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80 Cards in this Set

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Mode (NOIR)
Category or score that occurs the most
NOIR- Only one in which you can use Nominal level variables
Highest # of repeating data
Eg. (122579) the mode would be (2)
(12233444567) mode=(3,4 are bi-model values)
Median (OIRI)
Divides an array of values into two equal halves, midpoint
least effected by outliers of all three, good to use with outliers
Eg. (1223456789) Median 4.5-Even amounts of numbers
(345678910) Median would be 6-odd numbers
Mean
Ratio- the average of values,highest level of measurement
best represents the central tendency
use w/ largest # of cases, even w/ outliers
good with large # of values
Add ll values in data set & divide by the total number of values in set
Eg. (3,4,5,6,7,10,11) 46/7=6.57
What are the 4 levels of measurement?
Nominal
Ordinal
Interval
Ratio
Nominal
Categorizes values into descrete subclasses
Must have 2 or more values
No numerical value
Uses value categories
Only use w/mode
Eg, Do you believe govt. should provide health care?
1. yes 2. no 3. undecided
Ordinal
Numerical values, preserves rank order
Can rank order values from high to low and most to least
Use w/median
Does not indicate absolute quantities or assume equal intervals btwn. categories
Eg. How would you rate your social worker? 1 very good 2. good etc.
Interval
Can use to rank order diff. measurements of a variable
Places the value for the variable on an equally spaced continuum
Preserves rank order
Ratio
preserves rank order, unit differences and fixed zero points
Skewed Distribution
Symmetrical, ends do not tapper off
Positively skewed= tail curves to the right
Negatively skewed= tail curves to the left
Normal distributions
Symmetrical bell shaped curve that often arises when a trait is composed of a large number of random independent factors; the curve possesses a specific mathematical formula
Interval & Ratio Level
Normal Curve
Interval or ratio level variable that is normally distributed represented by a bell shape curve
The Mode,Median & Mean all
occur at the highest point in the center of the distribution
Graph types
bar
line
pie
histogram
Pareto
frequency polygons
stem & leaf
Graphs
can communicate the bigger picture.
Improve communication
Is possible to lie w/graphs like other analysis
Bar Graph

NOIR
NOIR
The highest of each bar reflects the frequency of the value category
Bars of equal width, do not touch each other
Line
Flip the access & put the frequency on the bottom
Pie-Charts

NOIR
NOIR used w/all levels
use "absolute percents" when making pie chart
Histogram

OIR
Looks like bar, but the bars touch each other
Uses height of bar to reflect the frequency of a value or value category
Bars can be various widths, however, if the are "equal" must be Ordinal
Pareto

IR
Portrays rank ordering of values of a variable by frequency in descending order.
Reflects "cumulative frequencies & cumulative percentages" of cases at any point or when added to the group
Frequency Polygon

IR
picks points to connect to
Stem & Leaf

IR
Displays all of the actual case values in he distribution of a variable
Stem= 1st or 2nd digits of a persons age
Leaf= 2nd digit of a persons age
Which graphs are used more often?
Frequency Polygon & Histogram
Central Tendencies
Shows what is typical with the data
Trimmed Mean
Designed to minimize the effect of a few extreme outliers.
Combines the best features of Mean & Median
Usualy top 5% & the bottom 5% of values in an array are thrown out.
The remaining 90% of values are averaged. *This is trimmed mean*
Use w/small amount of values & outliers
**Less than 30 points of data use trimmed mean
Weighted Mean
The weighing fo numbers in order to arrive at a value that is more meaningful for the data set than either the arithmetic mean or trimmed mean.
A. Summarized data
B. Provide a common reference point to compare 2 groups of data
Correlation
The degree of association btwn. or among variables, figuring out trends effectiveness, some may be weak & some may be strong or moderate (does not predict what other variables will do)
Covariance
The degree to which certain values of one variable are found to be associated with certain values of other variables
Scattergram
Displays the correlation btwn. 2 or more variables, it simultaneously portrays the info. for 2 values- not more than 2 values. If you see a pattern form it could mean that a relationship exists
What is the highest correlation?
+1.0
What is the weakest correlation?
-1.0
What is NO correction?
0 (zero)
How do you determine if a relationship exists?
-1.0 0 +1.0 (these are the numbers you look at to determine if a relationship exists, if 2.5 is your #, it is wrong because you can't go over 1.0 or -1.0
1 Tail Test
Predicts relationship btwn. variables, predicts a specific direction
Eg. Watching hockey increases beer consumption (A relationship btwn. the two, it takes it sin some sort of direction either improves, or increases or diminishes etc.)
2 Tail
Predicts relationship, but no specific direction
Eg. Gender is related to job satisfaction levels (does not predict whether M or F will be found to have higher levels of job satisfaction)
Proves some sort of relationship btwn. two variables, does not increase or decrease
Null Test
x=y, says there is no difference btwn. the population & the sample population
Dichotomous Variable
Has 2 value categories
Eg. Gender, T/F
Binary Variable
Assigns numerical value to categories of 1 or 0 to indicate presence, or absence of variable. Used when coding
Dummy Variables
Created by converting a qualitative into a binary variable, breaks it into sub-sets.
Eg. gender=make & female
Information
analyzed data
Data
numbers or scores generated by research study, measurements collected in a research study
Constant
Does not differ in quality or quantity
Conceptualization
4 step process used to narrow down list of potential variables by identifying those that must be measured to get answers to our research question
Steps in Conceptualization Process

