RES812 U4 Individual Project

Running head: CODING PROJECT REPORT 1 (version 1.01)

Unit 4 Individual Project

Coding Project Report of Virtual Interview Question Responses

ThienSi (TS) Le

Colorado Technical University

RES 812-1503C-02: Qualitative Research Methods

Professor: Dr. Kathleen Hargiss

August 24, 2015

CODING PROJECT REPORT 2

Abstract The paper in Unit 4 Individual Project (U4 IP) bases upon a qualitative data coding

process that includes coding and summarizing the responses from classmates in the

Course RES812 -1503C-02 Qualitative Research Methods. The transcript for an analysis

consists of eleven classmates’ responses to the virtual interview question posed in the

earlier Phase 3 Discussion Board 3: Capturing Qualitative Data Virtually. The virtual

interview question was

“Explain what being a doctoral student means for you. How has your life changed since

starting your doctoral journey?”

This paper is a coding project report that includes:

Description of the coding process.

Topical coding scheme (list of topics; code book).

Diagram of findings (topics and themes).

Brief narrative summary of findings referring to the graphic diagram (table).

Appendix with coded transcripts in one of the following formats:

A Word document showing color-coded codes

A Word document using the comments function to indicate codes

An output file from qualitative software

Keywords: Accuracy; central research question; cohort; consistency; data coding

process; dissertation; experience; goal; honor; knowledge; research; reward; time

management.


Title

Coding Project Report of Virtual Interview Question Responses

This paper is a coding project report of the qualitative data coding process. It

bases on the responses from thirteen participant classmates on the virtual interview

question (stated in the Abstract section above) assigned in Unit 3 Discussion Board.

Following the qualitative data coding process assigned to Unit 3 Discussion Board 3, this

paper provides a coding project report that consists of

A. Description of the coding process.

B. Topical coding scheme.

C. Tables of findings.

D. Brief narrative summary of findings

E. Summary.

F. Appendix with code transcripts in a word document using the comments

function to indicate the codes.

A. Description of the coding process:

The qualitative (Ql) data coding process can be divided into four stages:

- Preparation of data

- An initial coding phase

- A second coding phase

- A method to ensure the reliability of the coding process.

1. Preparation of data:


To organize and prepare the data for analysis, a researcher needs to transcribe

interviews, optically scan material, type up field notes, catalogue all of the visual

material, and sort and arrange the data into various types of depending on the sources of

information (Creswell, 2014). For example, organizing data types in Big Data bases on e-

commerce, e-government, science and technology, smart health, security and public

safety. A researcher should read all the data for general sense of information and reflect

on overall meaning.

2. An initial coding phase:

This initial phase mostly focuses on topical coding and the recording notes.

Coding is the process of organizing the data by bracketing chunks and writing

word that represents a category in the margins (Rossman & Rallis, 2012). It relates to

- taking text data, pictures, etc.

- segmenting sentences, paragraphs, images into categories.

- labeling these categories with a term based on the actual language (vivo term)

of the participants.

The codes on topics are for readers who would expect to find, based on the

past literature and common sense (Lofland & Lofland, 2006). Codes that are surprising

and were not anticipated at the beginning of the study make readers pay more attention.

Codes that are unusual are conceptual interests to readers.

3. A second coding phase:


The second phase focuses on themes or pattern identification. The coding

process is used to generate a description of the setting or people as well as categories or

themes for analysis (Bryman & Bell, 2011). A researcher may

- develop the codes on the emerging information collected from the

participants,

- use predetermined codes and then fit data to them.

- use some combinations of emerging information and predetermined codes.

He/she can develop a qualitative codebook that contains a list of predetermined

codes such as code definitions for coding the data.

4. Methods to ensure reliability of the coding process:

A researcher considers taking some steps in the study to check for accuracy

and creditability of the findings. Qualitative validity requires a researcher to check for

validity by using a certain procedure. Validity means trustworthiness, authenticity, and

credibility (Rubin & Rubin, 2011). On the other hand, qualitative reliability requires that

the approach in Ql design is consistent across numerous researchers and different projects

(Gibbs, 2007). Creswell (2014) suggested some methods of the qualitative reliability in

four procedures below:

a. Checking transcripts to make sure that there are no obvious mistakes made

during transcription.

b. Ensure that there is no drift in the definitions of codes, a shift in the

meaning of the codes during coding process by constantly comparing data with the codes

and writing memos about the codes.


c. For team research, coordinate the communication meetings among the coders

on the analysis.

d. Comparing the results that are independently derived by cross-checking the

codes developed by research members.

The coding process in this report is simplified as follows:

- Create a transcript from captured data in qualitative interviews.

- Carefully read the transcript, take notes, and add comments.

- Analyze qualitative data.

- Do coding on captured interview data.

- Develop a diagram of findings.

- Prepare tables.

- Write the coding project report.

B. Topical coding scheme:

Responses to the virtual interview question on Unit 3 Discussion Board (U3

DB):

This part is a list of topics:

1. Gain knowledge

2. Gain experience

3. Time management

4. Better person

5. Better researcher

6. Cohort


7. Pride

8. Rewards

9. Contribute the body of the knowledge

10. Hard work

11. Financial problem

12. Personal problems

Data analysis and code definition are listed in section C. Tables of Findings

below.

