Searching for "research"

Cohort 8 research and write dissertation

When writing your dissertation…

Please have an FAQ-kind of list of the Google Group postings regarding resources and information on research and writing of Chapter 2

digital resource sets available through MnPALS Plus

http://blog.stcloudstate.edu/ims/2017/10/21/digital-resource-sets-available-through-mnpals-plus/ 

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[how to] write chapter 2

You were reminded to look at dissertations of your peers from previous cohorts and use their dissertations as a “template”: http://repository.stcloudstate.edu/do/discipline_browser/articles?discipline_key=1230

You also were reminded to use the documents in Google Drive: e.g. https://drive.google.com/open?id=0B7IvS0UYhpxFVTNyRUFtNl93blE

Please have also materials, which might help you organize our thoughts and expedite your Chapter 2 writing….

Do you agree with (did you use) the following observations:

The purpose of the review of the literature is to prove that no one has studied the gap in the knowledge outlined in Chapter 1. The subjects in the Review of Literature should have been introduced in the Background of the Problem in Chapter 1. Chapter 2 is not a textbook of subject matter loosely related to the subject of the study.  Every research study that is mentioned should in some way bear upon the gap in the knowledge, and each study that is mentioned should end with the comment that the study did not collect data about the specific gap in the knowledge of the study as outlined in Chapter 1.

The review should be laid out in major sections introduced by organizational generalizations. An organizational generalization can be a subheading so long as the last sentence of the previous section introduces the reader to what the next section will contain.  The purpose of this chapter is to cite major conclusions, findings, and methodological issues related to the gap in the knowledge from Chapter 1. It is written for knowledgeable peers from easily retrievable sources of the most recent issue possible.

Empirical literature published within the previous 5 years or less is reviewed to prove no mention of the specific gap in the knowledge that is the subject of the dissertation is in the body of knowledge. Common sense should prevail. Often, to provide a history of the research, it is necessary to cite studies older than 5 years. The object is to acquaint the reader with existing studies relative to the gap in the knowledge and describe who has done the work, when and where the research was completed, and what approaches were used for the methodology, instrumentation, statistical analyses, or all of these subjects.

If very little literature exists, the wise student will write, in effect, a several-paragraph book report by citing the purpose of the study, the methodology, the findings, and the conclusions.  If there is an abundance of studies, cite only the most recent studies.  Firmly establish the need for the study.  Defend the methods and procedures by pointing out other relevant studies that implemented similar methodologies. It should be frequently pointed out to the reader why a particular study did not match the exact purpose of the dissertation.

The Review of Literature ends with a Conclusion that clearly states that, based on the review of the literature, the gap in the knowledge that is the subject of the study has not been studied.  Remember that a “summary” is different from a “conclusion.”  A Summary, the final main section, introduces the next chapter.

from http://dissertationwriting.com/wp/writing-literature-review/

Here is the template from a different school (then SCSU)

http://semo.edu/education/images/EduLead_DissertGuide_2007.pdf 

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When conducting qualitative data, how many people should be interviewed? Is there a minimum or a max

Here is my take on it:

Simple question, not so simple answer.

It depends.

Generally, the number of respondents depends on the type of qualitative inquiry: case study methodology, phenomenological study, ethnographic study, or ethnomethodology. However, a rule of thumb is for scholars to achieve saturation point–that is the point in which no fresh information is uncovered in response to an issue that is of interest to the researcher.

If your qualitative method is designed to meet rigor and trustworthiness, thick, rich data is important. To achieve these principles you would need at least 12 interviews, ensuring your participants are the holders of knowledge in the area you intend to investigate. In grounded theory you could start with 12 and interview more if your data is not rich enough.

In IPA the norm tends to be 6 interviews.

You may check the sample size in peer reviewed qualitative publications in your field to find out about popular practice. In all depends on the research problem, choice of specific qualitative approach and theoretical framework, so the answer to your question will vary from few to few dozens.

How many interviews are needed in a qualitative research?

