According to Robson's, there are three traditions in qualitative research: case studies, ethnographic studies, and grounded theory studies. He mentions the importance of the quality of the analyst in qualitative research. As Fettrman (1989) puts it, in the context of an ethnographic stance, the analysis is as much a test of the enquirer as it is a test of the data. Qualitative analysis remains much closer to codified common sense than the complexities of statistical analysis of quantitative data. However, humans as “natural analysis” have deficiencies and biases corresponding to the problems that they have as observers.

He also mentions some common features of qualitative data analysis:

1. Giving codes to the initial set of materials obtained from observations, interviews,
  etc.;

2. Adding comments, reflections, etc.;

3. Going through the materials trying to identify similar patterns;

4. Taking these patterns out to the field to help focus the next wave of data collection;

5. Gradually elaborate a small set of generalizations;

6. Linking these generalizations to a formalized body of knowledge in the form of
  constructs or theories.

One of the more famous qualitative research studies is the work of Margaret Mead, who studied the Samoan culture. Such studies not only rely on personal observation but also often require the recruitment of informants. Studies such as those conducted to see what life was like for first-generation immigrants who came to the United States in the early part of the twentieth century can be a qualitative study. Interviewers can interview a number of first-generation immigrants and develop life histories. With enough life histories showing similar patterns of behavior a picture can be developed as to what life was like for those who lived in that era. The interviews might be tape recorded. The interview process is conducted in such a way and in such a length to enable the informant to adjust to the interviewer and the recording device. It is part of the plan of qualitative research to carefully choose the interviewer in order to have the best match to the informant.

Quantitative research seldom deviates from the research plan. Qualitative research, on the other hand, is more flexible. However, qualitative research has several advantages over quantitative research. Qualitative research uses direct observation and semi-structured interviewing in real-world settings. The researcher looks for social transactions and interactions between people and events. The data collection process is less structured than quantitative research. The researchers may make a number of adjustments during the observations. The researcher may even develop new hypotheses during the research process. Qualitative research is more naturalistic, participatory, and interpretive.

Robson uses the term “attractive nuisance” to advise against our falling into the trap of being so taken in by the advantages of qualitative data that we fail to consider its limitations and incidental problems.  Some of the caveats to be borne in mind are:

·         The researcher may only know roughly in advance what he/she is looking for. As such, it is difficult, especially for beginning researchers, to determine when enough data has been collected.

·         Because it usually reflects individuals’ interpretation of events, the qualitative research of which it is a part is less able to be generalized than quantitative research.

·         The research problem, not our aversion or liking for a particular research method, should be the overriding determinant for the type of data, collection methods, and research design we adopt.

·         The fact that qualitative designs are not fixed but emerge as the study unfolds is a good reason to be well prepared when undertaking such research.

·         Being well prepared includes the mental readiness to rigorously analyze decisions points reached and succinctly explain conclusions made.

·         Because qualitative data comes in the form of words, pictures, or objects, qualitative studies can generate huge amounts of data, often requiring a lot of care to manage.

Robson also gives an in-depth discussion on the use of the NUD*IST software for analyzing the large amounts of qualitative data necessarily generated in the course large scale studies such as a doctoral dissertation might entail. But one requires having the software installed in order to fully understand this discussion. I know we have SPSS installed in the computers at the lab. Does anyone know what the situation is regarding NUD*IST, NVivo, or any qualitative research software at that? 

This part describes how we should deal with the data, which is quantitative or qualitative, collected from previous step. SPSS (The Statistical Package for the Social Sciences) is introduced to show how to analyze quantitative data. Before analyzing data each response for question in the questionnaire should be translated into appropriate code then enter these codes in the SPSS. If there is missing data ninety nine or -1 are frequently used instead 0. Data analysis is commonly divided into two broad types such as exploratory and confirmatory. While exploratory analysis explores the data, trying to find out what they tell you confirmatory analysis seeks to establish whether you have actually got what you expected to find. Particularly confirmatory data analysis (CDA) is the mainstream approach in statistical analysis. The main aspects of SPSS are follows.

 -       Significance test : Quantitative analysis is virtually synonymous with significance testing.   What a p value actually tells you is how likely it would be that you would get the difference you did. If the p value is small rather than large, this makes it less likely that the result is due to chance variation rather than to a real difference. In addition the chance of obtaining a statistically significant result increases as the sample size increases.

-       Measures of central tendency : The most commonly used are Mean, Median, and Mode.

