Download - Kuliah 9- Proses Analisis Data Kualitatif
PROSES ANALISIS DATA KUALITATIF
Topics Discussed in this lecture Data analysis
Characteristics of qualitative data Analysis during and after data collection Analytic strategies Computerized analysis
Interpretation of results Insights into interpreting Strategies
Data Analysis
The purpose of data analysis is to bring order to the data
Characteristics of qualitative data Thick, rich descriptions Voluminous Unorganized
Perspectives on analysis and interpretation No single way to gain understanding of phenomena Numerous ways to report data
Objective 1.1
Data Analysis
Perspectives Researcher’s messages are not neutral Researcher’s language creates reality Researcher is related to what and who is being
studied Affect and cognition are inextricably linked What is understood is not neat, linear, or fixed
Data Analysis During Data Collection Data analysis is an ongoing process throughout
the entire research project Analysis begins with the very first interaction between
the researcher and the participants This is a very important perspective given the
interpretive nature of the analysis and the emergent nature of qualitative research designs
Informal steps involve gathering data, examining data, comparing prior data to newer data, and developing new data to gain perspective
Objectives 3.1 and 3.2
Data Analysis After Data Collection
General guidelines and strategies but few specific rules
Common problems Premature conclusions Inexperience of the researcher Self-reinforcement of the researcher’s own ideas
without support from the data Impulsive actions Desire to finish quickly
Most problems are resolved by spending time “living” with the data
Objective 3.2
Data Analysis After Data Collection
Inductive nature of data analysis Large amount of data to analyze Progressively narrowing data into small groups
of key data Multi-staged process of organizing,
categorizing, synthesizing, interpreting, and writing
Objective 3.2
Data Analysis After Data Collection
Iterative process focused on Becoming familiar with the data and identifying
potential themes Examining the data in-depth to provide detailed
descriptions of the setting, participants, and activities
Coding and categorizing data into themes Interpreting and synthesizing data into general
written conclusionsObjective 4.2
Data Analysis After Data Collection Data management
Creating and organizing data collected during the study
Purposes Organize and check data for completeness Start the analytical and interpretive process
No meaningful analysis can be done without effective data management
Data Analysis After Data Collection Data management (continued)
Suggestions Write dates on all notes Sequence all notes with labels Label notes according to type Make photocopies of all notes Organize computer files into folders according to data types
and stages of analysis Make backup copies of files Read through data to make sure it is legible and complete Begin to note potential themes and patterns that emerge
Objective 6.1
Data Analysis After Data Collection
Three formal steps to analyze data Reading and memoing Describing the context and participants Classifying and interpreting
Objective 4.2
Data Analysis After Data Collection
Reading and memoing Reading field notes, transcripts, memos, and
the observer’s comments The purpose is to get an initial sense of the
data Suggestions
Read for several hours at a time Make marginal notes of your impressions, thoughts,
ideas, etc.Objective 4.2
Data Analysis After Data Collection
Description What is going on in the setting and among
participants Purposes
Provide a true picture of the setting and events to understand and appreciate the context
Separate and group pieces of data related to different aspects of the setting, events, and participants
Issues The influence of context on participants’ actions and
understandingObjective 4.2
Data Analysis After Data Collection
Classifying and interpreting The process of breaking down data into small
units, determining the importance of these units, and putting pertinent units together in a general interpretive form
Use of coding and classifying schemes Topic – A basic unit of information Category – a classification of ideas or concepts Pattern – a relationship across categories
Objective 4.2
Data Analysis Strategies
Eight strategies for starting data analysis Identifying themes
A good place to start analyzing data Listing themes or patterns you have seen emerge from
the data Coding data
Reducing the data to a manageable form Guidelines
Read through all the data and attach working labels to blocks of text
Cut and paste these blocks of text to index cards to make it easier to organize the data in various ways
Group the index cards together based on similar labels Re-visit each group of cards to be sure each card still fits
Objectives 6.1 and 6.3
Data Analysis Strategies
Eight strategies (continued) Asking key questions
Working through a series of questions such as those proposed by Stringer (e.g., who is centrally involved, who has resources, how do things happen, etc.)
Doing an organizational review Focus on the organization’s vision and mission, goals and
objectives, structures, operations, problems, issues, and concerns
Concept mapping Create a visual representation of the major influences that
have affected the studyObjectives 6.1 and 6.3
Data Analysis Strategies
Eight strategies (continued) Analyzing antecedents and consequences
Mapping causes and effects Displaying findings
Represent findings in effective visual displays (e.g., graphs, charts, concept maps, etc.)