Responses to qualitative data coding process on Unit 3 Discussion Board 3 (U3

DB3):

The topics are found in four stages as follows:

1. Data preparation:

In this stage, the topics are creating transcripts, identifying data, organizing

data, interpreting data, reducing data, categorizing data, and availability of data.

2. Initial phase coding:

This stage includes finding topical data, understanding data, representing

words on data, corrected data, labeling data, coding definition.

3. Second phase coding:

This stage focuses on themes, identifying the patterns, data characteristics,

data categories, data significance, data stability and codebook.

4. Reliability:


This stage emphasizes on data validity, credibility, accuracy, consistency,

clarifications, cross-checking codes, using software tools such as Nvivo Atlas.ti.

C. Tables of findings - Themes

The result of the data analysis through data coding process shows that doctoral

students are professionals who motivate themselves toward the doctorate. They are eager

to learn new concepts, up-to-date technologies and do a research study. With number of

counts in each category in the Table 2 below, five themes can be drawn as follows:

1. An ambition of gaining knowledge through the highest level of education with the

highest counts (i.e., 8) in the category of Knowledge Gain.

2. Time management received six counts. Participants know how to manage time well

and appropriately in a research study, family, work and their personal pleasure.

3. A desire of obtaining practical experience to promote themselves in their career

with five counts in the category of Experience Gain.

4. With three counts, a positive reflection on participants’ personality such as well-

educated persons, better researchers, pride, cohort, and reward from the research study.

5. With two counts, a negative feedback on participants’ sacrifice such as lack of

spouse’s support, hardship, spending less time with family during the study.

Topics and themes were found in this coding process are populated in three

primary tables as shown below:

Table 1: The classmates’ responses on the virtual interview question in U3 DB.


Table 2: Code definition of the responses from the classmates on U3 DB Virtual

interview questions. Some responses to each category are listed in the right-hand side

column. For example, the code GK (Gain Knowledge) receives the most number (8) of

responses by the participants.


Table 3: The classmates’ responses and codes on virtual interview question in U3 DB.

Table 4: Coding the responses from classmates on qualitative data coding process in

U3 DB3.


Table 5: The classmates’ responses and codes on qualitative data coding process in U3

DB3.

Table 6: Code definition of the responses from the classmates on data coding process in

U3 DB3


Table 7: Code definitions for U3 DB3

Table 7:

Code Definitions Ac Accuracy

Av Availability of data

C Categorizing data

Ca Data categories

Cb Codebook

Cd Code definitions

Cc

Cross-checking

codes

Ch

Data

characteristsics

Co Correlated data

Cr Credibility

Cl Clarification

L Labeling data

Id Identifying data

In Interpreting data

O Organizing data

P

Pattern

identification

R Reducing data

Rw Representing words

S Data significance

St Data significance

Sw Software tools

T Transcripts

Th Themes

To Topical data

Tr Traceability

Ty Data types

U Understading data

V Validity


D. Brief narrative summary of findings

The responses of thirteen participants on the virtual interview question indicate

that

the findings are quite interesting.

1. The highest number of responses is eight on the category of gaining

knowledge. It shows that the doctoral students are hungry for knowledge. They want to

know more about research and technologies.

2. The category of gaining experience receives the response number of five –

the second highest. Participants who are mostly professionals need more practical

application experience.

3. The number of responses is three for four categories: Better educated person,

super researcher, pride, and cohort.

4. Seven categories that receive two responses are

- Positive responses: Contributing to the knowledge body, improving

communication, learning dissertation.

- Negative responses: disease, financial problem, less time spent with family,

less pleasure time,

5. And eight categories receive one response each:

- Positive responses: accomplishing the goal, growth edge, honor, improving

technology, knowing processes, using the new approach, a new opportunity.

- Negative response: divorce.


Notice that the analysis result of qualitative data coding process in U3 DB3 is not

finished yet due to limited time constraint. It is left open for future completion.

E. Summary

The document of Unit 4 Individual Project presents the results of the coding

process, using matrix format tables to display the qualitative findings graphically. It

consists of

A. Description of the coding process.

B. Topical coding scheme (list of topics; code book).

C. Tables of Findings (topics and themes).

D. Brief narrative summary of findings referring to the graphic diagram

(table).

E. Appendix with coded transcripts in the following formats:

a. A power point document is showing color-coded codes.

b. A Word document using the comments function to indicate codes.

c. An output file from qualitative software

F. Appendix

Appendix section includes coded transcripts in the following formats:

a. A power point document showing color-coded codes:

Refer to Coding Tables.

.


b. A Word document using the comments function to indicate codes.

Refer to the attachments: RES812U4IP-U3 DB-Document,

RES812U4IP-U3 DB3-Document.

c. An output file from qualitative software:

The NVivo10 project was created as shown below, but the work was

not complete yet.


REFERENCES:

Bryman, A., & Bell, E. (2011). Planning a research project and formulating

research questions. In Business research methods. Cambridge: Oxford University

Press.

Creswell, J. W. (2014). Research design: qualitative, quantitative, and mixed methods

approaches. Sage publications

Hargiss, K (2015). Qualitative Research. Presentation presented at chat session 7 of the

Course RES812-1503C-02: Qualitative Research Methods. Colorado Technical

University.

Rubin, H. J., & Rubin, I. S. (2011). Qualitative interviewing: The art of hearing data.

Sage.

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