-       Measures of variability : Some commonly used measures are Range, Inter-quartile range, Variance, Standard deviation, and Standard error.

-       Deviation : deviation = (individual score) – (mean score)

-       Standardized Score : standardized score = (individual score – mean score)/(standard deviation)

-       The normal distribution: The shape of normal distribution is completely determined once the mean and standard deviation are known. Here median and mode coincide with the mean.

-       Correlation coefficients : This gives an indication of both the strength and the direction of the relationship between the variables.

-       Multiple regression: This is multiple in the sense that it involves a single dependent variable and two or more independent variables.  The regression equation is Y= a + b1x1 + b2x2 where y is the dependent variable, x1 and x2 are the two independent variables, a is the intercept, and b1 and b2 the regression coefficients for the two independent variables.

-       R-squared : This is the multiple coefficient of determination, measure of the proportion of the variance in the dependent variable which is explained by the independent variables in the equation.

-       Factor analysis : This is an approach to making sense of a large number of correlations between variables.  

The author also explains how to analyze qualitative data in chapter 14. Tesch (1990) lists forty-six labels that qualitative researchers have used to describe their approach and reduces these to four basic groupings. These are the characteristics of language, the discovery of regularities, the comprehension of the meaning of text or action, and reflection. Crabtree and Miller (1992) produce a different typology, more closely linked to the method of data analysis used. These are quasi-statistical methods, template approaches, editing approaches, and immersion approaches. Author empathize that the central requirement in qualitative analysis is clear thinking on the part of the analyst. For the qualitative analysis NUD*IST is introduced and author describes how document system and index system can be used, how to make link the document and index system, and how to display data.  

This section covers the analysis of quantitative and qualitative data, and reporting the enquiry. Data analysis can broadly b divided into two main categories: exploratory and confirmatory. Exploratory analysis attempts to find out what can be induced from the data while confirmatory analysis attempts to establish and provide support to is expected. The author presents a clear statement about some key terms including statistical significant, measure of central tendency and variability, analysis between two variables, and relationship between variables. Statistical significant, p value, is the value that tells whether the result is due to chance. If p<0.01 or 0.05, the result is less likely due to change. Meaning, p effect is >0.99 or >0.95, showing there exist an effect.Measure of central tendency and variability includes mean, mode, median, range, variance, standard deviation, standard error. Tools for analyzing relationship between two variables includes cross tabulation using Chi-square tests for distribution.

For hypothesis testing, varieties of tools are employed depending on whether the distribution describes attributes or variables. Analytic tools for parametric normal distributions includes: independent sample T-test, One-way ANOVA, Factorial ANOVA, and GLM MANOVA. Non-parametric (non-normal distribution) analytic tools includes: Pearson Chi-square (test of association), Mann-Whitney U test for two independent samples, Kruskal-Wallis Test for several independent samples, McNemar significance of change  for related sample, Wilcoxon Signed Ranks Test (ordinal) for two related sample, Friedman Test for several related samples.
For relationship between factors, structural equation modelling is used to assess the relationship and to determine goodness of fit for the model.
Analysis of Qualitative Data. I looked at the coding scheme using NUDIST aspect of analysing qualitative data. When creating codes, good place to start is creating a ‘working list’ of codes prior to fieldwork. The list should contain thematic items from the conceptual framework, research questions, hypotheses and problem area. Coding can be done using open coding, axial coding and selective coding.

Under reporting the enquiry, I found the items the author talked about very useful. They reinforced Booth’s et al. guidelines on crafting research. In particular I found the following guidelines helpful: now what is expected, provide an ‘executive summary’ – an abstract, put as much material as possible into appendices, and adhere to professional standard.

Overall, I am grateful to Robson and every author referenced in this book for providing the discipline with this real world research manual. Within this very short time of reading, I was able to learn new ways of discovery and navigate through difficult research formation terrains using some of the guides in this book. I also enjoyed the stimulating discussions held in class. Thanks everyone.

Colin elaborates in the forth part of his textbook the analysis stage for both quantitative and qualitative data. Colin gives a general introduction to readers about the Statistical Package of Social Science (SPSS) and some of the statistical methods that researchers should be aware of it whenever they start their research. Selecting which method to use shouldn’t be arbitrary. However, researchers need to keep in mind the type of research questions they are attempting to answer before selecting the analysis method. After researchers collect their data, they start the data analysis process. Data analysis is divided into two types, exploratory and confirmatory. Exploratory analysis is the one that explores and reveals what the collected data imply, while the confirmatory analysis is the one that establishes whether the researcher got what he/she was expecting to find.