4 S's
Select-most impt. Variable to study
State- what is mfeant by each variable
Specify-how each variable is to be measured
State the value- of categories or values t hat each variable can assume
Operationalization
Specifying exactly how to measure the variable that we have conceptualized, survey/observation
Reliability
The degree of consistency of measurement
Addresses the question=To what degree does the measurement of a variable produce consistent results?
Validity
The desire to whixch a measurement instrument accurately measures what it claims to measure* using measurement instruments
Research Hypothesis
A statement of a relationship btwn. or among variables-often stated in the future tense because it predicts what will be found-expresses what we believe to be true
Eg. Smoking Pot (IV) compromises the brain
Ability to function properly (DV)
Independent Variable (IV)
The variable that is predicted to do the influencing
Dependent Variable (DV)
Believed to be influenced by the (IV)
Predictor Variable (PV)
Used for prediction
Criterion Variable
(Outcome Variable) Variables whose values we hope to predict
Discrete Variable
Can take on only an finite number of variables whose values
Dichotomous Variable
Has 2 value categories
Eg. Gender, T/F, M/F
Binary Variables
Assigns numerical value to categories of 1 or 0 to indicate presence
Used when coding
Dummy Variable
Created by converting a qualitative variable into a binary variable breaks it into sub-sets
Eg. Gender=male & female
Descriptive Analysis
Nominal variables
Reduce large amounts of data to a simpler form, easier to understand
Inferential Analysis
Ratio Variable
Used when *sample* is drawn from population & not the *total* population
Frequency
Number of observations falling into a cell or value category of a specific variable
Frequency Distribution
Table or graph, shows the # of times (frequency) w/which different variables occur in a group of observations
Absolute Frequency
Number of times each value occurred *this total should always equal the last cumulative frequency number
Cumulative Frequency
Adds the absolute value together
Absolute Percent
Uses the absolute frequency and divides it by 10 and then multiplies it by 100, turn it into %, should equal 100
Cumulative Percent
Adds the absolute %, always want it to be 100%
Valid Percent incudes what?
The missing values, *Percent* does not.
Grouped Frequencies
What is meaningful group?
Reduces the # of values to a smaller #, easier to understand, while not loosing meas
Descriptive Analysis
Nominal variables
Reduce large amounts of data to a simpler form, easier to understand
Inferential Analysis
Ratio Variable
Used when *sample* is drawn from population & not the *total* population
Frequency
Number of observations falling into a cell or value category of a specific variable
Frequency Distribution
Table or graph, shows the # of times (frequency) w/which different variables occur in a group of observations
Absolute Frequency
Number of times each value occurred *this total should always equal the last cumulative frequency number
Cumulative Frequency
Adds the absolute value together
Absolute Percent
Uses the absolute frequency and divides it by 10 and then multiplies it by 100, turn it into %, should equal 100
Cumulative Percent
Adds the absolute %, always want it to be 100%
Valid Percent incudes what?
The missing values, *Percent* does not.
Grouped Frequencies
What is meaningful group?
Reduces the # of values to a smaller #, easier to understand, while not loosing measurement precision
Variability
Indicates the degree of variation among and value categories
5 different measures of variability

RIMVS
Range
Interquartile range
Mean deviation
Variance
Standard Deviation
*Uses only Interval & Ratio to measure variability
*Communicated best in a frequency distribution or graph, such as a bar chart
Range
The distance the encompasses all values within the data set

Expressed as a formula: Range=maximum value-minimum value +1
Maximum Value
The value of the case w/ the largest value of the variable
Max age=35
MIn age=30
The range would be 6
(35-30+1=6)
There are potentially 6 different ages(or values) that are included w/in the range:
35,34,33,32,31 & 30
Minimum Value
The value of the case w/the smallest value of the variableMax age=35
MIn age=30
The range would be 6
(35-30+1=6)
There are potentially 6 different ages(or values) that are included w/in the range:
35,34,33,32,31 & 30
What is an Outlier
Data points that are far removed from and numerically distant from the rest of the points