GEDU 6170 Research LiteracyQuantitative and Qualitative Research

Saad Chahine, PhD May 6, 2014

Quantitative Research

“technical literacy”- Focus on the specific analytical procedures and how & when to use them

“intuitive literacy”- Focus on a general understanding of the kinds of intuitions needed to understand the statistics

(Shank & Brown, 2007, p. 38)

Statistical Worldview

• Newton example… • By conducting several experiments, developed

an underlying model that can explain gravity • The model can then be used to predict any

falling object • Very deterministic – educational research likes

to be deterministic…but it is difficult to find such absolutes – life is much more about probability

Data is Pervasive

• All observations in life can be thought of a data

• Each observation is a datum • When combined these become distributions • Based on the kinds of data collected, different

distributions can form

Distributions

• Constant Distribution (AKA Uniform Distributions) • “Blob” Distribution (AKA Correlation r=0) • Normal Distribution (AKA Bell Curve) • Systematic Distributions (e.g. t distribution) • Skewed Distributions* • Many more…

(Shank & Brown, 2007)

Uniform Distribution

http://en.wikipedia.org/wiki/File:Uniform_Distribution_PDF_SVG.svg

Correlation

http://en.wikipedia.org/wiki/File:Correlation_examples2.svg

Normal Distribution

http://en.wikipedia.org/wiki/File:Standard_deviation_diagram.svg

t distribution

http://en.wikipedia.org/wiki/File:T_distribution_1df_enhanced.svg

Skewed Distribution

http://en.wikipedia.org/wiki/File:Negative_and_positive_skew_diagrams_(English).svg

Levels of Measurement

• Categorical Data– Non-ordered data – Often represents different categories: sex, eye

colour, SES, and group type (experimental or control)

– An average would be meaningless– More meaningful to talk about different categories

Levels of Measurement

• Ordinal Data– Distance between data points will vary – Examples: placement in a race, survey response,

teacher grades – Averages are not meaningful; middle number

(median) is most representative of data set

Levels of Measurement

• Interval Data– Very similar to ordinal data, however, distances

between points are equal – E.g., temperature and well designed rating scales – Important: ‘0’ is not meaningful – Averages (mean) is meaningful way to describe a

data set

Levels of Measurement

• Ratio Data– Same as interval except the “0” is meaningful – We can say “twice as much” – E.g., Temperature in Kelvin, height, and weight– Average is the most meaning full way to describe

the data set

Central Tendency

• If you want to describe a population or a group of people using one or two numbers you could say:– On average, students in Nova Scotia scored 570 on

an international test of reading (mean)– In Novo Scotia, the most frequent eye colour is

brown (mode) – In a small sub-sample of 10 students, the weekly

time spent on homework was 5 hours (median)

Descriptive vs. Inferential Statistics

• Descriptive statistics describe the sample or population usually by providing values of range, maximum, minimum, central tendency, variance (sum of individual differences from the mean)

• Inferential statistics are often used when you do not have access to the entire population and want to make an inference about this population

Sampling

• Convenience Sample • Purposive Sample • Representative Sample • Random Sample • Can be more complex… e.g., Proportional

Random Sample

(Shank & Brown, 2007, p. 46)

Analytic Procedures

• Correlation• t-test• ANOVA• Chi-Squares • Regression based

(Shank & Brown, 2007, p. 54)

Qualitative Research

• Has varied views and perspectives• More focused on meaning than a quantitative

method • Some basic perspectives that cut across most

qualitative methods

Holistic vs. Experimental

• More focused on examining phenomena in a naturalistic setting

• Less focused on individual components of a complex system

• More focused on interactions with the system as a whole

• Less focused on isolating relationships(Shank & Brown, 2007, p. 60)

Looking for Meaning

• At the most basic level, qualitative research looks for “themes” that describe patterns in a data set

• Researcher can take two different stances: “outsider looking in” vs. “Insider looking

out”• Some researchers can examine self as insider

and outsider in autobiography studies

(Shank & Brown, 2007, p. 62)

Strategies for Data Collection

• Observations • Interviews • Focus groups • Martials analysis • Archival and historical record analysis • Interpretive analysis (e.g. phenomenology) • Participant observations (Shank & Brown, 2007, p.63)

Methods

• Ethnography • Grounded Theory • Case Study • Narrative and Oral Historical Analysis • Critical Theoretical Analysis • Action Research • Qualitative Educational Evaluation (Shank & Brown, 2007, p.65)

Activity

• In groups, review the article you are provided • As a group identify: – Purpose– Methodology– Importance – Relevance to Education