section II

Culture
the customs, behaviors, attitudes and values that can be used to identify and characterize a population
Race
a way of classifying people based on real or imagined biological traits, most often centering on skin color
One-drop rule
hypodescent

related to RACE

Ethnicity
a concept related to a person’s identification with a particular group of people, often based on ancestry, country or origin or religion

Sex v. Gender v. Sexuality

How to use “race” in research
The concept of race may not tell you much about the individual’s ancestry or cultural experiences

However, race may be informative about the social experience a person may have within a mixed society

Biology & Culture are Intertwined
Females are seen as nurturers and males are seen as providers

What are the biological and cultural influences on these perceptions?

Sexual dimorphism, pregnancy, breastfeeding

The physical and behavioral characteristics within ethnicities may be more similar than between ethnicities
Geographical separation supports inbreeding within isolated groups/cultures

Assertive mating (culture based) supports inbreeding within mixed societies/cultures

We breed with partners that we have things in common with, including religion, physical traits, ages, SES, intelligence, politics, ect-

Making groups more alike over time

Etic
research finding that appears to be universally true across cultures
Absolutism
behavioral phenomenon can be viewed from the same perspective across cultures
Emic
a research finding that is valid only within a given culture
Relativism
behavioral phenomenon can be understood only within the context of the culture in which they occur

Example: sensitive caregiving v controlling caregiving

Universalism
(The Middle Ground)

a moderate view in cultural research that maintains that behavioral phenomenon may be based on invariant psychological processes but that each culture will induce different manifestations of those underlying processes

Methodological issues with Cross-Cultural Research
To compare groups you must measure the same things across groups

Content Validity
Diagnostic interview Schedule

CONTENT VALIDITY
the degree to which the material contained in a test relates to the concept being assessed
Diagnostic interview Schedule
do you often worry a lot about having clean clothes?”
For western, middle class individuals this can be about generalized anxiety
For developing societies or individuals without running water and indoor plumbing it could be about resources
What have we learned so far?
Human observation is often distorted.
(Measure variables carefully)

Inferred relationships are often erroneous
-Random events do not look random
-Use formal statistical methods to test hypotheses about relationships

Relationships that do exist may not be causal relationships (Some research designs do not demonstrate causality)

-Different experiences & individuals
*Life sciences are more complicated than strictly physical sciences
-Studies may have different results due to sample size
*Although both report same p-values, one may be more significant (esp. with a bigger pop.) because it has more power & faith
-Relationship problems may be due to methodological issues

What’s necessary for causation?
To say that A causes B, three conditions must exist:

-A precedes B

-A and B must covary (B must occur when A does)

-A must be the most plausible cause for B with other potential causes ruled out

((The methods we discuss today do not meet these conditions. [correlational methods]))

causation
Temporal precedence
When A changes, there must be concurrent or follow-up changes in B
A rational explanation with other potential causes ruled out [other potential causes is a criterion but have a theoretically, reasonable explanation is good but not always critical]
Why conduct a non-experimental study?
Some variables cannot be experimentally manipulated
-(Gender – impossible to manipulate
Love/hate, alcoholism – unethical to manipulate)

Some processes take a long time to study experimentally (Language development in children)

Non-experimental methods can be used as a means of suggesting, clarifying, refining, or extending experimental research findings.

Correlational Research Methods
Allows us to study the relationship between two or more variables.

A correlational analysis quantifies the strength of the relationship between two or more variables

Where a correlation exists, it can be used to predict values for one variable from values for other variables (regression analysis)

______________________
Purpose: establish if a relationship exists and to describe that relationship
Measurements can be made in natural settings or in the lab, but we are simply measuring (at least) two variables to see if there is a consistent pattern of relationship.

-Assume: relationship b/wn variables
-Goal: Measure the strength of the association; how correlated are A &B?
-e.g. For the sample (not the individual) that if you know what “Mom’s level of depression is” then you can make a reasonable assumption about the “level of anxiety” their child has can’t say that anxiety
*Predict one group mean from the next
-Applied to samples, NOT individuals

(regression analysis)
examples

Using a personality test to predict how someone will respond to certain situations
Using an aptitude test to determine whether applicants have the skills necessary for a job.

