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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 bimodel 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 4.5Even amounts of numbers (345678910) Median would be 6odd 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


PieCharts
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 subsets.
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 
Selectmost impt. Variable to study
State what is mfeant by each variable Specifyhow 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 variablesoften stated in the future tense because it predicts what will be foundexpresses 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 subsets
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 valueminimum 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 (3530+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 (3530+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
