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57 Cards in this Set

  • Front
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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
Bi-modal
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.