There are different views in literature and no one agreed to the exact number. Here I reviewed some mostly cited references. Based Creswell (2014), it is estimated that 16 participants will provide rich and detailed data. There are a couple of researchers agreed ‎on 10–15 in-depth interviews ‎are ‎sufficient ‎‎ (Guest, Bunce & Johnson 2006; Baker & ‎Edwards 2012).

your methodological choices need to reflect your ontological position and understanding of knowledge production, and that’s also where you can argue a strong case for smaller qualitative studies, as you say. This is not only a problem for certain subjects, I think it’s a problem in certain departments or journals across the board of social science research, as it’s a question of academic culture.

here more serious literature and research (in case you need to cite in Chapter 3)

Sample Size and Saturation in PhD Studies Using Qualitative Interviews

http://www.qualitative-research.net/index.php/fqs/article/view/1428/3027

https://researcholic.wordpress.com/2015/03/20/sample_size_interviews/

Gaskell, George (2000). Individual and Group Interviewing. In Martin W. Bauer & George Gaskell (Eds.), Qualitative Researching With Text, Image and Sound. A Practical Handbook (pp. 38-56). London: SAGE Publications.

Lieberson, Stanley 1991: “Small N’s and Big Conclusions.” Social Forces 70:307-20. (http://www.jstor.org/pss/2580241)

Savolainen, Jukka 1994: “The Rationality of Drawing Big Conclusions Based on Small Samples.” Social Forces 72:1217-24. (http://www.jstor.org/pss/2580299).

Small, M.(2009) ‘How many cases do I need ? On science and the logic of case selection in field-based research’ Ethnography 10(1) 5-38

Williams,M. (2000) ‘Interpretivism and generalisation ‘ Sociology 34(2) 209-224

http://james-ramsden.com/semi-structured-interviews-how-many-interviews-is-enough/

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how to start your writing process

If you are a Pinterest user, you are welcome to just sbuscribe to the board:

https://www.pinterest.com/aidedza/doctoral-cohort/

otherwise, I am mirroring the information also in the IMS blog:

http://blog.stcloudstate.edu/ims/2017/08/13/analytical-essay/ 

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APA citing of “unusual” resources

http://blog.stcloudstate.edu/ims/2017/08/06/apa-citation/

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statistical modeling: your guide to Chapter 3

working on your dissertation, namely Chapter 3, you probably are consulting with the materials in this shared folder:

https://drive.google.com/drive/folders/0B7IvS0UYhpxFVTNyRUFtNl93blE?usp=sharing

In it, there is a subfolder, called “stats related materials”
https://drive.google.com/open?id=0B7IvS0UYhpxFcVg3aWxCX0RVams

where you have several documents from the Graduate school and myself to start building your understanding and vocabulary regarding your quantitative, qualitative or mixed method research.

It has been agreed that before you go to the Statistical Center (Randy Kolb), it is wise to be prepared and understand the terminology as well as the basics of the research methods.

Please have an additional list of materials available through the SCSU library and the Internet. They can help you further with building a robust foundation to lead your research:

http://blog.stcloudstate.edu/ims/2017/07/10/intro-to-stat-modeling/

In this blog entry, I shared with you:

  1. Books on intro to stat modeling available at the library. I understand the major pain borrowing books from the SCSU library can constitute, but you can use the titles and the authors and see if you can borrow them from your local public library
  2. I also sought and shared with you “visual” explanations of the basics terms and concepts. Once you start looking at those, you should be able to further research (e.g. YouTube) and find suitable sources for your learning style.

I (and the future cohorts) will deeply appreciate if you remember to share those “suitable sources for your learning style” either by sharing in this Google Group thread and/or sharing in the comments section of the blog entry: http://blog.stcloudstate.edu/ims/2017/07/10/intro-to-stat-modeling.  Your Facebook group page is also a good place to discuss among ourselves best practices to learn and use research methods for your chapter 3.

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search for sources

Google just posted on their Facebook profile a nifty short video on Google Search
http://blog.stcloudstate.edu/ims/2017/06/26/google-search/

Watching the video, you may remember the same #BooleanSearch techniques from our BI (bibliography instruction) session of last semester.

Considering the fact of preponderance of information in 2017: your Chapter 2 is NOT ONLY about finding information regrading your topic.
Your Chapter 2 is about proving your extensive research of the existing literature.

The techniques presented in the short video will arm you with methods to dig deeper and look further.

