Dissertation Overview For all details related to dissertation layout, templates, deadlines, checklists etc. http://www.comp.dit.ie/btierney/MScDissertations/index.h tml http://www.comp.dit.ie/btierney/MScDissertations/index.h tml Aim for 100 pages
Dissertation Structure Title Abstract Table of Contents Table of Figures Table of Tables Introduction (starts at page 1) Literature Review (can be separate chapters) Your Design Your Findings Your Evaluation Conclusion and Future Work
Introduction Aim is to introduce the reader to The project Its background Justification that the project is viable Your project aims You project objectives Your research approach Any scope or limitations Overview of the rest of the dissertation 10-15 pages (approx. 10% of the dissertation)
Background/Literature Review Aim is to provide the reader with your insight into the body of literature Introduce key terms, definitions, ideas, thought leaders Provide critical analysis of existing approaches, techniques etc. Identify key ideas, themes, issues or directions that informed your approach Set the scene for why you will be doing things the way you will do things for your project 20-25 pages (approx. 20- 25% of the dissertation) Can be split into multiple chapters
Your Project = 60% of the dissertation Generally you need a chapter for each of the following: Design/Formulation of proposed experimentation/implementation Should be clear how the literature reviewed has influenced design Should be clear why research approach is suitable Experimentation/Implementation Should align with proposed approach Should discuss clearly any deviation/adjustments needed with justification Analysis of your findings Should honestly discuss the outcome of your experimentation Should draw conclusions about your work
Conclusion and Future work Approx. 10% of dissertation Should mirror your introduction Should address how well you have addressed the aim and objectives Should assess where and how well your work aligns with existing research Should discuss scope and limitations Should provide detail about potential future work To build on your experimentation Alternates to your experimentation Other avenues in which your work can be applied Etc.
Issues to be Discussed – Interview Design Justification for use as a tool Relate back to qualitative research Relate to your project State clearly Interview Aims Interview Objectives Relationship to project Choice of Interviewees (audience, sample) Profile Skills Justification of suitability Make transparent any constraints Question design Each question should address certain objectives Outline clearly this relationship Think clearly about the question wording Is it suitable for your interviewees? Is it suitable to elicit the knowledge you need?
Issues to be Discussed – Interview Execution Who Profile, how many etc. When Dates, times, duration Where Location, surroundings, anything that influenced How How long, one to one, recorded, over Skype etc. What Exactly what happened, any issues – hesitancy, lack of understanding of questions, additional questions, suggestions etc. Why For all the above why were things done in this way, why did certain things happen etc.
Issues to be Discussed – Interview Findings Transcribe your interviews You need to include the transcription Not within the main dissertation but as an appendix Can be included on a CD Analyse your data Summarise, organise and extract meaning from interview transcripts Coding Identify Major Themes Align to aims and objectives Of interview Of project To literature Present summary statistics Draw Conclusions Determine key issues to be addressed Identify recommendations Be aware of bias Make it transparent Validate findings and conclusions Expose to some interviewees Look for independent verification – literature, other research tools used Review and revise conclusions to ensure that they are reliable and valid
Bias What is bias? All views of reality are filtered. Bias only exists in relation to some reference point. Types of bias: Motivational bias Interviewee makes accommodations to please the interviewer or some other audience Observational bias Limitations on our ability to accurately observe the world Cognitive bias Mistakes in use of statistics, estimation, memory, etc. Notational bias Terms used to describe a problem may affect our understanding of it
Examples Social pressure response to verbal and non-verbal cues from interviewer Group think response to reactions of other experts Impression management response to imagined reactions of managers, clients,… Wishful thinking response to hopes or possible gains Appropriation selective interpretation to support current beliefs Misrepresentation expert cannot accurately fit a response into the requested response mode Anchoring contradictory data ignored once initial solution is available Inconsistency assumptions made earlier are forgotten Availability some data are easier to recall than others Underestimation of uncertainty tendency to underestimate by a factor of 2 or 3
Terminology Theme a topic that organizes a group of repeating ideas. usually developed during focused coding, but may emerge during literature supported by interview findings E.g. from analysis of interviewee responses, it emerged that that employees are reluctant to use the Wiki as they view the contents as out of date and stagnant. Making conclusions/recommendation a determination of what is working well and what needs to be improved based on repeating ideas and themes. Themes and repeating ideas should guide you in recommending or making improvements. E.g. in response to the view of the Wiki as stagnant and out of date, the project will introduce social media tools alongside the Wiki to encourage more informal knowledge sharing
Issues to be Discussed – Interview Findings Report repeating ideas/issues Repeated by a number of interviewees Report meaningful responses Those that most exemplify issues or support recommendations Quantify these How many people said this? How many disagreed? Why are they best situated to comment? Include quotes Present Graphically Organise themes and conclusions into tables or trees Demonstrate relationships
Interviews Unstructured interview Free flowing, used in early stages of elicitation/research, can produce basics of knowledge domain, basically broad chat Semi-structured interview Main technique Pre-defined questions sent to expert prior to interview, supplementary questions asked at interview. Can be used as part of validation. Structured interview Pre-defined set of questions, can simply be filling in a questionnaire at the interview.
