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An operationalization should be
 VALID
 Construct validity
 Criterion validity
 RELIABLE

Two Types of Reliability
Reliable measure: stable measure
Reliable results: stable finding across different measures or differentoperationalizations

Failure to allow for falsifiability
Is the major reason that Freud is not too much in favoramong academic psychologists

Type I error
reject null hypothesis when it is true

Type II error
Accept the null hypothesis when it is false

Power
 Rejecting a false null hypothesis
 The probability that we will detect an effect if an effect actually exists

Sensitivity of a study
The ability to detect an effect even if the effect is small

Ceiling effects
All your test scores are high

Floor effects
all your test scores are low

parsimonious
avoids unnecessary concepts

how do we avoid type II errors?
Look at array of data

How to test the idea or theory
 Be concrete (operational)
 Say  in advance  what would prove you wrong
 FALSIFIABILITY – hallmark of modern scienceSays Karl Popper
 The theory is parsimonious

qualitative methods go along with
idiographic

quantitative methods go along with
nomothetic

qualitative data
 words
 Some times code into categories
 Often count what you code

Quantitative data
 Numbers
 they communicatea lot of information with a few symbols
 NEED TO USE NUMBERS WISELY (donʼtdecontextualize

kind of quantitative data

nominal data
 Categorical
 • Examples
 • Yes/no
 • Male/female
 • Car color
 • Type of pet

Continuous data
 ALONG A CONTINUUM
 • Shortest to tallest
 • Poorest to richest
 • Least to mos

how to make continuous data into categorical data
 Step 1: you have rating of best = 10to worst = 1)
 Step 2: you make these good and bad (e.g.,good = 10 to 6; and bad = 5 to 1

can you make Categorical data into continuous data?
NO!

Types of continuous data
 • Ordinal (in order)
 • Interval (in order and same differences)
 • Ratio (in order, same differences and realzero point)