If you would like to do a decent job exploring all corners of the vast area called Internet, please consider other search engines similar to Google Scholar:

Microsoft Semantic Scholar (Semantic Scholar); Microsoft Academic Search; Academicindex.net; Proquest Dialog; Quetzal; arXiv;

https://www.google.com/; https://scholar.google.com/ (3 min); http://academic.research.microsoft.com/http://www.dialog.com/http://www.quetzal-search.infohttp://www.arXiv.orghttp://www.journalogy.com/
More about such search engines in the following blog entries:

http://blog.stcloudstate.edu/ims/2017/01/19/digital-literacy-for-glst-495/

and

http://blog.stcloudstate.edu/ims/2017/05/01/history-becker/

Let me know, if more info needed and/or you need help embarking on the “deep” search

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tips for writing and proofreading

please have several infographics to help you with your writing habits (organization) and proofreading, posted in the IMS blog:

https://blog.stcloudstate.edu/ims/2017/06/11/writing-first-draft/
https://blog.stcloudstate.edu/ims/2017/06/11/prewriting-strategies/ 

https://blog.stcloudstate.edu/ims/2017/06/11/essay-checklist/

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letter – request copyright permission

Here are several samples on mastering such letter:

https://registrar.stanford.edu/students/dissertation-and-thesis-submission/preparing-engineer-theses-paper-submission/sample-3

http://www.iup.edu/graduatestudies/resources-for-current-students/research/thesis-dissertation-information/before-starting-your-research/copyright-permission-instructions-and-sample-letter/

https://brocku.ca/webfm_send/25032

 

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qualitative method research

Cohort 7

By miltenoff | View this Toon at ToonDoo | Create your own Toon

Qualitative Method Research

quote

Data treatment and analysis

Because the questionnaire data comprised both Likert scales and open questions, they were analyzed quantitatively and qualitatively. Textual data (open responses) were qualitatively analyzed by coding: each segment (e.g. a group of words) was assigned to a semantic reference category, as systematically and rigorously as possible. For example, “Using an iPad in class really motivates me to learn” was assigned to the category “positive impact on motivation.” The qualitative analysis was performed using an adapted version of the approaches developed by L’Écuyer (1990) and Huberman and Miles (1991, 1994). Thus, we adopted a content analysis approach using QDAMiner software, which is widely used in qualitative research (see Fielding, 2012; Karsenti, Komis, Depover, & Collin, 2011). For the quantitative analysis, we used SPSS 22.0 software to conduct descriptive and inferential statistics. We also conducted inferential statistics to further explore the iPad’s role in teaching and learning, along with its motivational effect. The results will be presented in a subsequent report (Fievez, & Karsenti, 2013)

Fievez, A., & Karsenti, T. (2013). The iPad in Education: uses, benefits and challenges. A survey of 6057 students and 302 teachers in Quebec, Canada (p. 51). Canada Research Chair in Technologies in Education. Retrieved from https://www.academia.edu/5366978/The_iPad_in_Education_uses_benefits_and_challenges._A_survey_of_6057_students_and_302_teachers_in_Quebec_Canada

unquote

 The 20th century notion of conducting a qualitative research by an oral interview and then processing manually your results had triggered in the second half of the 20th century [sometimes] condescending attitudes by researchers from the exact sciences.
The reason was the advent of computing power in the second half of the 20th century, which allowed exact sciences to claim “scientific” and “data-based” results.
One of the statistical package, SPSS, is today widely known and considered a magnificent tools to bring solid statistically-based argumentation, which further perpetuates the superiority of quantitative over qualitative method.
At the same time, qualitative researchers continue to lag behind, mostly due to the inertia of their approach to qualitative analysis. Qualitative analysis continues to be processed in the olden ways. While there is nothing wrong with the “olden” ways, harnessing computational power can streamline the “olden ways” process and even present options, which the “human eye” sometimes misses.
Below are some suggestions, you may consider, when you embark on the path of qualitative research.
The Use of Qualitative Content Analysis in Case Study Research
Florian Kohlbacher
http://www.qualitative-research.net/index.php/fqs/article/view/75/153

