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57 Cards in this Set
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A numerical representation of information is a

Statistic

Weight is an example of this type of variable

Continuous

List 2 types of quantitative variables

Discrete, continuous

These variables can not be directly measured, but are inferred

Latent

These variables can be observed and directly measured

Observable

These variables are numeric in nature

Quantitative

These variables are nonnumeric in nature

Qualitative

These numeric variables measure "how many"  they cannot be subdivided

Discrete

These numeric variable measure "how much"

Continuous

List 4 scales of measurement

Nominal, Ordinal, Interval, Ratio

This type of scale of measurement has discrete, qualitative variables

Nominal

This scale of measurement has qualities including magnitude, equal intervals, and absolute 0

Ratio

This scale of measurement has qualities including magnitude and equal intervals

Interval

This scale of measurement only has the quality of magnitude

Ordinal

This scale of measurement has no special qualities, but includes things like names or lists of words.

Nominal

A Likert scale or rank ordered scale is an example of this type of scale of measurement

Ordinal

Temperature is an example of this type of scale of measurement

Interval

Age, height, and weight scales are examples of this type of scale of measurement

Ratio

You can multiply or divide items on this type of scale of measurement

Ratio

You can add and subtract items on this type of scale of measurement, but cannot multiply or divide

Interval

List 4 general ways in which researchers and test developers describe statistics

Frequency,
Central Tendency, Variability, Relationships 
This is a way to show a disorganized set of scores and place them in order, showing how many (people) obtained each of the scores

Frequency Distribution

This type of graph uses vertical lines and bars to portray the distribution of test scores.

Histogram

This variation of a histogram replaces bars with lines connecting the midpoint of each class interval.

Frequency Polygon

This graph gives us a better idea of the shape of the distribution as well as the frequency of scores

Smoothed Frequency Polygon (or frequency curve)

A frequency curve (smoothed frequency polygon) that is not symmetrical is called a

Skewed curve

In this skewed curve, the majority of data falls on the lower end of the scale

Positively skewed curve

In this skewed curve, the majority of data falls on the upper end of the scale

Negatively skewed curve

Three common measures of central tendency are the

Mean,
Median, Mode 
The average score in a distribution is referred to as the

Mean

Given these numbers, calculate the mean, median, and mode:
78,85,86,90,98,100,100,102,110,115,142,146,165 
Mean = 109
Median = 100 Mode = 100 
In descriptive statistics, this type of central tendency has two modes

Bimodal

This measure of central tendency is the middle score, or the score that divides the distribution in half

Median

For symmetrical distributions, these 3 measures of central tendency are equal

Mean
Median Mode 
In central tendency measures, this is the score that appears most frequently

Mode

Calculate the range of these numbers:
1,2,2,3,3,3,4,4,5,5,6,6,40 
39

Range is an example of the measurement of these types of descriptive statistics

Variability

This measure describes the average distance of test scores from the mean

Standard deviation

This curve has the following properties:
*bell shaped, *bilaterally symmetrical, *mean, median and mode are equal to each other *Asymptotic tails *Unimodal *100% of the scores fall between 3 and +3 standard deviations from the mean 
Normal Curve (aka normal distribution or bell curve)

68% of a population within a normal curve falls between what standard deviations

1 and +1 standard deviations

This measure of relationship indicates that two variables move in the same direction

Positive correlation

This measure of relationship indicates that two variables move in opposite directions

Negative correlation

A complete absence of a relationship between 2 variables is indicated by this number

0

A perfect positive relationship between two variables has this correlation coefficient

+1

A perfect negative relationship between two variables has this correlation coefficient

1

The number of violent crimes committed is strongly positively correlated with the number of ice cream sales at a given time. Does this mean that one causes the other?

No. Correlation does not mean causation.

Linear relationships between two continuous variables are measured using this type of correlation coefficient

Pearson Product Moment Correlation (r)

A variant of Pearson's r used for finding the association between two ordinal variables and does not require a linear relationship between variables is

Spearman's Rho (r)

Likert scales often use this type of correlation coefficient measure

Spearman's Rho (r)

The degree of association between two nominal variables is assessed by this correlation coefficient measure

Phi Coefficient

This is used to assess the size and direction of a relationship between variables

Correlation

This statistical method is used in the analysis of relationships among variables for predictive purposes

Regression

In measurements of relationship, this is used to predict the value of one variable based on the value of another single variable

Simple linear regression

In measurements of relationship, this is used to predict the value of one variable based on the value of two or more independent variables

Multiple regression

This analyzes the relationship among variables for purposes of reducing the number of necessary variables

Factor Analysis

Creat demographic questionnaire for study with 5 questions/statements including: 2 nominal, 1 ratio, 1 ordinal, and 1 interval scale. Identify each type.

Nominal:
Relationship Status, Gender Ordinal: Rate your interest in taking this test on a scale from 1 to 5 . . . Interval: What was your temperature in Fahrenheit degrees on your last doctor's visit Ratio: What is your current weight 
Difference between regression, factor analysis, and spearman's rho. Explain how you would determine which analysis to conduct and provide an example of each.

The all are measurement methods involving relationships between variables, however they are used for different purposes. Regression measures correlation strength and direction of a relationship between variables. There are different types of regression (Simple linear and Multiple Regression) analysis; all are used for predictive purposes.
(example  if there is a strong positive correlation between IQ and grades, I could take one of the two variables and figure out the other (dependent) variable based on that. Spearman's Rho is a way to calculate the correlation coefficient (used in regression). It is used to find the relationship between ordinal variables and doesn't require a linear relationship between the variables. You would use Spearman's Rho to figure out if a correlation between 2 ordinal variables exists. Factor Analysis is used to simplify variables. You would use it to reduce the number of questions necessary on a given test while still yielding accurate results. 