chapter 2 means to an end: computing and understanding averages part ii igma freud &...
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Chapter 2 Means to an End:
Computing and Understanding Averages
Part IISigma Freud & Descriptive
Statistics
What you will learn in Chapter 2
Measures of central tendency
Computing the mean for a set of scores
Computing the mode and median
Selecting a measure of central tendency
Measures of Central Tendency
The AVERAGE is a single score that best represents a set of scores
Averages are also know as “Measures of Central Tendency”
Three different ways to describe the distribution of a set of scores…Mean – typical average scoreMedian – middle scoreMode – most common score
Computing the Mean
Formula for computing the mean
“X bar” is the mean value of the group of scores
“” (sigma) tells you to add together whatever follows it
X is each individual score in the groupThe n is the sample size
XX
n
Things to remember…
N = population n = sampleSample mean is the measure of central
tendency that best represents the population mean
Mean is VERY sensitive to extreme scores that can “skew” or distort findings
Average means the one measure that best represents a set of scoresDifferent types of averagesType of average used depends on the data
and question
Weighted Mean Example
List all values for which the mean is being calculated (list them only once)
List the frequency (number of times) that value appears
Multiply the value by the frequencySum all Value x FrequencyDivide by the total Frequency (total n size)
Computing the Median
Median = point/score at which 50% of remaining scores fall below and 50% fall above.
NO standard formulaRank order scores from highest to lowest or
lowest to highestFind the “middle” score
BUT…What if there are two middle scores?What if the two middle scores are the same?
A little about Percentiles…
Percentile points are used to define the percent of cases equal to and below a certain point on a distribution75th %tile – means that the score received is
at or above 75 % of all other scores in the distribution
“Norm referenced” measureallows you to make comparisons
Computing the Mode
Mode = most frequently occurring scoreNO formula
List all values in the distributionTally the number of times each value occursThe value occurring the most is the mode
Democrats = 90Republicans = 70Independents = 140: the MODE!!
When two values occur the same number of times -- Bimodal distribution
When to Use What…
Use the Mode when the data are categorical
Use the Median when you have extreme scores
Use the Mean when you have data that do not include
extreme scores and are not categorical
Using SPSS
Glossary Terms to Know
AverageMeasures of Central Tendency
MeanWeighted meanArithmetic mean
MedianPercentile pointsOutliers
Mode
Chapter 3 Viva La Difference: Understanding
Variability
Part IISigma Freud & Descriptive
Statistics
What you will learn in Chapter 3
Variability is valuable as a descriptive toolDifference between variance & standard
deviationHow to compute:
RangeStandard DeviationVariance
Why Variability is Important
VariabilityHow different scores are from one particular
scoreSpreadDispersion
What is the “score” of interest here?Ah ha!! It’s the MEAN!!
So…variability is really a measure of how each score in a group of scores differs from the mean of that set of scores.
Measures of Variability
Three types of variability that examine the amount of spread or dispersion in a group of scores…Range Standard DeviationVariance
Typically report the average and the variability together to describe a distribution.
Computing the Range
Range is the most “general” estimate of variability…
Two types…Exclusive Range
R = h - lInclusive Range
R = h – l + 1
(Note: R is the range, h is the highest score, l is the lowest score)
Computing Standard Deviation
Standard Deviation (SD) is the most frequently reported measure of variability
SD = average amount of variability in a set of scores
What do these symbols represent?
Why n – 1?
The standard deviation is intended to be an estimate of the POPULATION standard deviation…We want it to be an “unbiased estimate”Subtracting 1 from n artificially inflates the
SD…making it largerIn other words…we want to be
“conservative” in our estimate of the population
Things to Remember…
Standard deviation is computed as the average distance from the mean
The larger the standard deviation the greater the variability
Like the mean…standard deviation is sensitive to extreme scores
If s = 0, then there is no variability among scores…they must all be the same value.
Computing Variance
Variance = standard deviation squared
So…what do these symbols represent? Does the formula look familiar?
Standard Deviation or Variance
While the formulas are quite similar…the two are also quite different.Standard deviation is stated in original unitsVariance is stated in units that are squared
Which do you think is easier to interpret???
Using the Computer to Compute Measures of Variability
Glossary Terms to Know
VariabilityRangeStandard deviation
Mean deviationUnbiased estimate
Variance
Chapter 4 A Picture is Really Worth a Thousand
Words
Part IISigma Freud & Descriptive
Statistics
What you will learn in Chapter 4
Why pictures are worth “a thousand words”
How to create:HistogramPolygonOther charts/graphs
Using SPSS to create & modify charts
Different types of charts and their uses
Why Illustrate Data?
When describing a set of scores you will want to use two things…One score for describing the group of data
Measure of Central TendencyMeasure of how diverse or different the
scores are from one anotherMeasure of Variability
However, a visual representation of these two measures is much more effective when examining distributions.
Ten Ways to a Great Figure
Minimize the “junk”Plan before you start creatingSay what you mean…mean what you sayLabel everythingCommunicate ONE ideaKeep things balancedMaintain the scale in the graphRemember…simple is bestLimit the number of wordsThe chart alone should convey what you
want to say
Frequency Distributions
Method of tallying, and representing the number of times a certain score occursGroup scores into interval classes/ranges
Creating class intervalsRange of 2, 5, 10, or 20 data points10-20 data points cover the entire range of
dataLargest interval goes at the top
HistogramsClass Intervals Along the x-Axis
HistogramsHand Drawn Histogram
HistogramTally-Ho Method
Frequency PolygonA “continuous line that represents the
frequencies of scores within a class interval”
Cumulative Frequency Distribution
Fat & Skinny of Frequency Distributions
Distributions can be different in four different ways…Average valueVariabilitySkewnessKurtosis
Average Value
Variability
SkewnessPositive & Negative Skewness
KurtosisPlatykurtic & Leptokurtic
Cool Ways to Chart DataColumn Chart
Cool Ways to Chart DataLine Chart
Cool Ways to Chart DataPie Chart
Using the Computer to Illustrate Data
Creating Histogram Graphs
Using the Computer to Illustrate Data
Creating Bar Graphs
Using the Computer to Illustrate Data
Creating Line Graphs
Using the Computer to Illustrate Data
Creating Pie Graphs
Glossary Terms to Know
Frequency distributionClass intervalHistogramFrequency PolygonCumulative Frequency Distribution
Ogive SkewnessKurtosis
PlatykurticLeptokurtic