EE5801F GroupT Ppt

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    Not everything that counts can be counted,

    and not everything that can be countedcounts -Albert Einstein

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    outline

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    Introduction

    Who developed it?

    Developed by Barney Glaser and AnslemStrauss in 1967 as a general qualitative data

    analysis approach where it is further developed

    to become grounded theory method.

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    IntroductionDefinition:

    It is an inductive data analysis in qualitative research where

    newly collected data is compared with previous data that was

    collected in earlier studies.

    This is done continuously as the theories are forming, and

    consistently being enhanced, then being confirmed or

    disregarded all together when new data surfaces from data

    collection. (Savenye & Robinson, 1996)

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    IntroductionDefinition:

    Meaning the strategy involves taking one piece of

    data and comparing it with all others that may besimilar or different in order to develop

    conceptualization of the possible relations between

    various pieces of data.

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    Where is it applied?

    A1

    Applying constant comparative and discourse analysis

    to virtual worlds research by (Leong, Joseph, &Boulay, 2010)

    A2 The use of information by environmental planners: aqualitative study using grounded methodology by

    Mutshawa, A. (2009)

    A3

    Patients action during their cardiac event: qualitativestudy exploring differences and modifiable factorsby Ruston, A., Clayton, J., & Calnan, M. (1998)

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    Where is it applied?

    A4

    To explore where and how learning takes place in a

    networked leadership and development learningprogramme for SMEs by Peter, S. (2010)

    A5 Tenure and Promotion Considerations: An Analysis of

    Cultural Issues by Ovington, J. et al.

    A6

    The role of schools perceived human resource policies inteachers professional development activities: a comparativestudy of innovations toward competence-based education bySeezink, A., & Poell, R. (2011)

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    Purpose of the articles

    A1

    To show that by combining multiple dataanalysis methods to attain the different typesof research questions.

    RQ: Students reactions to an experimental

    course taught online in Second Life.

    A2

    To examine the use of information byenvironmental planners.

    RQ: Factors that help influence and explainbehaviour exhibited by environmental planners.

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    Purpose of the articles

    A3

    To explore the circumstances and factors thatexplain variations in response to a cardiacevent.

    To identify potentially modifiable factors.

    A4

    To explore where and how learning takesplace in a networked leadership anddevelopment learning programme forSMEs

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    Purpose of the articles

    A5

    To review a selected sample ofliterature on tenure decision-making andpresent a model for productivity analysis

    A6

    This study investigates professional developmentactivities taken by teachers in order to play new roles incompetence-based education programs.

    RQ: Types of professional development activitiesundertaken by teachers, how these activities differamong schools and extent of ability to identify elementsof approach

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    sampling

    A1 Students in a six-week graduate educationaltechnology elective summer course.

    A2 44 officers working or connected to the 5

    councils in Botswana.

    A3 43 patients and 21 other people present at

    the time of the cardiac event.

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    sampling

    A4

    The study uses one cohort of LEAD that

    consisted of 25 small business ownermanagers.

    A5 18 articles, 7 letters to the editor and 5

    faculty handbooks

    A6 30 PVSE teachers from nine different

    schools

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    Data collection

    Article 1

    I. Classroom observations or video recording of

    the use of virtual world in world classsessions.

    II. Students discourse in written text, including

    students blogs, chat box text andasynchronous discussion postings.

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    Data collectionArticle 2

    Interviews with the 44 officers needed to explain onthe environmental issues and planning to:

    1)define general terms and how information use torelate factors

    2) explore the findings in depth,

    3)develop relationship and find example,

    4) search for evidence to refute the finding

    5) have more open discussion to confirm the findings.

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    Data collection

    Article 3

    i. Semi structured face to face interview with

    the patients admitted to two districthospitals for a cardiac event.

    ii. Semi structures face to face interview with

    the patients associates.

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    Data collection

    Article 4

    i. Interviews and observations by the

    researcher to provide information on howLEAD was impacting them both personallyand professionally.

    ii. Information were thematically analysed andidentified, hence used in the networkedlearning.

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    Data collection

    Article 5

    Reviewing literature to assess thespecific requirements in awarding

    tenures

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    Data collection

    Article 5

    Semi-structured interviews (open-ended questions)

    conducted with an interview protocol to explore on:

    i. Respondents background

    ii. Implementation of teachers professional

    development activities, SOAP principles andIPM.

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    Findings & data analysis

    A1

    Via CCA, the salient theme in the studentsreactions were;

    a) students frustration with technical issues andlearning functionalities of virtual worlds,

    b) need for socialization, and c) appreciation for the potential of virtual world for

    teaching and learning.

    The analysis has implication on how students learnin

    the virtual worlds and the changing roles ofinstructorsin virtual worlds as well.

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    Findings & data analysis

    A2

    After CCA, there were 8 main categories

    discovered; motivators, role context,dynamic project information context,experiential knowledge, patent ofinformation use, informational power,

    strategies for participation, and situatedpower.

    The analysis has implication in showing on

    how the information used by the plannersempowerstheir decision and behavioursduring the environmental planning process.

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    Findings & data analysis

    A3

    The interviews were tape recorded. The data were divided into 3 groups

    (1) Non delayers

    (2) Delayers (3) Extended delayers

    The data were then analyzed usingthe constant comparative method

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    Findings & data analysis

    A4

    Five learning spaces wereidentified to show where thedelegates were learning onLEAD, namely:

    LEAD, 2. reflective 3. Social 4.Peer-to-peer and 5.future

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    Findings & data analysis

    A5

    Through CCA, the factors that universityadministrators need to evaluate beforeawarding of tenure and promotion arecategorized for example; the candidatesability to increase competence, size offaculty, organizational structure, contextvariables, and teaching versus researchcommitment

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    Findings & data analysis

    A6

    Interviews were fully transcribed, followed by acoding procedure where core concepts were codedinto 11 main categories and 62 sub categories.

    Data collected were compared between categories

    The analysis has implication on views of teachers onprofessional and career development, theirmotivation to participation in development activities

    and actual learning in the process.

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    Conclusion

    Strengths Weaknesse

    Effectiveness

    CCA is useful in

    identifying importantand prominentthemes or conceptsfrom a broad

    exploratoryapproach but

    Rigidity of data

    due to the use of

    multiple coders anddata sources somewill disregard therigor of data

    collection andanalysis. (Leong etal., 2010)

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    Conclusion

    Strengths Weakness

    Generates analytical

    data whichcontribute tounderstanding

    Time consuming

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    Conclusion

    On Etic versus Emic dilemma

    Most studies assumed to put emphasis on emicapproach (Douglas, 2003)

    but sometimes there are failures by theresearchers to differentiate between these twotypes of approach when interpreting data causingconfusion in coding/conception of data categories,making it unreliable for generalization. (Zubaran,2009)