survey design

origins “the questionary”

Darwin (facial expressions of emotion)

Hall (contents of children’s minds in urban areas)

Questions the value of surveys (James, Titchener)`

sampling issues in survey research

Biased vs. representative samples

Non-probability vs. Probability sampling

Participation incentives

Self selection bias

surveys vs psychological assessment

Attitudes, opinions, beliefs, projected behaviors vs. Psychological functioning

creating an effective survey

use of likert scales; assessing memory and knowledge; adding demographic information

types of survey questions or statements

open ended vs closed; “most important problem” question

survey wording

key problem in effective survey (avoid ambiguity); double-barreled questions; avoid biased questions

other wording tips

Keep it simple

Use complete sentences

Avoid negatively worded questions (should vs. should not)

Use balanced items (don’t favor one position over the other)

in person interview surveys

advantage: in person, comprehensive

disadvantage: representative samples, cost, interviewer bias

mailed written surveys

advantage: ease of scoring

disadvantage: cost, response rate (nonresponse bias), social desirability bias

phone surveys

advantage: cost, efficiency

disadvantage: must be brief, response rate, sugging

electronic surveys

advantage: cost, efficiency

disadvantage: sampling issues, ethics

Using and Abusing Survey Data

No requirement for informed consent if kept anonymous

Survey data is seen as objective and causal when it’s really not

Plays into confirmation biases and feeds availability heuristic

Correlation does not equal causation!

correlation

Finding the relationship between two variables without being able to infer causal relationships; a statistical technique used to determine the degree to which two variables are related

three types of linear correlations

Positive correlation

Negative correlation

No correlation

positive correlation

Higher scores on one variable associated with higher scores on a second variable

negative correlation

Higher scores on one variable associated with lower scores on a second variable

scatterplots

graphic representations of data from your two variables; One variable on X-axis, one on Y-axis

correlation coefficients

Statistical tests include: Pearson’s r, Spearman’s rho, phi coefficient

Ranges from –1.00 to +1.00

Numerical value strength of correlation

Closer to -1.00 or +1.00, the stronger the correlation

Sign: direction of correlation (Positive or Negative)

effect size

Proportion of variability in one variable that can be accounted for (or explained) by variability in the other variable

The remaining proportion can be explained by factors other than your variables

r = .60 r2 = .36

36% of the variability of one variable can be explained by the other variable

64% of the variability can be explained by other factors

outliers

Scores dramatically different from remaining scores in data set impact Pearson's r and r2; could lead to type 1 error

regression

The process of predicting individual scores AND estimating the accuracy of those predictions

regression line

straight line on a scatterplot that best summarizes a correlation

regression line formula

y = bx + a ;

Y = criterion variable—the variable that is being predicted (DV)

X = predictor variable—the variable doing the predicting (IV)

a = point where regression line crosses Y axis

b = the slope of the line

multiple regression

One criterion variable

More than one predictor variable

Relative influence of each predictor variable can be weighted

interpreting correlational results

Directionality problem (A could cause B, or B could cause A); third variables (Uncontrolled third variable could cause both A and B to occur); mediating vs moderating variables

mediator

explains how or why a relationship between two variables exists

moderator

explains under what conditions the relationship between two variables exist