Correlational Research Methods vs. Correlational Statistics
Method: how we obtain the data
Gathering 2 or more bits of information from a person
No attempt to manipulate anything, just measured variables.
Self-esteem and attitudes towards minority groups
Class attendance predicting final grade

Statistics:
Can be used on any set of data, regardless of method
Can be used in a true-experiment, quasi-experiment, or a correlational study
____
-Correlations between a measurement and a measurement of itself again—reliability

Correlational Research Methods vs. Correlational Statistics:: METHOD
Gathering 2 or more bits of information from a person
No attempt to manipulate anything, just measured variables.

Self-esteem and attitudes towards minority groups
Class attendance predicting final grade

Correlational Research Methods vs. Correlational Statistics:: STATISTICS
Can be used on any set of data, regardless of method
Can be used in a true-experiment, quasi-experiment, or a correlational study
Conceptual Review: Correlation Coefficients
A correlation examines two or more sets of measurements taken from the same individual within a sample

A descriptive statistic that describes the linear relationship between two variables.

-Linear relationship: point A to point B that makes sense (a LINE)

Correlations range from -1.00 to +1.00
Absolute size indicates strength of the relationship
Sign indicates direction of the relationship

Scatterplots provide a pictorial view of the relationship

-Weakest correlation: (asymptotic) to 0

Interpreting Correlations: Magnitude and Sign of r
Is the correlation significantly different from zero (i.e., evidence for a relationship)?
Is the p value less then the alpha level?

Magnitude and Sign of r
General guidelines by Cohen (1988) – use with caution
Small: |r| = .20-.29
Medium: |r| = .30-.49
Large: |r| = .50-1.0

P-value? Probability of achieveing a corelation this large or larger if the correlation in the population was zero.
If the probability is low, it means that there is little chance that the pop correlation is zero.
So we say that the correlation is statistically significant. (usually less than .05)

-Statistic tells you the likelihood that something is different than 0 it is ZERO
*Even if r = .8 but p = .06, the correlation is indeed 0
~Due to be underpower e.g. sample size = 3

-Small effects (can’t say certain relationships aren’t meaningful)
-Don’t know that something that is a small effect size will translate into something meaningful down the line
*Use with caution

Interpreting Correlations: Coefficient of Determination
Coefficient of Determination (r2)
Proportion of variance shared by the two variables you have correlated

Often more useful than the correlation

Note: r2 is always less than r in absolute value (except at extremes: 0 & +/-1)

-Covariation is r-squared (intersection of 2 variables in regards to a Venn-diagram)
*Accounts for that amount of variation in that particular relationship

. R-square

Common misconception that correlations are on a ratio scale. For example, a correlation of .8 is not twice as strong as .4. Or we cannot predict one variable from another twice as accurately.
Need to think more in terms of Variance.
When all the scores fall close to a line, then the variance is small (there is little error variance) and the correlation is high.
Squaring the correlation gives us a better measure of variance. SO if r=.8 (or -.8) then the r-square is .64 which means that one variable accounts for 64% of the variance in the other.
So in the example I gave you earlier .4 vs .8 (.8 gives you 64%) what does .4 give you? (16%) Not half at all.

A corr of .99 indicates 98% of the variability in the criterion can be accounted for or predicted by the predictor

Factors that Affect A Correlation Coefficient
Nonlinearity

Range Restriction (Truncated Range)

Heterogeneous subsets (multiple populations)

Outliers or Extreme scores

Nonlinear Relationships
Here there is a clear relationship between the two variables, but the value of the r is 0.

ALWAYS check to see if your relationship is linear before computing a correlation coefficient or regression analysis.

-Relationship BUT the LINEAR relationship here is 0
*Nonlinear relationship (power function—parabolic)
*Negative quadratic
-At any extreme in real life, things are never often good happy medium

Restrictions in Range
positive relationship, but r will be lower if the range of values is restricted.

For the full range of scores, r = .45

but for data in the restricted range (shown within the circle), the value of the correlation drops, r = .29

Heterogeneous Subsets/Multiple Populations
-As grade grows up, kids become more anxious
*Driven by age
-Within each grade, as anxiety goes then reading scores go down

Overall positive relation between two variables, but each of the subsets has a negative correlation.
When you combine subgroups into one overall data set, the correlation coefficient may not provide useful information.

