describe two different types of behavior. Simply put, overt behavior is something other people can see, while covert behavior is hidden. It is easy to remember to the difference between the two by thinking of the words open and close. Both overt and open begin with the letter o, while covert and close both start with the letter c.
In psychological terms, overt behavior includes anything that is directly observable, like walking, talking and biking. Covert behavior is not observable and includes thinking and imagining. Some covert behaviors are detectable through special means, while others are truly private and completely undetectable. Observable covert behaviors include things like a heart beating. Normally an unobservable behavior, a beating heart is monitorable. By using special equipment, a heart beat can be heard by many people simultaneously. Other equipment can detect symptoms of nervousness. No equipment that currently exists can monitor things like thought and imagination. These convert behaviors remain truly private. Overt and covert antisocial behaviors are frequently discussed because people who exhibit antisocial behaviors are at a disadvantage in society. Children and adolescents who display antisocial behaviors are much more likely to experience depression as adults. This starts a snowball effect because antisocial behaviors are largely influenced by family and parenting factors. Children of parents with deviant or antisocial behaviors are much more likely to develop these behaviors, themselves. Overt antisocial behaviors include aggression towards peers, resentment, negativity and irritability. Covert antisocial behaviors include anxiety and less participation in social activities, such as sports and clubs. Understanding the difference between overt and covert behaviors is important to psychologists, but other interested people may enjoy knowing about these behaviors, too. user www.sfsu.edu or www.ncbi.nlm.nih.gov

http://www.reference.com/motif/society/difference-between-overt-and-covert-behavior

Etic and Emic Perspectives

An emic view is the view from within, the etic view is the view from outside.

Emic is what a person IN the culture studied would have, and an anthropologist would take an outsider's view.

For example all cultures have a taboo on incest. The emic view would be the cultural logic that makes the taboo logical for that culture such as a positive one - when you marry outside your family you extend your family's influence.

The etic view might point to a Darwinian explanation concerning the problems you can get with offspring if you marry someone with similar genes.
Obviously, before Darwin this was unknown and is not a culturally sensitive answer.

The problem with this etic view is that no culture would ever know that unless they had practiced incest- the problem with it can never be known.

This particular example is interesting, because the etic (which is our emic) is actually not correct. In most cases where the genes are healthy no problems ever occur with close relationships and animal breeders do it all the time; it happens in nature all the time without problem.
Looking at emic explanations from other cultures can reveal the cultural fallacies in our own.

 Ref: http://answers.yahoo.com/question/index?qid=20111024014600AAVz8LU

Twelve Major Characteristics of Qualitative Research

Design Strategies

1. Naturalistic inquiry

Studying real-world situations as they unfold naturally; non- manipulative and non-controlling; openness to whatever emerges (lack of predetermined constraints on findings).

2. Emergent design flexibility

Openness to adapting inquiry as understanding deepens and/or situations to change; the researcher avoids getting locked into rigid designs that eliminate responsiveness and pursues new paths of discovery as they emerge.

3. Purposeful sampling

Cases for study (e.g., people, organizations, communities, cultures, events, critical incidences) are selected because they are “information rich” and illuminative, that is, they offer useful manifestations of the phenomenon of interest; sampling, then, is aimed at insight about the phenomenon, not empirical generalization from a sample to a population.

Data-Collection and Fieldwork Strategies

4. Qualitative data

Observations that yield detailed, thick description; inquiry in depth; interviews that capture direct quotations about people’s personal perspectives and experiences; case studies; careful document review.

5. Personal experience and engagement

The researcher has direct contact with and gets close to the people, situation, and phenomenon under study; the researcher’s personal experiences and insights are an important part of the inquiry and critical to understanding the phenomenon.

6. Empathic neutrality and mindfulness

An empathic stance in interviewing seeks vicarious understanding without judgment (neutrality) by showing openness, sensitivity, respect, awareness, and responsiveness; in observation it means being fully present (mindfulness).

7. Dynamic systems

Attention to process; assumes change as ongoing whether focus is on an individual, an organization, a community, or an entire culture; therefore, mindful of and attentive to system and situation dynamics.

Analysis Strategies

8.  Unique case orientation

Assumes that each case is special and unique; the first level of analysis is being true to, respecting, and capturing the details of the individual cases being studied; cross-case analysis follows from and depends on the quality of individual case studies.

9. Inductive analysis and creative synthesis

Immersion in the details and specifics of the data to discover important patterns, themes, and interrelationships; begins by exploring, then confirming, guided by analytical principles rather than rules, ends with a creative synthesis.

10.  Holistic perspective

The whole phenomenon under study is understood as a complex system that is more than the sum of its parts; focus on complex inter-dependencies and system dynamics that cannot meaningfully be reduced to a few discrete variables and linear, cause effect relationships.

11. Context sensitivity

Places findings in a social, historical, and temporal context; careful about, even dubious of, the possibility or meaningfulness of generalizations across time and space; emphasizes instead careful comparative case analyses and extrapolating patterns for possible transferability and adaptation in new settings.

