Introduction to quantitative and qualitative research Dr Liz
Introduction to quantitative and qualitative research Dr Liz FitzGerald Institute of Educational Technology Research and research methods Research methods are split broadly into quantitative and qualitative methods Which you choose will depend on your research questions your underlying philosophy of research your preferences and skills Basic principles of research design
Four main features of research design, which are distinct, but closely related Ontology: How you, the researcher, view the world and the assumptions that you make about the nature of the world and of reality Epistemology: The assumptions that you make about the best way of investigating the world and about reality Methodology: The way that you group together your research techniques to make a coherent picture Methods and techniques: What you actually do in order to collect your data and carry out your investigations These principles will inform which methods you choose: you need to understand how they fit with your bigger picture of the world, and how you choose to investigate it, to ensure that your work will be coherent and effective Four main schools of ontology
(how we construct reality) Ontology Realism Internal Realism Relativism Nominalism Summary The world is real, and
science proceeds by examining and observing it The world is real, but it is almost impossible to examine it directly Scientific laws are basically created by people to fit their view of reality Reality is entirely
created by people, and there is no external truth Truth There is a single truth Truth exists, but is obscure There are many truths
There is no truth Facts Facts exist, and can be revealed through experiments Facts are concrete, but cannot always be revealed Facts depend on the viewpoint of the
observer Facts are all human creations However, none of these positions are absolutes. They are on a continuum, with overlaps between them. Epistemology i.e. the way in which you choose to investigate the world Two main schools are positivism and social constructionism: Positivists believe that the best way to investigate the world is through objective methods, such as observations. Positivism fits within a realist ontology.
Social constructionists believe that reality does not exist by itself. Instead, it is constructed and given meaning by people. Their focus is therefore on feelings, beliefs and thoughts, and how people communicate these. Social constructionism fits better with a relativist ontology. Methodology Epistemology and ontology will have implications for your methodology Realists tend to have positivist approach tend to gather quantitative sources of data Relativists tend to have a social constructionist approach tend to gather qualitative sources of data Remember these are not absolutes! People tend to work on
a continuum role for mixed methods and approaches Also consider the role of the researcher*: internal/external; involved or detached? * See also Adams, Anne; FitzGerald, Elizabeth and Priestnall, Gary (2013). Of catwalk technologies and boundary creatures. ACM Transactions on Computer-Human Interaction (TOCHI), 20(3), article no. 15. http://oro.open.ac.uk/35323/ A note about data Quantitative data is about quantities, and therefore numbers Qualitative data is about the nature of the thing investigated, and tends to be words rather than numbers Difference between primary and secondary data
sources Be aware of research data management practices and archives of data sets (both in terms of downloading and uploading) Choosing your approach Your approach may be influenced by your colleagues views, your organisations approach, your supervisors beliefs, and your own experience There is no right or wrong answer to choosing your research methods Whatever approach you choose for your research, you need to consider five questions: What is the unit of analysis? For example, country, company or individual. Are you relying on universal theory or local knowledge? i.e. will your results be generalisable, and produce universally applicable results, or are there local factors that will affect your results? Will theory or data come first? Should you read the literature first, and then develop your theory,
or will you gather your data and develop your theory from that? (N.B. this will likely be an iterative process) Will your study be cross-sectional or longitudinal? Are you looking at one point in time, or changes over time? Will you verify or falsify a theory? You cannot conclusively prove any theory; the best that you can do is find nothing that disproves it. It is therefore easier to formulate a theory that you can try to disprove, because you only need one wrong answer to do so. Quantitative approaches Attempts to explain phenomena by collecting and analysing numerical data Tells you if there is a difference but not necessarily why Data collected are always numerical and analysed using statistical methods
Variables are controlled as much as possible (RCD as the gold standard) so we can eliminate interference and measure the effect of any change Randomisation to reduce subjective bias If there are no numbers involved, its not quantitative Some types of research lend themselves better to quant approaches than others Quantitative data Data sources include Surveys where there are a large number of respondents (esp where you have used a Likert scale) Observations (counts of numbers and/or coding
data into numbers) Secondary data (government data; SATs scores etc) Analysis techniques include hypothesis testing, correlations and cluster analysis Black swans and falsifiability Falsifiability or refutability of a statement, hypothesis, or theory is the inherent possibility that it can be proven false Karl Popper and the black swan; https://www.flickr.com/photos/lselibrary/ deductive c.f. inductive reasoning
IMAGELIBRARY/5 Hypothesis testing Start with null hypothesis i.e. H0 that there will be no difference CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=1243220 Type I and Type II errors Analysing quant data Always good to group and/or visualise the data initially outliers/cleaning data What average are you looking for? Mean, median or mode?
Spread of data: skewness/distribution range, variance and standard deviation What are you looking for? Trying to find the signal from the noise Generally, either a difference (between/within groups) or a correlation Choosing the right test to use: parametric vs non-parametric (depends what sort of data you have interval/ratio vs nominal/ordinal and how it is distributed) Correlation does not imply causation!
