Johnny, CH.1 - Intro to Statistical Reasoning Flashcards
Research Process
What are the steps in the Research Process?
- initial observation
- theory
- hypothesis (identify variables)
- prediction
- data collection (measure variables)
- data analysis
see picture 1!
Before going from your initial observation to your theory, what must you do first?
You must identify one or more Variables, in order to be able to collect data later on
What is a Theory?
An explanation/set of principles that has been substantiated by repeated testing’s and explains a broad phenomenon
- A theory is general, and not specific to your observations
What is a Hypothesis?
A proposed explanation for a fairly narrow phenomenon of observations
- A hypothesis is specific and theory driven, and attempts to explain what has been observed
- The step from “Hypothesis” to “Generate Predictions” is to transform your hypothesis (something unobservable) into a prediction (something osbervable)
What are the 2 types of Variables?
- Predictor Variable (PV) (How Johnny calls the Independent Variable (IV)). It is thought to predict the outcome Variable
- Outcome Variable (OV) (How Johnny calls the Dependent Variable (DV)). Changes as a function of changes in a predictor variable
What are the different types of variables?
-
categorical
> binary (2 categories)
> nominal (>2 categories)
> ordinal (many ordered categories) -
continuous
> interval (equal intervals represent the same difference; no true 0)
> ratio (equal intervals represent equal differences; true 0)
see powerpoint!
Validity and Reliability
What is Validity?
Whether an instrument measures what it sets out to measure
What are the 4 types of Validity (mentioned by Johnny, not in general)?
- Criterion Validity: How accurately a test measures the outcome it was designed to measure.
~ Concurrent Validity: The extent of agreement between two measures when data are recorded simultaneously
~ Predictive Validity: The ability of a test to predict a future outcome. - Content Validity: Degree to which test items represent the constructs being measured
What is Reliability?
Whether an instrument can be interpreted consistently across many studies
Research Designs
What are the two main research methods?
- Correlational research method
- Experimental method
What is the correlational research method?
Observing natural events and their correlations without manipulation
What are some problems with the correlational research method?
- Doesn’t establish contiguity between two variables (which variable affects which)
- Tertium Quid (3rd variable problem)
What is the experimental method?
Researcher manipulates the IV and observe the effects of that manipulation on the DV
- it establishes causality
What are the different possible designs in an experimental research method?
- Between-groups/subject design (or else, independent design): There are different groups with different people for each condition
- Within-subject/repeated measures group: All participants are in all conditions
What is Variance?
The statistical measure of Variability
What are the 2 types of variance in an experimental research method?
- Systematic: Due to manipulation
- Unsystematic: Created by unknown (random) factors
What can researchers do to maximize systematic and minimize unsystematic variance?
Randomize participants to conditions OR randomize the order in which participants receive conditions.
EXAMPLE: in repeated measures design, there are the following problems
- Practice effects: Participants perform differently in the 2nd condition because of familiarity with the situation and measures
- Boredom effects.
If we randomize this solves the above problems:
- Half of the participants: Condition 1, then Condition 2
- The other half: Condition 2, then condition 1
Data Analysis - Distribution
What are Histograms (or frequency distributions)?
A visual representation of the distribution of quantitative data. (See picture 2)
What is the skew of a frequency distribution?
It’s the measure of asymmetry of a distribution
(See Picture 3 for different skews)
What is the kurtosis of a frequency distribution?
A statistical measure used to describe the degree to which scores cluster in the tails or the peak of a frequency distribution
- Leptokurtic: too many scores in the tails (Kurtosis>0)
- Platykurtic: too little scores in the tails (Kurtosis<0)
- Mesokurtic: Normal Distribution (Kurtosis=0)
!!! Pointiness of distribution does not play a role !!!
(See picture 4 for examples of the above)