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67 Cards in this Set

  • Front
  • Back
What is a Concept?
A concept is something that can be quantifiable in some manner
What is a construct?
A construct is a special concept. However, it is not readily quantifiable. One must use a theoretical framework to decide for operational variables to test for the construct. Ex: Chair is a concept. Comfort of the chair is a construct
What scales are used?
What scales are used?
What is a nominal scale
Nominal scales are names that can be associated with numbers. It codes a variable into a number. This is the lowest form of a scale, and some places do not recognize this as such.
Characteristics of an ordinal scale
First real scale
magnitude varies along a continuum

no math manipulations
ex. class rank
a track record
a simple list of 1st, 2nd, 3rd

pitfall: doesn't address distances between magnitudes
Interval scale characteristics
equal distances on a number line explains equal maginitudes

can have linear transformations
ex: temp scale - Celcius
relative: pull to the right or left, distances remain the same
shapes of distribution should remain the same

If you see two shapes and want to see if they share the same interval scale, plot out and if in a straight line, they share the scale

Each interval scale is also ordinal, and nominal

pitfall: doesn't give info about proportions
ratio scale characteristics
zero is fixed. can't be moved, isn't relative

proportion of magnitude is also equal to proportion on number line

hierarchal in nature - upgraded interval scale

Slope may vary depending upon the equation for transformation. However, it always goes through zero.

Kelvin scale is example of temperature as a ratio.
What is meta-analysis
summary of results over a huge body of studies.

You increase the validity of findings IF the study has been conducted properly.
Qualitative study
Uses Inductive reasoning
generalize over separate studies
Quantitative Study
Hard sciences like Psychology

Deductive Reasoning is used
Uses more causal relationships (If Then)
Major types of Validity
Content Validity
Criterion Related Validity
Construct Validity
Three types of Validity
Validity in Qualitative Research
Validity in Quantitative Research
Validity in Measurement
Explain Construct Validity
this is the degree to which a test measures the theoretical construct or trait it was designed to measure.

Based on current theory regardng the trait, the researcher makes predictions about how test scores will behave.

The predictions are tested.
If data supports prediction, construct validity is enhanced.
If data are not supported:
1.the exp. was flawed
2.theory was wrong and should be revised
3. test doesn't measure trait
How do you make a prediction support consruct validity?
Allow for group differences
allow for changes
look for correlations
Look at processes
What factors threaten internal validity?
history, maturation, pretest effects, instruments, statistical regression towards the mean, differential selection of participants, mortality, and interactions of factors
What are the threats to external validity?
Interaction effects of selection biases and treatment, reactive interaction effect of pretesting, reactive effect of experimental procedure, and multiple treatment interference
Types of validity in Qualitative research

Also, validity threats:

Researcher Bias (researcher's existing theory or preconceptions and the selection of data stand out to researcher.)

Reactivity (influence of researcher on the settings or individuals)
Validity threats in qual studies
Researcher Bias
(researcher's existing theory or preconceptions)

(influence of researcher on the settings or individuals)
Checklist of options to reduce validity threats
MO approach
(hold validity threats in contrast - treat as events)

Search for discrepant events and negative cases
analyzing these data is key!

collecting info from a diverse range of individuals and settings


Member checks- feedback from the people you are studying

Rich data -
"Thich" in that it is detailed and complete enough to reveal the picture of what is going on

running the simple numeric results that can readily be derived from the data.

Generalization in Qualitative Research
Internal generalizability - gen of a conclusion w/in the setting or group

External generalizability - not as crucial - face value?
3 steps that make a random design
1. Randomly select participants
2. random assignments of participants to groups
3. random assignemtn of treatments to groups
Quasi-experimental design
deals with intact groups like classrooms
major goal of randomness
To provide control in equal conditions that may affect outcome of treatment
How to begin randomizing
1. Decide and define population
2. Select a random representative sample. Remember to look at clusters or strata for population. This shrinks image of population. This is called the stratification approach
Validity with experimental design
Internal validity:
Does treatment produce any difference w/in a certain study?

External validity:
Does treatment make a generalization to a population?
Sample group size
The more factors you introduce, the smaller the sample group. (think of this as refining a search on the internet!)
best random design?
Randomized Solomon 4-group design
The degree to whichthe measurement is accurate and precise
Types of reliability
Internal consistancy- indicates to what degree the test scores reflect the same construct
Cronbach's alpha
varies between 0-1

0=low reliability
1= high reliability
test-retest reliability
reliability for stability across time points

ex. changes in vision
Pearson correlation, r
r shows the correlation between x & y
value between -1 and +1

want a + score for test/retest
content validity
1. face validity (arm chair)

2. logical validity - exam the degree to which items reflect content domain measured by test.

all cells proportionally represented at all levels
Criterion Related Validity
ex: entrance exam test
2 aspects to criterion related validity
Predictibility (predictive validity)

construct validity
to what degree does measurement measure what it is supposed to measure

theoretical frameworks

operationalize measurable variables
confirmatory factor analysis

will allow to test for hypothesized structure of test
when alpha is set before testing, the p-value is the actual figure of error found. Thus, use the highest alpha you can afford, but typically is 5%.

So: if p=0.023 when an alpha has been set to 0.05, the error is less than 5%. Then we reject the null hypothesis. (Thus saying that the hypothesis is not equal to zero.) Accept the alternate hypothesis.

The smaller the alpha, the easier it is to reject or accept the null.
Variance problems
not all numbers are in the same unit, so must find standard deviation
Standard Deviation
drawbacks to finding SD:
when you square the variance, you're artificially inflating the numbers

variance has different units of measure
target variable
dependent variable
Predictor variable
Independent variable
Variance proportion
square the correlation. This answers: What proportion of variance in Y is explained by the variance in x?
a magnitude that indicates the spread of scores about the mean.
finding variance
Ssquared=(x1-meanx)squared+(X2-meanx)squared....divided by n-1
measurement validity
how well does the test measure the construct?
labeling construct will depend upon your background knowledge.
measure of linear relationships
correlation between two variables
scatterplot design
the closer the dots are to a median line, the higher/stronger the relationship
finding standard deviation
take Ssquared and find the square root
if neg, neg correlation
if pos, pos correlation

is found by taking
if neg, neg correlation
if pos, pos correlation

is found by taking
semi-partial, or squared part correlation
gives proportion of unique contribution
Analysis of Variance
a statistical procedure
A statistic

higher F= lower p value=higher chance to reject null
zero correlation
if line on graph is horizontal
Lack of relationship
big circle of plots on graph with no linear line
Curve linear or quadratic
when the line on a graph is curved
how to study a curve linear graph
Analyze the intervals to form a linear relationship

examine meaningful sub groups
population coefficient
large P with x and y beneath
Null hypothesis

This means no correlation
Alternate hypothesis
Ha is not equal to zero
Type 1 error
to falsely reject the null hypothesis
Level of acceptable error
Alpha value of acceptable error
0.05 level of significance
More on alpha
Alpha is the probablility of a type 1 error.