excellent guide to the structure of a qualitative research

Palys, T., & Atchison, C. (2012). Qualitative Research in the Digital Era: Obstacles and Opportunities. International Journal Of Qualitative Methods, 11(4), 352-367.
http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d89171709%26site%3dehost-live%26scope%3dsite
Palys and Atchison (2012) present a compelling case to bring your qualitative research to the level of the quantitative research by using modern tools for qualitative analysis.
1. The authors correctly promote NVivo as the “jaguar’ of the qualitative research method tools. Be aware, however, about the existence of other “Geo Metro” tools, which, for your research, might achieve the same result (see bottom of this blog entry).
2. The authors promote a new type of approach to Chapter 2 doctoral dissertation and namely OCR-ing PDF articles (most of your literature as of 2017 is mostly either in PDF or electronic textual format) through applications such as
Abbyy Fine Reader, https://www.abbyy.com/en-us/finereader/
OmniPage,  http://www.nuance.com/for-individuals/by-product/omnipage/index.htm
Readirus http://www.irislink.com/EN-US/c1462/Readiris-16-for-Windows—OCR-Software.aspx
The text from the articles is processed either through NVIVO or related programs (see bottom of this blog entry). As the authors propose: ” This is immediately useful for literature review and proposal writing, and continues through the research design, data gathering, and analysis stages— where NVivo’s flexibility for many different sources of data (including audio, video, graphic, and text) are well known—of writing for publication” (p. 353).
In other words, you can try to wrap your head around huge amount of textual information, but you can also approach the task by a parallel process of processing the same text with a tool.
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Here are some suggestions for Computer Assisted / Aided Qualitative Data Analysis Software (CAQDAS) for a small and a large community applications):

– RQDA (the small one): http://rqda.r-forge.r-project.org/ (see on youtube the tutorials of Metin Caliskan); one active developper.
GATE (the large one): http://gate.ac.uk/ | https://gate.ac.uk/download/

text mining: https://en.wikipedia.org/wiki/Text_mining
Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output.
https://ischool.syr.edu/infospace/2013/04/23/what-is-text-mining/
Qualitative data is descriptive data that cannot be measured in numbers and often includes qualities of appearance like color, texture, and textual description. Quantitative data is numerical, structured data that can be measured. However, there is often slippage between qualitative and quantitative categories. For example, a photograph might traditionally be considered “qualitative data” but when you break it down to the level of pixels, which can be measured.
word of caution, text mining doesn’t generate new facts and is not an end, in and of itself. The process is most useful when the data it generates can be further analyzed by a domain expert, who can bring additional knowledge for a more complete picture. Still, text mining creates new relationships and hypotheses for experts to explore further.

quick and easy:

intermediate:

advanced:

http://tidytextmining.com/

Introduction to GATE Developer  https://youtu.be/o5uhMF15vsA


 

use of RapidMiner:

https://rapidminer.com/pricing/

– Coding Analysis Toolkit (CAT) from University of Pittsburgh and University of Massachusetts
– Raven’s Eye is an online natural language ANALYSIS tool based
– ATLAS.TI
– XSIGTH

– QDA Miner: http://provalisresearch.com/products/qualitative-data-analysis-software/

There is also a free version called QDA Miner Lite with limited functionalities: http://provalisresearch.com/products/qualitative-data-analysis-software/freeware/

– MAXQDA

–  NVivo

– SPSS Text Analytics

– Kwalitan

– Transana (include video transcribing capability)

– XSight

– Nud*ist

(Cited from: https://www.researchgate.net/post/Are_there_any_open-source_alternatives_to_Nvivo [accessed Apr 1, 2017].

– OdinText

IBM Watson Conversation
IBM Watson Text to Speech
Google Translate API
MeTA
LingPipe
NLP4J
Timbl
Colibri Core
CRF++
Frog
Ucto
– CRFsuite

– FoLiA
PyNLPl
openNLP
NLP Compromise
MALLET
Cited from: https://www.g2crowd.com/products/nvivo/competitors/alternatives [accessed April 1, 2017
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more on quantitative research:

Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
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literature on quantitative research:
Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press. https://mplus.mnpals.net/vufind/Record/ebr4_1006438
St. Cloud State University MC Main Collection – 2nd floor AZ195 .B66 2015
p. 161 Data scholarship in the Humanities
p. 166 When Are Data?
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015

limitations and delimitations in research

Shortly:
Limitations are influences that the researcher cannot control.   They are the shortcomings, conditions or influences that cannot be controlled by the researcher that place restrictions on your methodology and conclusions. Any limitations that might influence the results should be mentioned.
Delimitations are choices made by the researcher which should be mentioned. They describe the boundaries that you have set for the study.
Assumptions are accepted as true, or at least plausible, by researchers and peers who will read your dissertation or thesis.