Issues to be Discussed –Design Justification for use as a tool Relate back to qualitative research Relate to your project State clearly Aims Objectives Relationship to project Choice of audience Profile Skills Justification of suitability Size of sample Explain distribution mechanism Make transparent any constraints Question design Each question should address certain objectives Outline clearly this relationship Think clearly about the question wording Is it suitable for your interviewees? Is it suitable to elicit the knowledge you need? Think clearly about the question design Multi-choice, open text etc. Justify How long will it table to complete? Check your grammar, twice (Rule of Thumb – two proofreads gets rid of 95% of errors).
Issues to be Discussed Who Profile, how many targeted, how many responses etc. When Dates, times, duration Where Geographic location How Email, paper etc. What Exactly what happened, any issues –omissions, lack of understanding of questions, additional questions, suggestions etc. Why For all the above why were things done in this way, why did certain things happen etc.
Issues to be Discussed – Survey Findings Collate your findings You need to include the collated responses Not within the main dissertation but as an appendix Can be included on a CD Report summarised responses Analyse your data Summarise, organise and extract meaning from responses Coding Identify Major Themes Align to aims and objectives Of interview Of project To literature Present summary statistics for survey overall Draw Conclusions Determine key issues to be addressed Identify recommendations Be aware of bias Make it transparent Validate findings and conclusions Look for independent verification – literature, other research tools used Review and revise conclusions to ensure that they are reliable and valid
Issues to be Discussed – Survey Findings Present summary statistics for question responses (for key questions) Report repeating ideas/issues Report meaningful responses Those that most exemplify issues or support recommendations Quantify these How many people said this? How many disagreed? Why are they best situated to comment? Include quotes Present Graphically Present outcomes of key questions or question groupings as charts or diagrams Organise themes and conclusions into tables or trees Demonstrate relationships
Questionnaires Keep questions short and simple Avoid questions with “not” Avoid questions with bias Avoid sensitive questions (ask indirectly) Do not ask compound questions, just ask one question at a time e.g. "Do you know what services are available to you and how to find out?"
Questionnaires Likert scales Poor, Weak, O.K., Good, Excellent Very Low, Low, O.K., High, Very High 1, 2, 3, 4, 5
Measure of Central Tendency Central position of a frequency distribution for a group of data Possible measures: mode, median, and mean. Mean: The arithmetic average of a group of scores; the sum of the scores divided by the number of scores. Median The middle score of a sequence of all the scores in a distribution arranged from lowest to highest. Mode The value with the greatest frequency on the distribution
Measures of spread Ways of summarizing a group of data by describing how spread out the results/findings Normal Curve: A specific, mathematically defined, bell-shaped frequency distribution that is symmetrical and unimodal; Normal Distribution: A frequency distribution following a normal curve. Skewness: The extent to which the majority of cases in a frequency distribution fall to one side of the middle. Inter-quartile range: The range of the middle 50 per cent of all scores in a distribution when arranged from lowest to highest. Standard Deviation: A measure of the degree to which scores in a distribution vary from the mean. Variance Another measure of the degree to which scores in a distribution vary from the mean (equal to the standard deviation squared).
Way to Present Data Interval or Ratio Variables Mean and Standard Deviation (if approximately normally distributed) Median and Interquartile Range (if skewed and thus not normally distributed) Histograms Boxplots Stem-and-Leaf Displays Interval variable: A variable with actual values rather than categories e.g. salary Ration variable: As with interval variable but with added characteristic that there is a true zero value e.g. age
Way to Present Data Ordinal or Nominal Mode and/or simple frequencies Barcharts Piecharts Tables Nominal variable: A variable that consists of two or more categories. E.g. male or female Ordinal variable: A variable that consists of categories that can be rank ordered in relation to being 'more' or 'less' of the concept in question. E.g. age ranges 16-20 etc.