section II

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Culture
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the customs, behaviors, attitudes and values that can be used to identify and characterize a population
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Race
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a way of classifying people based on real or imagined biological traits, most often centering on skin color
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One-drop rule
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hypodescent related to RACE
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Ethnicity
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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
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How to use \”race\” in research
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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
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Biology & Culture are Intertwined
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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
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The physical and behavioral characteristics within ethnicities may be more similar than between ethnicities
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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
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Etic
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research finding that appears to be universally true across cultures
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Absolutism
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behavioral phenomenon can be viewed from the same perspective across cultures
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Emic
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a research finding that is valid only within a given culture
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Relativism
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behavioral phenomenon can be understood only within the context of the culture in which they occur Example: sensitive caregiving v controlling caregiving
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Universalism
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(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
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Methodological issues with Cross-Cultural Research
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To compare groups you must measure the same things across groups Content Validity Diagnostic interview Schedule
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CONTENT VALIDITY
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the degree to which the material contained in a test relates to the concept being assessed
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Diagnostic interview Schedule
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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
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What have we learned so far?
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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
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What’s necessary for causation?
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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]))
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causation
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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]
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Why conduct a non-experimental study?
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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.
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Correlational Research Methods
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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
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(regression analysis)
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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.
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Correlational Research Methods vs. Correlational Statistics
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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
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Correlational Research Methods vs. Correlational Statistics:: METHOD
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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
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Correlational Research Methods vs. Correlational Statistics:: STATISTICS
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Can be used on any set of data, regardless of method Can be used in a true-experiment, quasi-experiment, or a correlational study
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Conceptual Review: Correlation Coefficients
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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
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Interpreting Correlations: Magnitude and Sign of r
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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
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Interpreting Correlations: Coefficient of Determination
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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
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Factors that Affect A Correlation Coefficient
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Nonlinearity Range Restriction (Truncated Range) Heterogeneous subsets (multiple populations) Outliers or Extreme scores
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Nonlinear Relationships
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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
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Restrictions in Range
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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
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Heterogeneous Subsets/Multiple Populations
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-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.
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Extreme Scores
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-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
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Regression Analysis
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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
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regression equation
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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.
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predictor
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known variable
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criterion
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is the predicted variable
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Regression Equations
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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
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Different approaches to the same story
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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
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Multivariate Analyses: Multiple Regression
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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
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Multivariate Analyses: Multiple Regression
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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.
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Examining the Third Variable: Partial Correlation
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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
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partial correlations
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the correlation between X and Y, with Z held constant
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Mediation/Moderation
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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
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Mediator
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a variable that accounts for the relationship between 2 other variables.
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Moderator
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specifies when effects will hold
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Partial Correlation: An Example
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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
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The importance of controls
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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\”
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Survey Research
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A research method in which an investigator asks questions of a respondent Survey question= Item
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Ethics in Survey Research
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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
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Surveys: Imperfect Response Rates
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Don’t want your sample of respondents to look different than the population of interest
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How do we fix this problem? (of sample reflecting the population of interest)
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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
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foot in the door phenomenon
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small request, get them in a request- ask you a couple question it will be really quick…now I have just a few more
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door in the face phenomenon
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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
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Response Bias
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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?\”
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Surveys: Another pitfall
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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
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Creating a good survey is difficult, things to keep in mind (not exhaustive):
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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
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Field test for right response options
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Beware of Order Effects
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Order Effects
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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
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Avoid Restricted Ranges
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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)
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Ask Sensitive Questions Sensitively
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Best to place these types of questions in the middle or ends of the survey (warmed up) Improve phrasing to improve responses
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Avoid Vague Numerical referents (discrete number options are ALWAYS better) see slide) usually, specificity is better, avoid slashes
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Use questions relevant to all
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Don’t ask too few questions (diff for psyc than soci) More items= more reliability The better the overall response
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Don’t ask too many questions, find a balance
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People get tired= don’t pay attention How many is too many? Pretest and try it out yourself and with pretest subjects
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Other things to consider
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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
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MEDIATION
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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
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longitudinal studies
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An experimental method used in developmental psychology to compare the same group of individuals repeatedly over time. Con: Expensive, difficult
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correlational studies
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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)
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COHORT EFFECTS
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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…
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TREND STUDY
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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
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PANEL STUDY
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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
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COHORT STUDY
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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)
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COHORT SEQUENTIAL STUDY (under panel study)
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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
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RETROSPECTIVE
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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
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PROSPECTIVE
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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
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attrition
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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
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Issue w longitudinal studies
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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)
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Planned Missing Design (in longitudinal work)
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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
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multiple imputation
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active deception
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the process of misinforming a research participant about some aspect of a study so that investigator’s intent in the project
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passive deception
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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
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debriefing
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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
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dehoaxing
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the process of telling research participants of any deception or rueses used in a study
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desensitization
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the process of eliminating any negative afereffects that a participant might experience after taking part in a project

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