Example correlations From Spurious correlations website http:// www.tylervigen.com/spuri ous-correlations Interpreting test statistics Significance level a fixed probability of wrongly rejecting the null hypothesis H0, if it is in fact true. Usually set to 0.05 (5%). p value - probability of getting a value of the test statistic as extreme as or more extreme than that observed by
chance alone, if the null hypothesis H0, is true. Power ability to detect a difference if there is one Effect size numerical way of expressing the strength or magnitude of a reported relationship, be it causal or not Example of quant data/analysis* Matched users were those who learning styles were matched with the lesson plan e.g. sequential users with a sequential lesson plan. Mismatched participants used a lesson plan that was not matched to their learning style, e.g. sequential users with a global lesson plan. H0 there will be no statistically significant difference in knowledge gained between users from different experimental groups H1 students who learn in a matched environment will learn
significantly better than those who are in mismatched environment H2 students who learn in a mismatched environment will learn significantly worse than those who learn in a matched environment * Case study taken from: Brown, Elizabeth (2007) The use of learning styles in adaptive hypermedia. PhD thesis, University of Nottingham. http://eprints.nottingham.ac.uk/10577/ Interpreting test statistics Statistical testing was carried out using a univariate ANOVA in SPSS, to determine if there was any significant difference in knowledge gained. Initial conjecture suggests that the mismatched group actually performed better than the matched group. However, the difference between the two groups was not significant (F(1,80)=0.939, p=0.34, partial eta squared = 0.012) and hence
hypotheses 1 and 2 can be rejected. What quant researchers worry about Is my sample size big enough? Have I used the correct statistical test? have I reduced the likelihood of making Type I and/or Type II errors? Are my results generalisable? Are my results/methods/results reproducible? Am I measuring things the right way? Whats wrong with quant research? Some things cant be measured or measured accurately
Doesnt tell you why Can be impersonal no engagement with human behaviours or individuals Data can be static snapshots of a point in time Can tell a version of the truth (or a lie?) Lies, damned lies and statistics persuasive power of numbers Qualitative approaches Any research that doesnt involve numerical data Instead uses words, pictures, photos, videos, audio recordings. Field notes, generalities. Peoples own words. Tends to start with a broad question rather than
a specific hypothesis Develop theory rather than start with one inductive rather than deductive Gathering qual data Tends to yield rich data to explore how and why things happened Dont need large sample sizes (in comparison to quantitative research) Some issues may arise, such as Respondents providing inaccurate or false information or saying what they think the researcher wants to hear Ethical issues may be more problematic as the researcher is usually closer to participants
Researcher objectivity may be more difficult to achieve Sources of qual data Interviews (structured, semi-structured or unstructured) Focus groups Questionnaires or surveys Secondary data, including diaries, self-reporting, written accounts of past events/archive data and company reports;
Direct observations may also be recorded (video/audio) Ethnography Analysing qual data Content analysis Grounded analysis
Social network analysis (can also be quant) Discourse analysis Narrative analysis Conversation analysis Example of qual data research* Describing and comparing two types of audio guides: personled and technology-led Geolocated audio to enable public, informal learning of historical events Data sources: questionnaires, researcher observations, and small focus groups
* Taken from: FitzGerald, Elizabeth; Taylor, Claire and Craven, Michael (2013). To the Castle! A comparison of two audio guides to enable public discovery of historical events. Personal and Ubiquitous Computing, 17(4) pp. 749760. http://oro.open.ac.uk/35077/ Data analysis and findings Comparison of the two different walks Differences/similarities of the walks Issues surrounding participant engagement Thematic analysis Mode of delivery Number of participants and social interactions Geographical affordances of places and locations User experience
Opportunities for learning Other factors Findings, lessons learned, recommendations What qual researchers worry about Have I coded my data correctly? Have I managed to capture the situation in a realistic manner? Have I described the context in sufficient detail? Have I managed to see the world through the eyes of my participants? Is my approach flexible and able to change?
Whats wrong with qual research? It can be very subjective It cant always be repeated It cant always be generalisable It cant always give you definite answers in the way that quantitative research can It can be easier to carry out (or hide) bad (poor quality) qual research than bad quant
research Other aspects of research design Validity Reliability Trustworthiness* Dependability: showing that the findings are consistent and could be repeated Confirmability: a degree of neutrality or the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest Credibility: confidence in the 'truth' of the findings Transferability: showing that the findings have applicability in other contexts
* See Lincoln, YS. & Guba, EG. (1985). Naturalistic Inquiry. Newbury Park, CA: Sage Publications. Summary The type of approach you choose will be determined by your research question, your epistemological and ontological stances and your skills or ability to utilise a certain appoach For most people in ed tech, a mixed methods approach will be used So long as you make an informed choice and can justify it, it should be fine Just be aware of the limitations of your approach(es) and try to compensate where necessary
Acknowledgments and further links Some content borrowed from SkillsYouNeed website (http:// www.skillsyouneed.com/learn/research-methods.html) Other useful links: Introduction to Quantitative and Qualitative Research Models (William Bardebes). PDF at http://tinyurl.com/qq-models Methods Map: http://www.methodsmap.org Ready To Research: http://readytoresearch.ac.uk [email protected]: http:// www.methods.manchester.ac.uk/resources/categories Research Data Management training: http://datalib.edina.ac.uk/mantra/
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