A document of a combination of information from lectures, power points and the textbook. There are in-depth explanations of the topics explored in Week 1 as well as many examples and figures. These notes are in an easy-to-read format. At the end of the document, there is a colourful and visual; sum...
Classification of data - 4 types :
1) Nominal/ Categorical – no numbers, no rank, just labels e.g., blood type or eye
colour, one isn’t better than the other, all the numbers do is distinguish one category
from another
2) Ordinal – one is more or less than the other, ranked or ordered, Grade of A, B or C <>,
intervals are meaningless as they do not tell us how far apart, they are, could be 89%
and 70% or 79% and 80%
3) Interval – true quantitative measure, mathematical operations like addition and
subtraction, no multiplication or division, scale of measurement is not the same : 40
degrees isn’t the twice as hot, someone with an IQ of 100 doesn’t’ have double of
one with an IQ of 50, it is relative value not an absolute value, differences are
meaningful
4) Ratio: True zero value, start to measure absolute values, psychical properties like
length and age. If you have 0 of this value, you have 0 of this construct. E.g., age or
weight
Type of data helps you inform what type of stats test to use, variables can be measured on
different scales of measurement
Chi- squared used for – nominal/categorical and sometimes ordinal
When and why is classification useful? ( Types of data)
- Allows researchers to group data into exhaustive and mutually exclusive categories
- Exhaustive = encompasses all members of the population, capture all iterations
, - Mutually exclusive= categories clear, no overlap and no member of the population
can be in 2 categories
2 types of Chi-squared test:
- Goodness of fit
- Test of contingency
Use of contingency tables:
When to use: When data is classified with respect to 2 or more qualitative variables
Purpose: Help us understand the 2 types of Chi-squared tests
Infer stats: some inference about the population at large
Example of multidimensional table:
Example of unidimensional table:
What is a goodness of fit test?
What is test of contingency?
LECTURE 2: Psychometrics – writing and evaluating test items
Item analysis
Definition: Procedure of identifying poor items in a measure or a scale
- Poor items in this context prove they are meaningless ( everybody responded to the
item in the same way)
Purpose:
- Item analysis gets rid of bad items
- Start by generating a large pool of questions from which you select the best one
What constitutes a ‘ good item’ in psychometric terms? Ways to analyze an item
1) Item difficulty
2) Item discriminability
3) Item characteristic curves ( ICCs)
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