More:
https://www.bcps.org/offices/lis/researchcourse/develop_writing_methodology_limitations.html

https://www.phdstudent.com/Choosing-a-Research-Design/stating-the-obvious-writing-assumptions-limitations-and-delimitations/Page-2

http://dissertationrecipes.com/wp-content/uploads/2011/04/AssumptionslimitationsdelimitationsX.pdf

Dissertation Guidelines
http://www.regent.edu/acad/schedu/pdfs/residency/su09/dissertation_guidelines.pdf

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more on dissertation research in this IMS blog
http://blog.stcloudstate.edu/ims?s=dissertation+research

social media research toolkit

thank you, Greg Jorgensen, an excellent list of tools for analytics + excellent background info (price, social media tools served, output format

Social Media Research Toolkit – Peer Tested & Peer Reviewed

Social Media Research Toolkit

Gephi, Hootsuite, NodeXL, Sysomos, Gnip, Issuecrawler, Brandwatch, Netvizz, Datasift, Crimson Hexagon, tweepy, streamR, Twitoxmy, Digmind, Twitris, yourTwapperKeeper, DiscoverText, Webometric Analyst, python-twitter, twurl, Tweet Archivist, vtracker, Netlytic, twython, OutWit Hub, Mozdeh, Affinio, Rfacebook, Facepager, Flocker, 140dev, Sodato, Foller.me, Textexture, Hosebird, Websta, followthehashtag, Chorus, VOSON/Uberlink, Info Extractor, twarc, iScience Maps, Social Feed Manager, facebook-sdk, Socioviz, Naoyun, Visibrain Focus, TwitterGoogles, DD-CSS, YouTube Data Tools, SocialMediaMineR, tStreamingArchiver, Twitter Stream Downloader

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more on social media analytics in this IMS blog
http://blog.stcloudstate.edu/ims?s=social+media+analytics
more on social media management in this blog
http://blog.stcloudstate.edu/ims?s=social+media+management
more on altmetrics in this blog
http://blog.stcloudstate.edu/ims?s=altmetrics

research how to

also: http://bit.ly/edad829

Are Q&A startups a threat to Google?

search

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– The Internet

– Google Scholar https://scholar.google.com/

  • web sites (Web 1.0)

– blogs, listservs etc (Web 2.0)

– social media

– YouTube https://www.youtube.com/ and similar

– e.g. SCSU streaming : http://www.stcloudstate.edu/library/research/video.aspx

– Q&A plaforms such as Quora https://www.quora.com/, AskScience https://www.reddit.com/r/askscience/, Medium, PeerPong and similar

– Reddit https://www.reddit.com/, Digg http://digg.com/ , StackExchange http://stackexchange.com/ , Mahalo CompanyKngine.com   and similar

– Google Search, Yahoo Answers and similar

– Wikipedia

– Facebook groups, LinkedIn groups and similar

– SlideShare https://www.slideshare.net/  and similar

 

 

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more on the research process in this IMS blog:
http://blog.stcloudstate.edu/ims?s=search 

Save

pedagogical research elearning

What Does Recent Pedagogical Research Tell Us About eLearning Good Practice?

Many instructors indicate that they want their elearning teaching approaches to be evidence-based. Indeed, there are rich and varied sources of research being conducted on elearning good practices available in scholarly journals and government reports. However, few of us have time to keep up with these publications. In this session Christina Petersen will do some of that work for you. She summarize findings from recent government and university reports which review over 1,000 online learning studies. Additionally, she will summarize the findings from newly published articles from pedagogical journals with important information about good practices in online education. These practices address evidence-based methods for promoting student engagement in online courses, good practices for video production, and other topics related to online teaching. We will discuss the importance of all of these findings for your teaching.