Extreme Scores
-Related to tails of the distribution (like range restriction)
-Be mindful of range (not only that there is an adequate range to capture all the variability & look to see the distribution e.g. floating point then possible censor/delete it)
*Treat extreme score as a case study –> pop up w/in samples as an extreme(s) then follow-up
Regression Analysis
The relationships between two variables can also be expressed by a regression equation
Predicting values for one variable if we know the value of other variables

The predictor is the known variable. The criterion is the predicted variable

regression equation
The relationships between two variables can also be expressed by a regression equation
Predicting values for one variable if we know the value of other variables

The predictor is the known variable. The criterion is the predicted variable

A regression equation is an equation of the form
Y = a + b X
where X is the predictor, Y the criterion

*y = mx + b

Example of how correlational design can be used

Prediction
For example: relationship between certain behaviors and immenent suicide attempts (can use warning signs, like talking more about suicide or giving away prized possessions) and take steps to intervene

Doesn’t necessarily have to be about future behavior, for example,
Knowing someone’s IQ allows us to make some predictions about the intelligence of their parents. Use available knowledge of one variable to predict value of unavailable variable.

Often describe one variable as the predictor and one as the criterion. In cases of prediction the designation is quite clear. GRE to predict graduate school success. GRE predictor, success is criterion.

predictor
known variable
criterion
is the predicted variable
Regression Equations
A regression equation is an equation of the form
Y = a + b X
where X is the predictor, Y the criterion

The equation predicts Y if we know X
A common example: predicting GPA based on SAT score.

_____
Predicting scores on one variable from scores on another can be done mathematically through a regression analysis.
Use standard equation for a straight line.
Y is the score we want to predict (vertical axis),
A is the value of where the extended regression line would hit the vertical axis (the y-intercept),
B is the slope of the line
X is the score we already know

Different approaches to the same story
Predicting scores on one variable from scores on another can be done mathematically through a regression analysis.
Use standard equation for a straight line.
Y is the score we want to predict (vertical axis),
A is the value of where the extended regression line would hit the vertical axis (the y-intercept),
B is the slope of the line
X is the score we already know

Beta is equivalent to the correlation

Multivariate Analyses: Multiple Regression
We often have more than one predictor for a given criterion
Example: predict college GPA from SAT scores and high school grades

Regression analysis can be extended to Multiple Regression
Using several predictors for one criterion

Multivariate Analyses: Multiple Regression
When there are only 2 variables (simple linear regression)
Extension is multiple regression in which we predict Y from a combination of two or more predictors

This cannot only be more accurate (rel’ps are complicated) but it can also
Tell us whether using 3 predictor variables accounts for more variance in the criterion variable than just 2 or one.
ALSO
It can be used to statistically control for potential third variables.

Examining the Third Variable: Partial Correlation
The meaning of a correlation between X and Y can sometimes be clarified by introducing a third variable, Z, and examining partial correlations
This is the correlation between X and Y, with Z held constant

Partial correlations are easy to calculate

Mediation/Moderation (Baron and Kenny, 1986)
Mediator: a variable that accounts for the relationship between 2 other variables.
Moderator: Specifies when effects will hold

partial correlations
the correlation between X and Y, with Z held constant
Mediation/Moderation
Mediator: a variable that accounts for the relationship between 2 other variables.
Moderator: Specifies when effects will hold

-Controls
-Partial: controlling for another variable & seeing if it might mediate
*Form of multiple regression

Mediator
a variable that accounts for the relationship between 2 other variables.
Moderator
specifies when effects will hold
Partial Correlation: An Example
Did you know that a child’s language skills are correlated with the size of the child’s big toe?!!
In one study, r = 0.45

A little thought suggests that there is a critical mediating variable: Age

Age and language skills correlate 0.65
Age and big toe size correlate 0.62

The partial correlation of language skills and big toe size can be calculated to be 0.08
___________

When the correlation drops it is a “mediating variable”
When it turns out to be larger, then it is referred to as a “suppressor variable”

-Age is accounting for the shard covariation

The importance of controls
Partial correlation analyses and multiple regression analyses allow us to control for third variables that may confound a correlation!
__
When the correlation drops it is a “mediating variable”
When it turns out to be larger, then it is referred to as a “suppressor variable”

Small, significant effect size is more important no matter how large r is

When the correlation drops it is a “mediating variable”
When it turns out to be larger, then it is referred to as a “suppressor variable”

Survey Research
A research method in which an investigator asks questions of a respondent
Survey question= Item
Ethics in Survey Research
Survey research is bound by ethical considerations, like any research
It is standard to provide anonymity and confidentiality to respondents

LECTURE
Confidential- I know who you are, but no one else does
Anony- researcher doesn’t even know who you are (cant use with longitudinal research- cant follow up!)