Stating what is missing Identify what “pieces of the puzzle” are still missing
Objectives 6.1 and 6.3
Computerized Data Analysis
Software is readily available to assist with data analysis Researchers must code the data Manipulation of the data is enhanced The effectiveness of this manipulation is
dependent on the researcher’s ideas, thoughts, hunches, etc.
There is considerable debate as to whether data should be analyzed by hand or computer
Objectives 6.4 and 6.5
Interpretation
The purpose of the interpretation of qualitative analyses of data Attempts to understand the meaning of the findings
Larger conceptual ideas Consistent themes Relationships to theory
Differentiating analysis and interpretation Analysis involves making sense of what is in the data Interpretation involves making sense of what the data
meanObjectives 5.1 and 7.1
Interpretation
Insights into interpretation Interpretation is reflective, integrative, and explanatory
Need to understand one’s own data to describe it Integrated into report writing
Based heavily on connection, common aspects, and linkages among data, categories, and patterns
Interpretation makes explicit the conceptual basis of the categories and patterns
Objective 7.1
Interpretation
Four guiding questions What is important in the data? Why is it important? What can be learned from it? So what?
Objective 7.2
Interpretation
Six strategies Extend the analysis
Note implications that might be drawn Connect findings with personal experiences
The researcher knows the situation better than anyone else and can justify using his or her experiences and perspective
Seek advice from a “critical” friend Seek the insights from a trusted colleague
Contextualize findings in the literature Uncover external sources that support the findings
Objective 7.3
Interpretation
Six strategies (continued) Turn to theory
Provides a way to link the findings to broader issues Allows the researcher to search for increasing levels of
abstraction Provides a rationale for the work
Know when to say, “When!” Don’t offer an interpretation with which you are not
comfortable Suggest what needs to be done
Objective 7.3
Credibility Issues
Six questions to help researchers check the quality of their data Are the data based on your own observations or
hearsay? Is there corroboration by others of your
observations? In what circumstances was an observation made
or reported?Objective 7.4
Credibility Issues
Six questions (continued) How reliable are those providing data? What motivations might have influenced a
participant’s report? What biases might have influenced how an
observation was made or reported?
Objective 7.4
Slides seterusnya berkaitan cara menjalankan analisis data kualitatif Kaedah ini merujuk kepada Miles &
Huberman (1994)
Rujukan
Miles, M. B. & Huberman, A. M. (1994). Qualitative data analysis (2nd ed.). Thousand Oaks, CA: Sage.
Miles & Huberman (1982) telah menyarankan struktur berikut sebagai panduan proses analisis data kualitatif: Selecting Data, Reading Data, Presenting Data, Improving data and drawing conclusions, Collecting Data, Further research activities(Sumber: Altrichter, Posch, & Somekh, 1993).
Miles & Huberman (1982) telah menyarankan struktur berikut sebagai panduan proses analisis data kualitatif:
Selecting Data
Reading Data
Presenting Data
Improving data and drawing conclusions
Collecting Data
Further research activities
Langkah-langkah dalam aliran di dinamakan “The Constructive Stage of Analysis” / Peringkat Pembinaan.
Langkah 1(Reading data) Meneliti data-data yang diperolehi untuk mengingat kembali peristiwa & pengalaman yang berkaitan.
Langkah 2(Selecting data) Menapis untuk memilih fakta yang penting. Menghimpun data mengikut kategori/ criteria. Mempermudahkan maklumat yang kompleks.
Langkah 3(Presenting data)Merumuskan data yang terpilih dalam bentuk yang mudah dipersembahkan, misalnya garis kasar atau grafik.
Langkah 4(Interpreting data & drawing conclusions)Menjalinkan perhubungan antara data.Merangkakan model/teori untuk menjelaskan situasi.
Untuk menjamin kesahan (validity) kajian yang dijalankan, langkah-langkah tersebut hendaklah diiringi atau disusuli dengan “The Critical Stage of Analysis” / Peringkat Kritis.
Sehubungan itu, dua aktiviti utama ialah: Menyemak kesahan sebarang bukti yang
menyokong sesuatu dapatan. Mencari bukti yang tidak selaras dengan dapatan.
