Test 3: Regression Equations, Types of Measurement, Survey Design and Sampling. Flashcards
(46 cards)
regression lines is an “____”
estimate. it is the predicted y value. Thus, there will always be some degree of error where the predicted y value may differ from the observed y value.
residual on a graph is determined by:
the distance upwards or downwards from the regression line to the observed data point.
Residuals can be….
(A) Large or Small
(B) Positive or Negative
(C) Null (perfect
prediction)
regression line as a line of ______. if placed correctly…
> best fit. > It should sit in the middle of all the data points. > If accurately placed then the residuals (error) should add to 0. i.e. all the positive and negative valued error should equate to 0.
What is the goal for a regression equation?
Provide the best estimate of how to predict the y-values (DV) from the x values (IV).
thus, to understand the correlation between two variables we need the slope of the regression/correlation line to identify the direction and strength of the association between x and y.
Moving the regression line along the y-axis or adjusting it’s tilt will….
create more misfit i.e. increase the residuals.
In a regression we are predicting __ not __
the DV not the IV!
How does Methods link to methodology?
How we decide to define, and measure constructs is a large part of the research process. What is equally as important is the ability to test is the measure is both reliable and valid.
What is the point of Measurement?
In science, we aim to make “good” measurements of psychological phenomena. What two core concepts within the philosophy of science does this relate to?
(A) Theory/Predictions
(B) Theoretical debates are filled with constructs, hypothetical psychological phenomena that cannot be measured directly.
Observations
Derived from data, observations are used to shed light on those constructs. This is done by using measurement to capture data that represent those constructs.
Representations of Constructs/Conceptual Variables are derived using?
operationalizations.
Example:
Wellbeing is a conceptual variable or construct that can be defines with words but cannot be directly measured- it’s intangible.
Thus, researchers need to operationalize this variable into terms that makes it both observable and measurable.
i.e. we can measure the construct indirectly, by measuring the representation of it we decide on through our operational definition.
To determine if two constructs are theoretically linked one must ….
a meaningful conclusion is contingent on?
compare the observable variables which represent the conceptual variables respectively. This is contingent on the two measures both being reliable and valid in order for a meaningful conclusion to be drawn.
“Good” Variables are both ___ why is this important?
reliable and valid. Why? This allows us to be confident that our proxy measure is representing the construct and not something else (i.e. another construct or error).
variables are ___ and _____?
Measurable representation of an abstract construct.
A proxy/indirect measure of said construct.
measurements are generally more reliable if… ___ or ___.
(A) Measures that don’t include a lot of “noise” (= error)
Most psychological measurements contain relatively high amounts of “random variation” (i.e. naturally occurring variation withing the data, error, that we aim to minimize) due to contextual factors.
For example, variation due to equipment, or physiological changes.
(B) Measures obtain focused information about the
core construct
For example, self-report scales need multiple items that represent the construct being measured.
Note: Noise, Random Variation, Error are the same thing.
Implications of physiological, observational data and Self-Report Measures, respectively:
Physiological Measures:
Needs to be repeatedly measures across multiple time points.
Data has to go through an extensive data cleaning process to identify the key variables within it, this can be conducted using a computer program.
Due to the large variation between measurements/noise
• Observational Data:
Has to also go through an extensive data cleaning process to identify the key variables within the data.
Due to the large variation between measurements/noise
• Self-Report Measures:
Requires more than 1 or 2 items in order to effectively measure the construct it represents and the naturally occurring noise!
Typically, a minimum of 4-5 items should be used.
Sometimes, and only sometimes is 3 sufficient.
Note: These items need to be focused around the construct!!! Variability within the
same scale will not produce reliable measurements.
Do people Want to find reliability within their data?
*It depends on the type of variable you are measuring!
For example,
Q: If you have 3 different self-report measures: Gender, mood, and optimism. Which of these measures will be the most reliable over a three-month period?
A: Gender.
Gender: Is Highly stable demographic variable, the most reliable in terms of test-retest reliability with r = .95.
Psychological: are variables rooted in personality like optimism that have intermediate stability, has moderate test-retest reliability with r = .70.
Mood: Variable that changes frequently, has weak stability, has the lowest test-retest reliability, r = .50.
The Goal of Test-Retest Reliability is Somewhat Ambiguous because it depends on…
“The value will depend on the time between test and retest, the length of the test, what is being measured, and the characteristics of the sample. Some traits are very stable. Others may show some change over time. Thus, there is no absolute value and it will depend on the situation. “
If someone asks you how reliable is your measure you should ask…..
