Midterm

Question 1

Hypothesis

Answer 1

Answer to the research question that may or may not be right

Question 2

Testable Hypothesis

Answer 2

A version of your hypothesis that you can test with empirical data and evidence

Question 3

Causal Mechanism

Answer 3

Showing a relationship between two variables. How they are related

Question 4

Independent Variable

Answer 4

X; phenomenon doing the explaining

Question 5

Dependent Variable

Answer 5

Y; phenomenon being explained

Question 6

Internal Criteria

Answer 6

How does the hypothesis fare?

Question 7

Falsifiability

Answer 7

The hypothesis must be capable of being proven wrong

Question 8

Parsimony

Answer 8

Hypothesis must be able to explain while using as few explanatory variables as possible

Question 9

Encompassing

Answer 9

Hypothesis must be able to apply to a lot of different cases

Question 10

Concrete

Answer 10

Concepts cannot be too abstract, must be clear and have sound reasoning

Question 11

Operational Definition

Answer 11

Very specific definitions of variables that will enable us to gather data

Question 12

Reliability

Answer 12

When testing the hypothesis, the same result must be obtained if the measurement process is repeated

Question 13

Validity

Answer 13

The variables accurately reflects the abstract concept of interest.
Validity implies reliability, but NOT the other way around.

Question 14

Scales of Measurement

Answer 14

AKA Measure Precision

Question 15

Nominal

Answer 15

With qualitative data, unordered categories

ie; single, married, divorced

Question 16

Ordinal

Answer 16

With quantitative or qualitative data, ordered categories

ie; social class, type of education

Question 17

Interval

Answer 17

With quantitative data, numeric values with a significant distance between them
ie; amount of education, average temp

Question 18

Qualitative Data

Answer 18

Numbers are not involved

Question 19

Quantitative Data

Answer 19

Numbers are involved, central to data

Question 20

Categorical

Answer 20

Has categories to classify data

ie; type of education, marital status

Question 21

Non-categorical

Answer 21

ONLY numbers, with interval data

Question 22

Discrete Data

Answer 22

Data with a definite number or count

Question 23

Continuous Data

Answer 23

An infinite number of values can be measured

ie; ages

Question 24

Central Tendency

Answer 24

Identifying frequently occurring values

ie; mean, median, and mode

Question 25

Mean

Answer 25

Add up values and divide by number of cases
Use the mean when you have data that does not include extreme scores and are not categorical
ie; “Average”

Question 26

Median

Answer 26

The midpoint in a set of scores
value of the ((n+1)/2)th case (n is # of cases)
Most commonly used for ordinal scale variables
Use the median when you have extreme scores and you don’t want to distort the average

Question 27

Mode

Answer 27

Most frequently reported/utilized category in a data set

Us the mode when the data is categorical

Question 28

Dispersion

Answer 28

Reflects how data points differ from one another

ie; range, variance, standard deviation

Question 29

Range

Answer 29

Difference between the values of the smallest and largest variables

Question 30

Variance

Answer 30

Just know how to interpret

Bigger values mean more dispersion, but otherwise its kind of hard to interpret

Question 31

Standard Deviation

Answer 31

Average difference/distance from the mean

Bigger numbers mean more dispersion

Question 32

Scatterplot

Answer 32

For ordinal and interval scale variables

Question 33

“Chart Junk”

Answer 33

Un-necessary chart material that distracts from the data and makes it difficult to interpret

Question 34

Graphical Integrity

Answer 34

Bad graphs misrepresent data through distortion, changing scale of measurement, etc. which lacks integrity

Question 35

Pearson’s Correlation Coefficient

Answer 35

Quantifies strength and direction of relationship of variables
Ranges from -1 to 1
The larger the ABSOLUTE VALUE, the stronger the relationship
Positive sign indicated a positive relationship and vice versa

Question 36

Coefficient of Determination

Answer 36

R^2 - Proportion of shared variance
Ranges from 0 (0%) to 1 (100%)
Square of Pearson’s Correlation Coefficient

Question 37

Spearman’s Correlation Coefficient

Answer 37

Similar to Pearson’s in all ways

EXCEPT—can be used for either two ordinal scale variables or for one ordinal and one interval scale variable

Question 38

Regression Line/Linear Regression

Answer 38

Trend lines in a graph

Works for any combination of ordinal and interval scale variables

Question 39

Y = a + bx + e

Answer 39

Linear Regression Model

Question 40

y

Answer 40

dependent variable

Question 41

x

Answer 41

independent variable

Question 42

a

Answer 42

y-intercept of the line

Question 43

b

Answer 43

slope of the line

Question 44

slope

Answer 44

rise/run

Question 45

e

Answer 45

random error (not always there)

Question 46

Ordinary Least Squares

Answer 46

Estimation of a Regression Line

The line with the smallest squared distance

Question 47

Regression Prediction Equation

Answer 47

Y’ = a + bx

Question 48

Slope Coefficient

Answer 48

the “b” in “Y’ = a + bx”

Predicted change in Y as X increases by 1 unit

Question 49

Crosstab

Answer 49

For two qualitative variables
conditional distributions identical = no association
conditional distributions differ = association

Question 50

Chi-Squared Statistic

Answer 50

For two qualitative variables
minimum value = 0 : NO association
Non-zero values indicate an association: a bigger number equals a stronger association