Analyzing qualitative data depends to a great extent on the analyst himself. Colin gives in chapter fourteen an overview for the NUD*IST approach for qualitative data analysis. He also gives a quick insight on NVivo which is the new version of NUD*IST. The main problem that encounters the researcher in qualitative studies is how to confirm his/her findings from the qualitative data? Miles and Huberman answered this question in chapter fourteen where they suggested thirteen tactics for assessing the quality of qualitative data analysis in box.14.9 in page 483. I found this box a very useful reference for researchers to keep in mind whenever they are working on qualitative research. At the end, researchers need to make their research public. At this point, the researchers start the reporting stage where the researcher communicates his/her findings with others (audience) within and outside the field. In this part, Colin emphasized the importance of considering the audience and following the format that most of journals follow to publish papers. Colin gives a checklist for that format in box15.1 in page 505 in his book. I found this box very useful for readers including me because it serves as guidelines for writing a report.

On the whole, I found Colin’s book (Real World Research) very interesting book and it is easy to read. Colin sheds the light on many critical subjects that researchers especially the beginners need to be aware of before starting research to avoid the mistakes that some researchers were trapped in it.

Davis is full of valuable advice for every prospective doctoral student/candidate. From the selection of dissertation topic through to the publication of the research report, it speaks to the essential mindset and strategy necessary to produce a good dissertation within the allowed time limit and consequently launch ourselves into a successful career of scholarly work. I particularly warm to the advice that we consider the dissertation as an integral part of our plan for a scholarly career—the discipline acquired doing the doctoral research will be crucial in that career. On the other hand, if we fail to conclude the dissertation, the planned scholarly career may never really kick off.

Davis advises on the use of a systematic approach geared toward planning and careful management of the whole dissertation process. This approach requires a good appreciation of the hurdles that lie ahead, how we can best draw on the advice and mentoring of the advisor and committee, a good sense of relevant research resources to employ, and the overarching importance of a time management approach to the work involved. Providing statistics on the distributions of dissertation time and size, the book makes clear that dissertation work is serious business and not to be considered as just another term paper. In a word, it cautions that we never take a lackadaisical position regarding our dissertation. Indeed, we are already at our own predissertation stage! 

Like Booth and Robson, this is an invaluable companion for every doctoral student/candidate.

Hint #157 relates to the need for time management. #158 advises that no matter how long we surmise our work to last, it will actually take more time. So, it's always good to brace ourselves for that eventuality. I see a common theme running through the Davis book and these hints.

For our purposes as research students, I think those on “Writing” (#127 – 134) and “Publishing” (#135 – 153) are very handy. I particularly point you guys to #130, “Plagiarism is a Cardinal Sin”. It advises that although it is a serious offence to plagiarize others’ work, it is perfectly alright to do that to oneself. This so-called scholarly rich-get-richer phenomenon can go a long way boosting us psychologically when we are at initial stages of a new work. Limitations apply, though!

In Gray and Drew's, hints 53 and 54 offer very useful tips on grants. As they say in hint 53 (learn grantsmanship), it is a skill like any other. They suggest attending a workshop if necessary. Since it is a skill, it can be acquired, and learned. It also needs practices to master this skill. They mention the powerful 100 (hint 2). Those powerful 100 don't emerge from nowhere. They too have to start their career as a novice. Why do they become the powerful 100 and others don't? One of the reasons is that they manage to master some critical skills that are needed in their academic career.

Hint 54 gives some points on writing a grant proposal. It says that "keep the budget small or at least reasonable". This one is very important. I have experiences in writing grant proposals. The first grant is usually the most difficult one to come by in that you have no track record. Once you get your first grant, it will be a lot easier for you to get your second. Keeping your budget small would make it easier for your grant to be granted. If you have a very big project for a grant, my advice is that you might want to break it into several smaller projects. By doing so, you submit a smaller project with smaller budget for a grant. When you come through with your first small project, your chances will be much better for your subsequent projects. "Don't be modest", OK. But bear in mind that your proposal should sound like feasible.

Another advice of mine is that a researcher should establish close ties with industries. When you have close ties with the industries, you will be able to know what they really need to know about from your research. You will also have the savvy to submit a research proposal that can elicit support from the industries.

Although written in 1997, much of the information in this book still provides a good overview of how to manage the process of dissertation for doctoral students. Davis and Parker start off by providing case studies in the first chapter. The value of this book lies in its suggestions for overcoming real-world road blocks in the journey. Davis and Parker provide concrete tips and examples for dealing with common problems. This book has done a good job by keeping the reader's focus on a manageable dissertation plan. 