12.  Voice, perspective, and reflexivity

The qualitative analyst owns and is reflective about her or his own voice and perspective; a credible voice conveys authenticity and trustworthiness; complete objectivity being impossible and pure subjectivity undermining credibility, the researcher’s focus becomes balance understanding and depicting the world authentically in all its complexity while being self-analytical, politically aware, and reflexive in consciousness.

Source: From M. Q. Patton, Qualitative Research and Evaluation Methods, Third Edition, pp. 40–41, copyright 2002 by Sage Publications, Inc.

Ref: http://www.southalabama.edu/coe/bset/johnson/oh_master/Ch12/Tab12-01.pdf

Qualitative Research

Qualitative research relies primarily on the collection of qualitative data (i.e., nonnumeric data such as words and pictures).

• I suggest that, to put things in perspective, you start by reviewing the table showing the common differences between qualitative, quantitative, and mixed research. That is, take a quick look at Table 2.1.

Next, to further understand what qualitative research is all about, please carefully examine Patton’s excellent summary of the twelvemajor characteristics of qualitative research, which is shown in Table 14.1 in your book.

In addition to Patton’s 12 major characteristics, some qualitative researchers advocate the theories/philosophies of postmodernism and poststructuralism. These ideas are explained (including their historical origins) in Exhibit 14.1.

Postmodernism is an intellectual movement that reacted to and rejected what they called modernism. They also rejected what is commonly called positivism. Here are the definitions of modernism and positivism (the ideas they rejected) followed by the definition of postmodernism (the idea they like).

·         Modernism is the term used by postmodernists to refer to an earlier and outdated period in the history of science that viewed the world as a static (i.e., unchanging) machine where everyone follows the same laws of behavior.

·         Positivism is the term used by qualitative research to refer to what might better be labeled “scientism,” which is the belief that all true knowledge must be based on science.

·         Postmodernism is a historical intellectual movement that constructs its self-image as in opposition to modernism; postmodernism emphasizes the primacy of individuality, difference, fragmentation, flux, constant change, lack of foundations for thought, and interpretation.

Another intellectual and historical movement that characterizes some qualitative researchers is called poststructuralism. Rather than rejecting the prior movement from which it takes its name (structuralism), poststructuralists reject parts of structuralism and build on other parts. Here is the definition of structuralism and the movement called structuralism that is popular with a significant number of qualitative researchers:

·         Structuralism is a broad or grand theory that emphasizes the importance of cultural-structural-institutional and functional relations as providing a large part of the social world in which humans live, and this structure is key in determining meaning and influencing human behavior.

·         Poststructuralism refers to a historical intellectual movement that rejects universal truth and emphasizes differences, deconstruction, interpretation, and the power of ideas over peoples’ behavior.

In short, a significant number of qualitative researchers identify with the movements of postmodernism and poststructuralism, and that’s why it is important to know what those ideas are all about.

In the rest of this chapter, we discuss the four major types or methods of qualitative research:

• Phenomenology.

• Ethnography.

• Grounded theory.

• Case study.

(In the next chapter (Chapter 15) we discuss historical research which can be viewed as a fifth type of qualitative research because of its interpretative nature.)

To get things started, please examine the key characteristics (i.e., purpose, origin, data-collection methods, data analysis, and report focus) of these four types or methods of qualitative research as shown in Table 14.2 (see your textbook).

Phenomenology

The first major approach to qualitative research is phenomenology (i.e., the descriptive study of how individuals experience a phenomenon).

• Here is the foundational question in phenomenology: What is the meaning, structure, and essence of the lived experience of this phenomenon by an individual or by many individuals?

• The researcher tries to gain access to individuals' life-worlds, which is their world of experience; it is where consciousness exists.

• Conducting in-depth interviews is a common method for gaining access to individuals' life- worlds.

• The researcher, next, searches for the invariant structures of individuals' experiences (also called the essences of their experience).

• Phenomenological researchers often search for commonalities across individuals (rather than only focusing on what is unique to a single individual). For example, what are the essences of peoples' experience of the death of a loved one? Here is another example: What are the essences of peoples' experiences of an uncaring nurse?

• After analyzing your phenomenological research data, you should write a report that provides rich description and a "vicarious experience" of being there for the reader of the report. Shown next are two good examples. See if you get the feeling the patients had when they described caring and noncaring nurses.

• Here is a description of a caring nurse (from Exhibit 14.3) based on a phenomenological research study: “In a caring interaction, the nurse’s existential presence is perceived by the client as more than just a physical presence. There is the aspect of the nurse giving of oneself to the client. This giving of oneself may be in response to the client’s request, but it is more often a voluntary effort and is unsolicited by the client. The nurse’s willingness to give of oneself is primarily perceived by the client as an attitude and behavior of sitting down and really listening and responding to the unique concerns of the individual as a person of value. The relaxation, comfort, and security that the client expresses both physically and mentally are an immediate and direct result of the client’s stated and unstated needs being heard and responded to by the nurse” (From Creswell, 1998, p.289).