KAEDAH-KAEDAH ANALISIS DATA KUALITATIF
1. Analisis Kandungan (Content Analysis)Secara ringkas, kaedah ini melibatkan penyelidik Meneliti data yang diperoleh(contoh: transkrip rakaman
audio sesi pengajaran & pembelajaran) dan menggariskan/menandakan perkara-perkara yang dianggap penting berhubung dengan persoalan kajian.
Merujuk balik perkara-perkara yang ditandakan dan menentukan kategori yang sesuai (contoh: Penyoalan Guru, Pujian dll).
Melabelkan bahagian-bahagian data dengan Nama Kategori atau Singkatan/Kodnya (Contoh: PY bagi Penyoalan, PJ bagi Pujian dsb) digunakan, pastikan koding yang lebih detail digunakan agar punca data berkenaan dapat dikenal pasti.
Contoh:
PJ: TSPP3 / 2 / 18
Kategori – Pujian Punca – Transkrip
Sesi P&P Ke-3
Muka Surat ke-2
Baris ke-18
Analisis Pola (Pattern Analysis)
Setelah mengenal pasti serta membuat pengekodan perkara-perkara penting, penyelidik membuat analisis berpandukan kepada soalan-soalan berikut:
Apakah pola yang dapat dilihat? Apakah kesignifikanan pola ini? Apakah kesan pola perlakuan tersebut? Sejauh manakah pola serta kesan daripadanya
serasi/sepadan dengan hasrat guru? Sekiranya kesan pola berkenaan memang
sepadan dengan hasrat guru, apakah teori yang dapat dirumuskan?
Analisis Dilema (Dilemma Analysis)
Meneliti data yang diperolehi (khasnya transkrip temu bual) untuk mencari kes-kes di mana sasaran kajian menghadapi dilema yang bercorak
“On one hand .. but, on the other hand … “ Menuliskan satu pernyataan yang jelas tentang
dilema berkenaan. Menjelajah dilema tersebut untuk memahami faktor-
faktor yang menimbulkan nya, kesan serta tahap seriusnya.
Mencari penyelesaian.
TEKNIK MENYEMAK DATA(TRIANGULASI DATA) Data yang dipungut perlu mempunyai
kredibiliti. Dengan demikian, data yang dipungut seboleh-bolehnya perlu disemak untuk menentukan sejauh manakah ianya boleh dipercayai.
Satu kaedah yang popular yang digunakan untuk menyemak keboleh percayaan data kualitatif ialah triangulasi.
Cohen & Manion (1994: m.s. 236) dan Altrichter et al (1993: m.s. 116) telah mencadangkan beberapa jenis triangulasi yang boleh diamalkan mengikut situasi dan keperluan kajian iaitu 1. triangulasi masa, 2. triangulasi metod, 3. triangulasi sumber dan 4. triangulasi penyelidik.
1.Triangulasi Masa
Mengumpul data daripada sumber yang sama untuk satu jangka masa yang panjang (juga dikenali sebagai rekabentuk longitudinal). Contohnya, berbincang atau menemu bual seorang individu yang sama mengenai topik yang sama pada masa yang berlainan.
2.Triangulasi Metod
Menggunakan kaedah mengumpul data yang berlainan ke atas objek kajian yang sama (triangulasi antara kaedah – between methods , untuk menguji kesahan). Contohnya, mengumpul maklumat mengenai aspek yang sama melalui temu bual, pemerhatian dan borang soal selidik.
Menggunakan kaedah yang sama untuk situasi yang lain (triangulasi dalam kaedah – within methods – bermaksud mengulang satu kajian untuk menguji keboleh percayaan termasuk membuat replikasi).
3. Triangulasi Sumber
Mendapat maklumat dan pandangan daripada sumber-sumber informasi yang berlainan. Contohnya, mendapat pandangan pelajar, guru sebaya, pemerhatian sendiri, pihak pentadbir, buku rekod guru dan lain-lain lagi.
Altrichter et al (1993: m.s. 116) menggambarkan triangulasi tiga penjuru ini seperti berikut:
Perspektif pihak ketiga
Perspektif Guru Perspektif Pelajar
4.Triangulasi Penyelidik
Menggunakan lebih daripada seorang penyelidik untuk mengumpul data.
(Sumber: Altrichter et al, 1993; Cohen & Manion, 1994)