However, this book was written with conventional students in mind. For example, if you are enrolled at an open university, you will find that this book lacks related information.

In addition, at some points, the text becomes a tad bit dry. This book does not provide enough information about other significant issues (e.g. the dissertation defense). Furthermore, the book lacks an index which should be very helpful.

Davis and Parker (1997) present a very useful set of advice on the dissertation process for prospective doctoral students. The advice covers issues ranging from selecting an advisor and dissertation committee, selecting dissertation topic, submitting a proposal, working with advisors and dissertation committee, writing and defending the dissertation.

I found advice on selecting an advisor and dissertation committee, and working with them extremely useful. The relationship between the advisor and the prospective doctoral student is that of a senior and junior colleague. Advisors are interested in the topic of interest and competent to advise on the topic, methods, or both. They have reasonable level of expectations; they are consistent in requirements and advice; they view the role as an important responsibility, and above all they are interested in the student as a person and as a scholar.
Prospective doctoral students are willing to do a good dissertation in a reasonable time; they show initiative and accept guidance and follow through on suggestions. They are organized, use advisor’s and committee’s time effectively, and have personal integrity.

Reference
Davis, G. B., & Parker, C. A. (1997). Writing the doctoral dissertation: A systematic approach. Hauppauge, NY: Barron's Educational Series, Inc.

Davis and Parker give many ideas how to approach and manage dissertation to doctoral student or candidate. They explain the objective of a doctoral dissertation which is as follows.

Do independent research

Make a contribution to knowledge with the research

Document the research and make it available to the scholarly community

Also they suggest yearly plan for dissertation from the first year to last year including example that is useful way to prepare dissertation. What I mostly concern part in this book is the selection of a dissertation topic since I know most of student spend a lot of time to find appropriate topic for dissertation. This book introduces characteristics of a good dissertation topic and various ways to find good topic. I learned from this chapter good course work and various experiences can reduce methodology constraints in selecting a topic.

Especially two case studies, introduced in chapter 1, The need for a different approach to the dissertation, make me think carefully how I can arrange this doctoral program effectively. I can choose James Carthly’s way or Ted Maren’s. Although they achieved doctoral degree in different environment, can we say the quality of academic achievement for both case is same?  Maybe not.  It depends on your choice.

Drawing from their combined experience of many years as faculty and administrators, Gray and Drew (2008) assembled a 199 helpful guide for success in academic career – contributing to and getting the most from the academe. The guide covers issues ranging from getting the PhD as a licence to reproduce, landing the first job, determining the culture, teaching and servicing on committee, doing research and applying for tenure, academic remuneration, life as an institutional citizen.

The aspect regarding publications being the only form of portable wealth and not serving in a committee where one is an expert struck me. In spite of teaching being an act of doing public good, publications are the only portable wealth. They are evidence based documents containing selected information and artifacts and establishing significance and activity effectiveness.  Committees, one should refrain from serving in committees where one is an expert because such service might not increase one’s visibility. why? This runs at variance with my cognition.

I found writing hints packed in appendix C very useful. I have started using the reference ever before this assignment blog and I can reflect satisfactorily on my writing progress.

The two books - Davis and Parker (1997) and Gray and Drew (2008 - called my attention to reflect on my learning and teaching philosophy, styles and approaches, and management of my candidature. They are great guides and I will be referencing them from time to time.

Reference
Gray, P. and Drew, D. E. (2008). What they didn't teach you in graduate school: 199 helpful hints for new and future faculty on how to succeed in academic. Sterling, VA: Stylus Publishing.

Davis and Parker gave a very well organized guidance for doctoral students on how to write a good quality dissertation in their book “writing the doctoral dissertation”. The main purpose of their book is to guide the students to a systematic approach that helps them to write their dissertation in short time with good quality. The book also helps the master students to write their master thesis. The authors in the first chapter gave two examples for two doctoral students that show the benefit of being organized and focused on the thesis. Additionally, the authors explained the management approach’s three stages in writing a doctoral dissertation. Those stages are pre-dissertation stage, selecting the dissertation topic, and finally management of research and writing. Moreover, the authors gave a characteristics list for a good dissertation. Basically, any dissertation should contribute to knowledge; this contribution can take many forms such as: new or improved evidence, new or improved methodology, new or improved analysis, and new or improved concepts or theories. In general, this book covers every single issue that any student can face in his/her writing dissertation’s journey.