Ch. 12-Research Methods

Question 1

confirmatory -data analysis

Answer 1

hypothesis testing

*create a hypothesis based on research (theories) that is already done

Question 2

exploratory-data analysis

Answer 2

no firm hypotheses

• start off doing descriptive stats
• detective work
• newer research is exploratory

Question 3

example of comparing group means- analyzing results

Answer 3

*second hand smoke was rated as more dangerous by nonsmokers (m=6.55) compared to smokers (m=2.32)

Question 4

example of comparing individual scores -analyzing results

Answer 4

*compare scores of the individual smokers

Question 5

frequency distributions

Answer 5

• first step in understanding data
• shows how many people received each possible “score” on a variable
• tables and graphs are helpful to get a quick glance at the data

Question 6

pie charts- graphical illustrations

Answer 6

*uses percentages

Question 7

bar graph- graphical illustrations

Answer 7

• x axis=category

* y axis =frequency

Question 8

frequency polygon- graphical illustrations

Answer 8

• uses a line to represent frequencies
• best with interval or ratio scales
• plot each point and “connect the dots”

Question 9

histogram- graphical illustrations

Answer 9

• uses bars to represent quantitative data
• scale values are continuous on the x-axis
• bars are drawn next to each other

Question 10

frequency tables- graphical illustrations

Answer 10

• allow us to get a quick count of data

Question 11

stem and leaf- graphical illustrations

Answer 11

*present original numbers with a visual summary 
allow us to see
-symmetry 
-variability
-outliers 
-concentrations
-gaps

Question 12

back to back stem and leaf-graphical illustrations

Answer 12

*allows you to compare groups

Question 13

shape-distribution

Answer 13

• what does the distribution look like?
• bell shaped curve
• skewed -most scores are concentrated to the one side of the distribution
• normal

Question 14

center-distribution

Answer 14

*where is the center of the distribution?

Question 15

spread-distribution

Answer 15

• what values does your distribution have?

Question 16

distribution vs stem and leaf

Answer 16

• sort scores in ascending order

* flip the distribution to see how it fits the data

Question 17

bimodal distribution

Answer 17

*there are two modes

Question 18

scatterplot

Answer 18

*study time/exam grade

Question 19

descriptive stats

Answer 19

• allows us to summarize data
• make statements about data
• describe sample which should represent your population
• sample-the people in your study
• population-people you to want to generalize to.

Question 20

central tendency

Answer 20

• typical behavior for the sample as a whole
• mean-average of all scores (x or m)
• median-middle score -the number that cuts the distribution in half (50%)
• mode -most frequency occurring score

Question 21

means

Answer 21

• unweighted-sum of group means/# of group means
• means carry the exact same weight
• weighted-sum of values/# of values
• larger means will carry more weight

Question 22

outliers

Answer 22

• impact the mean

* trimmed mean

Question 23

advantage of mode

Answer 23

• outliers are not a problem

* only care about frequency

Question 24

advantage of median

Answer 24

• one answer -small n skewed
• outliers aren’t as problematic
• comparing sets of data

Question 25

advantage of mean

Answer 25

* one answer | * comparing sets of data

Question 26

disadvantage of mode

Answer 26

* multiple answers * difficult to interpret * values may no repeat

Question 27

disadvantage of median

Answer 27

*doesn't take into account each score

Question 28

disadvantage of mean

Answer 28

*outliers are problematic

Question 29

variability

Answer 29

*how spread out the scores are around the mean

Question 30

range

Answer 30

*crude-difference between highest and lowest scores

Question 31

variance

Answer 31

*average squared distance from the mean

Question 32

standard deviation

Answer 32

*average deviation of scores of mean

Question 33

standard distribution

Answer 33

*mean of distribution subtracted from raw score

Question 34

homogeneity/diversity of results

Answer 34

*same mean /different samples

Question 35

standard deviation vs. variance

Answer 35

sd - more intuitive -in the units we're working with - in a normal distribution, we calculate percentiles of any given score - what is included in research error variance - best for theory and development - squaring makes all numbers positive - makes differences stand out -good and bad - exploratory data analysis

Question 36

z-scores

Answer 36

* standardizes scores on two distributions - allows us to make easy comparisons - tells you if each score is above or below the mean - how far the score is from the mean (in sd) - needed for correlations

Question 37

correlations

Answer 37

* cannot infer cause and effect | * restriction of range -limitations to possible values

Question 38

curvilinear relationship

Answer 38

*correlation coefficients only test linear relationships

Question 39

effect size

Answer 39

*a term for a general set of indices to test the magnitude of relationships-pearson r

Question 40

size of effects for pearson r

Answer 40

.15 small .39 medium .40 large

Question 41

multiple correlation (R)

Answer 41

``` uses multiple predictors, not just one (as in a simple r) equation= Y= b1X1+b2X2+b3X3..etc b1=college grades b2=GRE grades b3=conscientiousness ```

Question 42

partial correlation

Answer 42

correlation between an IV and DV when some other variable is 'held constant' partially out y=b0+b1x1+b2x2 *more than one predictor(IV) is in the model, so the model has "partial correlations."

Question 43

structural equation modeling (sem)

Answer 43

* display the expected pattern of relationships among a set of variables - assumes that there is a causal relationship - predictors/outcomes * equation y=b1x1+b2x2+b3x3...bnxn