What type? There are two main types of reliability!
(A) Test Retest Reliability
Correlation overtime for the same individuals.
(B) Internal Reliability
The average level of intercorrelation between items
within a scale.
e.g. Cronbach’s Alpha
Internal relaibility using cronbachs alpha tells us….
item-rest correlation tells us…
Item if dropped tells us..
o Internal Reliability, using Cronbach’s Alpha is 0.85. i.e. the average subscale intercorrelation value for all comparisons.
o Looking at item-rest correlation: this tell us how much each item correlates with the other items on the scale. The closer it is to the average Cronbach’s alpha the stronger the item is at internal reliability. And the more likely that this item represents a core attribute of the construct being measured.
o If low item-rest correlation it may mean that the item measures a peripheral attribute of the construct being measured.
o If item is dropped, calculates if the we were to drop this item would the average Cronbach’s alpha improve?
o If so, you may choose to drop it, i.e. shorten the scale to improve its internal reliability.
Note: A scale can be highly reliable but not
valid!!!!!!
Three Main Types of Validity:
(A) Content validity (lower order)
Do the items in the scale accurately represent the construct being measured?
Often confused with “Face Validity”- whether the measure appears to be valid to those who are using it. It’s which is not a valid form of validity because a scale can have face validity with no content validity i.e. magazine or buzzfeed quizzes.
A little bit subjective
Closest examination of the items themselves
(B) Criterion Validity (intermediate)
To what extent does the scale predict the expected outcome?
More critical form of validity that measures if:
a. Scale accurately predicts future behavior
b. Scale is meaningfully related to other measures of the same behavior.
E.g. does grit predict academic performance?
(C) Construct Validity (higher order)
To what extent does the scale measure the intended hypothetical construct?
i.e. directly linked to the authors operationalization of the construct and scale accurately measure the conceptual variable.
Does the measurement truly capture the construct as a whole?
Construct validity cannot be obtained in a singular study, it develops gradually overtime through the processes of replication.
Establishing criterion and construct validity establishes construct validity.
Two Intermediate types of Validity:
(A) Convergent Validity
Measures the extent to which the scale in question correlates with other scales that were designed to measure the same construct.
Much more straightforward intermediate validity to demonstrate.
Includes (+ or - ) correlations!
(B) Discriminant Validity
Measures the extent to which a scale does NOT correlate with other scales that are theoretically not expected to be related to the construct of interest.
The aim is to find a non-significant correlation and not a negative correlation!
This is hard to demonstrate.
“Good” scales:
- Reliability: it produces a similar score for the same individuals for attributes which are very stable (don’t change much) or intermediately stable (change a little). We are confident that our measure is close to the true measure.
- Validity: we want our scale to measure what they are intended to measure, and nothing else. If all (5) characteristics of validity are met, then we can be confident that it measures it’s intended construct.
A scale demonstrates construct validity if:
and is likely to perform well in a ___ analysis.
• Multiple studies demonstrate the scale has good construct validity & the other (4) validity characteristics.
• Construct validity is the highest order, more abstract form of validity that incorporates all characteristics of validity.
• Needs to demonstrate that the scale is valid in multiple studies, context with various demographics.
e.g. Meta-Analysis: a measure that performs well in aggregations of numerous studies is likely to have good construct validity.
(4) Scales of measurement:
(A) Nominal (Categorical)
Variables in which its numerical value corresponds with the category it is a member of.
It’s purely a form of categorization.
For example, Gender is coded into 0:1:2 with number indicating whether the participant was male or female or other.
Note: Gender is no longer considered a binary measure- it is considered nominal now!
(B) Ordinal
Numerical value indicates the object or individual rank or relative standing.
Higher numbers= Lower rank
Smaller numbers= Higher rank
Only feasible with a small number of groups of comparisons.
Can rank almost any attribute
Relative standing is the ONLY thing we know.
(C) Interval (Continuous)
Variables with numerous levels of obtained values within the minimum and maximum of the scale.
Assumption of equal distances between interval points.
Distribution of scores should equate to a normal distribution.
(D) Ratio
Relatively uncommon scale of measurement used in psychology.
Similar to ordinal or interval scales but it has a true point of zero
e.g. that the 0 is meaningful, it means an absence of the attribute being measured. For example, 0 errors made.
Treated like interval variables but the key difference between them is the meaningful!!