Ironically, stressing anonymity and confidentiality may arouse suspicions in respondents

Surveys: Imperfect Response Rates
Don’t want your sample of respondents to look different than the population of interest
How do we fix this problem? (of sample reflecting the population of interest)
Face to Face > phone > e-mail (?) > mail

Incentives (v coercion)

Make compliance the path of least resistance

Use the foot in the door phenomenon (small request, get them in a request- ask you a couple question it will be really quick…now I have just a few more)

Use the door in the face phenomenon (give them something that they are obvi going to say no to and will say yes to in comparison)

LECTURE: cant guarantee that youll get the number or demographics that you need to answer your question

foot in the door phenomenon
small request, get them in a request- ask you a couple question it will be really quick…now I have just a few more
door in the face phenomenon
give them something that they are obvi going to say no to and will say yes to in comparison,

tendency for people to agree to a small request after turning down a larger request

Response Bias
Class of biasing influences that cause people to distort the truth.

Ex: Social Desirability concerns

How do we fix the problem?
Make sure that confidentiality and anonymity is made salient
1. People don’t always believe this and sometimes people don’t want to admit things to themselves
2. Ask or Prime for honest responses &
Generate rapport
“we know that many people find it difficult to use a condom every time they have sex. What we would like to know is how many times this has happened to you in the last year.”
3. Use a deception check
Ex: “have you ever told a lie?”

Surveys: Another pitfall
Participants may not know the answers
We may not know the reason for our own attitudes and behaviors
OR, we may simply not know the answer
People are often embarrassed about not knowing the answer.
EX. Questions about politics
How do you feel about the agricultural trade act of 2001?
30% of a sample typically provide an answer to issues that are invented by the researcher
Solution?
Can be reduced by an explicit “don’t know” alternative
EX: Gallup uses “no opinion/don’t know” response
Creating a good survey is difficult, things to keep in mind (not exhaustive):
Keeping it simple, but clear
Avoid double-barreled questions
Avoid loaded/leading questions
Avoid negative wording/difficult to understand
Avoid acquiescence (yea, nay saying)
Avoid vague response options
Beware of order effects (response options/questions)
dont switch scale ranges, avoid range restrictions
Ask sensitive questions sensitively
Use questions relevant to all
Striking the right balance for number of questions/items
Field test for right response options
Beware of Order Effects
Order Effects
Order of Options: ppl favor option presented last
Order of Questions: the answer to a question might be influenced by the question asked before it
Fixing order Effects
Counter balancing- changing the order person to person (random order, or say half gets this half first and blah blah to see if there IS an order effect); no way to get rid of it within a single person, but you don’t survey just one person
Order effects should even out
Can also check to see if there is an order effect in your data analysis
Avoid Restricted Ranges
Want to avoid floor/ceiling effects

Most of data will be clustered at top/bottom (avoidable), now you don’t have range/variablilty
Number that makes you think metrically that you may be uncomfortable with (sometimes build in artifact, like putting a letter grade to it)

Ask Sensitive Questions Sensitively
Best to place these types of questions in the middle or ends of the survey (warmed up)
Improve phrasing to improve responses
Avoid Vague Numerical referents (discrete number options are ALWAYS better) see slide) usually, specificity is better, avoid slashes
Use questions relevant to all
Don’t ask too few questions (diff for psyc than soci)
More items= more reliability
The better the overall response
Don’t ask too many questions, find a balance
People get tired= don’t pay attention
How many is too many?
Pretest and try it out yourself and with pretest subjects
Other things to consider
Think about where to place key questions
Up front, so that you have the answers if they stop filling out the survey?
If they are boring (demographic) questions, will they stop before getting to the “good” stuff?

Group items together logically
According to topic
According to response format (rating scale, open-ended)

Consider the lay-out (simple and uncluttered)
Vocabulary and language should be appropriate
Example slide 26: what to eat on a date

MEDIATION
Mediation is said to occur when the prediction/the dependent variable by the independent variable.
________
Observational, archival and survey research can be very useful and provide valuable information
__________
Often a good starting point for developing hypotheses

Appropriate caution must be used because of the limitations of these methods.

