Day 7

Question 1

Measurement and Prediction

Answer 1

Are changes in X associated with Y?, If I know x can I predict y? You are studying conversation not causation

Question 2

Relational designs are appropriate when

Answer 2

focus is on relationships between naturally occurring events. The variables can be measured reliably. Both events do vary.

Question 3

Contingency research

Answer 3

Data consist of frequencies, contingency tables, X2 (Ex:identity and career)

Question 4

Correlation coeffictents

Answer 4

Used when data can be measured on a ordinal, interval or ratio. Show strength and direction of relationship. Cary from -1.0 or +1.0, signifacants when?

Question 5

Scatterplot

Answer 5

(X) predictor- varable goees wherever (Y) outcomes variable goes where? spread shpws strenth of relationship.

Question 6

Why use a scatterplot?

Answer 6

To show you outliers and show you represintive

Question 7

Source of misleading(spurious) correlations

Answer 7

Restricted range, nonlinear relationship. skewed distribution, use of subjects with only extreme scores on one variable, outliers.

Question 8

The larger samples?

Answer 8

The less outliers and then therefore you will have a better score.

Question 9

Correlations and casuality

Answer 9

measures naturally occurring events after the fact; no experimental manipulation or control. Three possible sources of a significant r: x influences 4 or vice Versa. Something influences both.

Question 10

Drawing causal hypotheses from correlational

Answer 10

logical relationship between variables, contributing other variables, testing causal hypotheses, suggested correlational data experimenting. using patterns of correlations among a number of variables.

Question 11

cross-lagged panel correlations

Answer 11

complex correlation design, using temporal order to move inferences about causal relationships

Question 12

QMRI

Answer 12

Q: question
M: method
R: results
I:implications