Christina Petersen is an Education Program Specialist in the Center for Educational Innovation at the University of Minnesota where she partners with faculty and departments to help create and redesign courses and curriculum to promote maximal student learning. She facilitates a monthly Pedagogical Innovations Journal Club at the CEI. She has a PhD in Pharmacology and her teaching experience includes undergraduate courses in Pharmacology, and graduate courses in Higher Education pedagogy. Her teaching interests include integrating active learning into science courses, teaching in active learning classrooms, and evidence-based teaching practice. She is co-author of a soon-to-be-released book from Stylus, “A Guide to Teaching in Active Learning Classrooms”

View the eLearning Summit presentation

WebEx link for the webinar
Date: Thursday, December 1, 2016
Time: 2:00 p.m., Central Daylight Time (Chicago, GMT-05:00)
Session number: 805 333 130
Session Password: MNLC@2016

Teleconference information

To receive a call back, provide your phone number when you join the training session. Alternatively, you can call one of the following numbers and enter the access code:

Call-in toll-free number: 888-742-5095
(US) Call-in number: 619-377-3319
(US) Conference Code: 297 345 8873
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more on elearning in this IMS blog:

http://blog.stcloudstate.edu/ims?s=elearning

research on blended learning

Discussion on the EDUCAUSE Blended and Online Learning Group’s listserv

Question:\

I head an instructional design unit and we’ve been noticing that instructors with no experience in online teaching seem to struggle to teach in a blended environment. They get easily confused about 1) how to decide what content is best suited for in class and what goes online and 2) they also have difficulty bridging the two modalities to create a seamless and rich learning environment.

Rema Nilakanta, Ph.D., Director of Design and Delivery   Engineering-LAS Online Learning 1328 Howe Hall 537 Bissell Rd  P      515-294-9259        F      515-294-6184        W     http://www.elo.iastate.edu 

Answers:

Oregon State University has a hybrid course design program that is a partnership between OSU’s Ecampus and our Center for Teaching and Learning. You can find quite a few resources here: http://ctl.oregonstate.edu/hybrid-learning

Shannon Riggs Director, Course Development and Training Oregon State University Ecampus 4943 Valley Library Corvallis, OR 97331-4504 541.737.2613

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http://onlinelearningconsortium.org/consult/olc-quality-scorecard-blended-learning-programs/

Jennifer Mathes, Ph.D. Director of Strategic Partnerships Online Learning Consortium Office: (781) 583-7571 Mobile: (913) 226-4977 Email:  jennifer.mathes@onlinelearning-c.org Website:  http://www.onlinelearning-c.org Skype:     mathes.olc

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You might find my recent book The Blended Course Design Workbook: A Practical Guide to be a helpful resource. Each chapter has a literature review of the relevant research as well as activities to guide faculty through the various components of blended course design. You can read the first chapter on the fundamentals of blended teaching and learning at the publisher website. The book also has a companion website with additional resources here: http://www.bcdworkbook.com.

Katie Linder Research Director Extended Campus, Oregon State University 4943 The Valley Library Corvallis, Oregon 97331  Phone 541-737-4629 | Fax 541-737-2734 Email: kathryn.linder@oregonstate.edu Twitter: @ECResearchUnit & @RIA_podcast Check out the Research in Action podcast: ecampus.oregonstate.edu/podcast

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more on blended learning in this IMS blog:
http://blog.stcloudstate.edu/ims?s=blended+learning

research and literature review

Roberts, C. (2010). The Dissertation Journey. A Practical and Comprehensive Guide to Planing, Writing, and Defending Your Dissertation. Corwin, Thousand Oaks, CA.

Chapter 9.

Purpose and scope

We talked about “themes” and the need to be careful with breaking them into “subthemes”: if you do a historical overview, avoid chunking it into “dates” and rather keep the thematic relation. Make sure that the relate to your topic; that’s why it is good to keep your title (even if preliminary), outline (even if in progress), thesis (even if under work) etc. on the first page of your Chapter 2 manuscript / draft.

  1. 87

Writing a conceptual framework from Wylie Tidwell, III

Formulate your research question / thesis
https://drive.google.com/open?id=0B7IvS0UYhpxFNGhCZ01fWTBzSjg

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