• From the same study of nurses, a description also was provided of a noncaring nurse (our Exhibit 14.4). Here it is: “The nurse’s presence with the client is perceived by the client as a minimal presence of the nurse being physically present only. The nurse is viewed as being there only because it is a job and not to assist the client or answer his or her needs. Any response by the nurse is done with a minimal amount of energy expenditure and bound by the rules. The client perceives the nurse who does not respond to this request for assistance as being noncaring. Therefore, an interaction that never happened is labeled as a noncaring interaction. The nurse is too busy and hurried to spend time with the client and therefore does not sit down and really listen to the client’s individual concerns. The client is further devalued as a unique person because he or she is scolded, treated as a child, or treated as a nonhuman being or an object. Because of the devaluing and lack of concern, the client’s needs are not met and the client has negative feelings, that is, frustrated, scared, depressed, angry, afraid, and upset” (From Creswell, 1998, p.289).

Ethnography

The second major approach to qualitative research is ethnography (i.e., the discovery and description of the culture of a group of people).

• Here is the foundational question in ethnography: What are the cultural characteristics of this group of people or of this cultural scene?

• Because ethnography originates in the discipline of Anthropology, the concept of culture is of central importance.

• Culture is the system of shared beliefs, values, practices, language, norms,   rituals, and material things that group members use to understand their world.

• One can study micro cultures (e.g., such as the culture in a classroom) as well as macro cultures (e.g., such as the United States of America culture).

There are two additional or specialized types of ethnography.

1.      Ethnology (the comparative study of cultural groups).

2.      Ethnohistory (the study of the cultural past of a group of people). An ethnohistory is often done in the early stages of a standard ethnography in order to get a sense of the group's cultural history.

Here are some more concepts that are commonly used by ethnographers:

·         Ethnocentrism (i.e., judging others based on your cultural standards). You must avoid this problem if you are to be a successful ethnographer!

·         Emic perspective (i.e., the insider's perspective) and emic terms (i.e., specialized words used by people in a group).

·         Etic perspective (i.e., the external, social scientific view) and etic terms (i.e., outsider's words or specialized words used by social scientists).

·         Going native (i.e., identifying so completely with the group being studied that you are unable to be objective).

·         Holism (i.e., the idea that the whole is greater than the sum of its parts; it involves describing the group as a whole unit, in addition to its parts and their interrelationships).

The final ethnography (i.e., the report) should provide a rich and holistic description of the culture of the group under study.
 

Case Study Research

The third major approach to qualitative research is case study research (i.e., the detailed account and analysis of one or more cases).

• Here is the foundational question in case study research: What are the characteristics of this single case or of these comparison cases?

• A case is a bounded system (e.g., a person, a group, an activity, a process).
   

Because the roots of case study are interdisciplinary, many different concepts and theories can be used to describe and explain the case.
 

Robert Stake classifies case study research into three types:

1. Intrinsic case study (where the interest is only in understanding the particulars of the case).

2. Instrumental case study (where the interest is in understanding something more general than the case).

3. Collective case study (where interest is in studying and comparing multiple cases in a single research study).
    

Multiple methods of data collection are often used in case study research (e.g., interviews, observation, documents, questionnaires).
 

The case study final report should provide a rich (i.e., vivid and detailed) and holistic (i.e., describes the whole and its parts) description of the case and its context.

Grounded Theory

The fourth major approach to qualitative research is grounded theory (i.e., the development of inductive, "bottom-up," theory that is "grounded" directly in the empirical data).

• Here is the foundational question in grounded theory: What theory or explanation emerges from an analysis of the data collected about this phenomenon?

• It is usually used to generate theory (remember from earlier chapters that theories tell you "How" and "Why" something operates as it does; theories provide explanations).

• Grounded theory can also be used to test or elaborate upon previously grounded theories, as long as the approach continues to be one of constantly grounding any changes in the new data.
   

Four important characteristics of a grounded theory are

• Fit (i.e., Does the theory correspond to real-world data?),

• Understanding (i.e., Is the theory clear and understandable?),

• Generality (i.e., Is the theory abstract enough to move beyond the specifics in the original research study?),

• Control (i.e., Can the theory be applied to produce real-world results?).

Data collection and analysis continue throughout the study.

When collecting and analyzing the researcher needs theoretical sensitivity (i.e., being sensitive about what data are important in developing the grounded theory).

Data analysis often follows three steps:

1. Open coding (i.e., reading transcripts line-by- line and identifying and coding the concepts found in the data).

2. Axial coding (i.e., organizing the concepts and making them more abstract).

3. Selective coding (i.e., focusing on the main ideas, developing the story, and finalizing the grounded theory).
    

The grounded theory process is "complete" when theoretical saturation occurs (i.e., when no new concepts are emerging from the data and the theory is well validated).
 

The final report should include a detailed and clear description of the grounded theory.

Final note: The chapter includes many examples of each of the four types of qualitative research to help in your understanding (i.e., phenomenology, ethnography, case study, and grounded theory). In addition, you should find and read new examples in the published literature to help further your understanding of these four important approaches to qualitative research.

Ref: Chapter 14 Qualitative Research