- missingness not related to any other variable - BEST missingness - Little's test (want >.05), don't reject null that it's MCAR

- missingness related to another variable, but no patterns within the variable (not insidious) - prob. that missingness is unrelated, after controlling for another variable - eg. depressed people less likely to report SES, but within depressed people the prob of reported SES is unrelated to SES level

Lecture 2 Flashcards by Claire Jenkins

What are the different types of psychological research?

basic (look for knowledge for its own sake; theory) vs. applied (application)
lab (control over research) vs. field (realism, face validity, ecological validity)
quantitative (stats) vs. qualitative (richness of description)

How well did you know this?

Not at all

Perfectly

What are the different experimental designs?

single factor (2 levels, or more)
factorial (2 or more, main effects and interactions)
correlational (association)
regression (predict)

How well did you know this?

Not at all

Perfectly

What is the difference between applied and quasi-experimental research? What is the issue with this?

applied is controlled, forced random assignment
quasi-experimental is when groups occur naturally, no random assignment possible

ISSUE: lose any sense of causality. You have lost control

How well did you know this?

Not at all

Perfectly

What are the problems with NHST? What should you do to attempt to remedy this?

low power: may be a diff you don’t see
high power: find even a very small diff as sig
Type 1 error: false +ve; accepting false hypothesis as true
Type 2 error: false -ve; rejecting true hypothesis as incorrect
FIX: report CIs and effect sizes with p values

How well did you know this?

Not at all

Perfectly

What are the two types of multivariate models we fit to data?

confirmatory: seek to test prediction

- exploratory: models that seek to account for rships

How well did you know this?

Not at all

Perfectly

What 3 things do you use to assess the fit of a model?

residuals
summary measures (eg. R2)
statistical test (eg. null)

How well did you know this?

Not at all

Perfectly

What are the different types of variables?

independent vs. dependent (outcome vs. predictor)
discrete vs. continuous (discrete can be categorical or orindal)
exogenous vs. endogenous (exo = outside system we’re modelling)

How well did you know this?

Not at all

Perfectly

What are the levels of measurement?

nominal (categorical)
ordinal
interval
ratio

How well did you know this?

Not at all

Perfectly

What are the 2 types of missing data?

missing completely at random (MCAR)
missing at random (MAR)
both of these are IGNORABLE

How well did you know this?

Not at all

Perfectly

What is MCAR?

missingness not related to any other variable
BEST missingness
Little’s test (want >.05), don’t reject null that it’s MCAR

How well did you know this?

Not at all

Perfectly

What is MAR?

missingness related to another variable, but no patterns within the variable (not insidious)
prob. that missingness is unrelated, after controlling for another variable
eg. depressed people less likely to report SES, but within depressed people the prob of reported SES is unrelated to SES level

How well did you know this?

Not at all

Perfectly

How should you estimate missing data?

DO NOT use the mean
SPSS Missing Value Analysis: uses regression and EM algorithm approaches
SPSS Multiple Imputation. MI creates multiple complete sets of data
if a person is missing some items in a scale but has responded to others, then use the data you have to estimate missing data

How well did you know this?

Not at all

Perfectly

When do you transform data?

only if you really have to
can transform so that it meets the assumptions of your test
can change symmetry of data (i.e. want normal distro.) > can fix skew but not kurtosis

How well did you know this?

Not at all

Perfectly

Why use Guttman instead of Alpha?

alpha not the best
all relaibility coefficients are pessimistic (lower bounds - i.e. that value or higher)
Guttman allows for choice of lower bound estimates

How well did you know this?

Not at all

Perfectly

How do we develop research?

observations
theory
past research (gaps)

How well did you know this?

Not at all

Perfectly

What are the essential elements of experimental research?

hold some variables constant
vary others (IV)
observe changes on another variable (DV)

What are within and between subjects designs? What are the main issues with each of these?

between: independent groups; need equivalent groups (use random sampling and random allocation)
within: repeated measures; need to control for sequencing (learning) effects (counterbalance)

What is the goal of applied research?

to investigate real-world phenomenon

What 2 things should you never say?

X causes Y

- X leads to Y

What is multivariate?

multiple DVs

What is the basic model that underlies everything?

data = model + residual

- have a residual for each individual or each cell

What does the residual account for?

random noise
measurement error
diffs in scales
etc.
tells us how well the model FITS the data

What are the 2 types of NHST in model fit?

model IS null: want less than .05, reject model/null, residuals large
model NOT full: want >.05, do not reject model model, residuals not too large

What are the model parameters in the regression equation?

the regression coefficients/weights

How can you represent the regression equation in matrix form? What does regression do?

Y = Xb + e | - predicting a vector from a matrix

What is the regression equation?

Y = b0 + b1X1 + b2X2 + e

What does i indicate in the regression equation?

- the model holds for every i | - holds for every person

What 4 things do you need to do after you imput your data?

- CHECK the data - missing data - transformations - form totals

What are the 2 types of deleting data? Which one is better?

- pairwise: remove pairs of cases relevant to analysis - listwise: remove any case with missing data (LOSE POWER!) - pairwise is better, listwise loses power

What are the steps involved in missing data?

- determine extent - delete cases - evaluate missing values

What are common data transformations?

PULL IN HIGHER TAIL - (-)1/x2 > strongest change - (-)1/x - log(x) - sqrt(x) PULL IN LOWER TAIL - x2 - x3 - antilog(x) > strongest change

When is correlational/regressional research helpful?

- when experimental cannot be done - cannot randomly assign people to groups - has ecological validity

When is causality possible?

- carefully designed longitudinal studies | - careful experimental design

What type of variables do ANOVA and independent t-tests require?

discrete

What is interesting about ordinal scales in psychological research?

they are often assume to be interval | - this is usually fine, but can sometimes cause issues

What do totals involve? (6 things)

- reliability of test scores - validity of subscales - develop new subscales, if necessary - form empirically weighted totals - you can drop 'dud' items - cope with missing data