Caution: sometimes a third variable can account for the association between two variables because the third variable is directly predicting both of the others

Mediating v Confounding

longitudinal studies
An experimental method used in developmental psychology to compare the same group of individuals repeatedly over time.

Con: Expensive, difficult

correlational studies
research method that examines relationships between variables in order to analyze trends in data, test predictions, etc. (they do NOT discern cause and effect relationships)
COHORT EFFECTS
The effects of being born and raised in a particular time or situation where all other members of your group has similar experiences that make your group unique from other groups

can prevent from making valid effects across groups
____

Particularly problematic when you have only 1 sample/1 division
Def: Differences between age groups based on when they were born
When comparing with age based differences
Raw cultural changes, some slow, some fast- affect individuals differently & generationally over time
9/11, PTSD:
1 singular even changes group dramatically…

TREND STUDY
A type of longitudinal study in which a given characteristic of some population is monitored over time.

o Ex: VOTER TURN OUT
More 18-24 year olds voted in the 2008 election than previous elections (2004-previous)
Not the same group of ppl, even if in same window of people
Not interested in following same group of people, not interested in individual change
Interested in group (population-level) change
o Basically cross sectional study that you repeat over and over again,
o Relatively cheap

PANEL STUDY
a type of longitudinal study in which data are collected from the same set of people at several points in time

o Committed to following particular group over time
o NOT new sample each time in field (compared to trend study)
o Concerns: decide who they are (bc once you recruit, youre stuck with them),keeping track, ect

COHORT STUDY
a study in which some specific group is studied over time, although data may be collected from different members in each set of observations

o Common to select based on age (group of two year olds, levels of aggression, ect)
o Can still have cohort effects (sample of 2nd grade teachers- could all be different ages, others have been teaching for years, ect)

COHORT SEQUENTIAL STUDY (under panel study)
combines cross sectional and longitudinal to correct for cohort effects

o Way to try yo get around cohort effects by creating multiple samples
o Follow each sample, but examine
Implications of discrete event
o Military- define cohorts by war, for instance- can have veterans all the same age that are have been in four different wars

RETROSPECTIVE
Still longitudinal, bc still studying change over time within one person, just collecting data all at once- they are just reporting retrospectively
Ex) sexual abuse, ptsd, schizophrenia
Problem: memory is fallible , accuracy of the reports are questionable
Things that are low-base rate, like schizophrenia/bipolar- they are the more extreme conditions
PROSPECTIVE
can Random sample bc you are actively collecting the sampling frame)
Con: COST, cohort effects, 1-shot esp when it costs 1B

Sample size, what can you get, when can you get, money, concern of validity of measurement
Retrospective report is easier

attrition
losing/dropping from sample

Most at risk to drop: the risky, interesting people- boring ppl stick around
Results in decreased power
Relates to variation and sample size
When you lose the extreme groups, you lose variation & everyone starts to look alike
Over sample the group you think you’re going to lose
Common mistake- representative sample at time 1, but at time 4 it’s no longer representative- should have over sampled extremes so at time 4, it is representative- bc not as interested in where they start, interested in where they end up FRONTLOAD

Issue w longitudinal studies
Data: stats
Diff stats for cross sectional & longitudinal
Key c
Independent t-test: samples must be independent from one another & randomly assigned
LONgitudinal data analysis- sample is NOT independent of one another (at diff times, still same group of people) cross-sectional-ANOVAs
Lack of independence (more challenging), not true of trend studies (bc you don’t have same ppl, independent groups)
Planned Missing Design (in longitudinal work)
Saves money

Multiple imputation- statistical processes, half my data is great, half is missing,

Can estimate/derive MY missing values given other data to figure out what I should have gotten based on what I have done already
Purposely do not collect data on certain percent of the data bc we can impute the missing values, so instead of seeing 1000 ppl at each assessment point, I see 800 people (20%less)
Choice made in beginning of study

multiple imputation
active deception
the process of misinforming a research participant about some aspect of a study so that investigator’s intent in the project
passive deception
the failure to provide complete information to a research participant about some aspect of a study so that the individual is not aware of the investigator’s intent in the project
debriefing
informing research participants at the conclusion of a research project of the purpose of the research, including disclosure of any deception and providing an opportunity for participants to ask questions about the research
dehoaxing
the process of telling research participants of any deception or rueses used in a study
desensitization
the process of eliminating any negative afereffects that a participant might experience after taking part in a project