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Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs...

Date post: 17-Jan-2018
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Part 1 Central Tendency
40
Descriptive Statistics
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Page 1: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

DescriptiveStatistics

Page 2: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Outline of Today’s Discussion1. Central Tendency2. Dispersion3. Graphs4. Excel Practice: Computing the S.D.5. SPSS: Existing Files6. SPSS: Entering Data

Page 3: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Part 1

Central Tendency

Page 4: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

The Research Cycle

Real World

ResearchRepresentation

ResearchResults

ResearchConclusions

Abstraction

Data Analysis

MethodologyGeneralization

***

Page 5: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Central Tendency1. One of the themes in our course will be contrasting

so-called “inferential statistics” from “descriptive statistics”.

2. Inferential statistics are used to determine (“infer”) whether two populations (or conditions) are significantly different from each other.

3. By contrast, descriptive statistics are simply used to depict (“describe”) the data in a study. We’ll focus on three descriptive measures of central tendency…

Page 6: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Central Tendency1. The crudest measure of central tendency is the mode

- the most frequently occurring score.

2. Here are some examples: The modal number of eyes is two.

The modal number of fingers per hand is five.The modal number of years to graduate is four.

Other examples?

3. A frequency distribution can have one mode, or two modes (bi-modal), or more (multi-modal).

Page 7: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Central Tendency1. The next most precise measure of central tendency is

the median - the middle score. It is the 50th percentile, i.e., the point at which half of the scores are greater and half are less.

2. The median is equal to the middle value when the data set contains an odd number of items [3, 4, 5, 6, 7, 8, 8].

3. The median is equal to the the average of the two middle two values when the data set contains an even number of items.

4. Median of [3, 5, 5, 7, 8, 8] = (5+7)/2 = 6.

Page 8: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Central Tendency1. The median is the best measure of central

tendency when one side of a frequency distribution contains a few, extreme scores.

2. Would someone give us some examples of “skewed” distributions (ones having a few extreme scores)?

Page 9: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Central Tendency1. The most commonly used measure of central

tendency is the mean - the arithmetic average.

2. There are many symbols for the mean. For a population, we use , pronounced Myou. For a sample, we simply use “M”.In computations, we use “X bar”.

3. Mean = X / N (i.e., the sum of X over N).

Page 10: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Central Tendency1. Here’s some sample syntax for the measures of

central tendency in Excel….

2. “ =mode(a1:a9)”

3. “ =median(a1:a9)”

4. “ =average(a1:a9)”

5. Questions on measures of central tendency?

Page 11: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Part 2

Dispersion

Page 12: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion1. When describing the data (i.e., when

generating DESCRIPTIVE STATISTICS), we want to know how the scores are distributed (“dispersed”) around the center.

2. There are several measures of dispersion.

3. We’ll consider two (for now), the range, and the standard deviation.

Page 13: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion1. The range is a crude measure of dispersion. It is

computed as Range = Max - Min.

2. In the set of scores [2, 4, 5, 9], the range of the scores would be Max - Min = 9 - 2 = 7 units.

3. Sometimes, rather than reporting the range, researchers will simply report the Max & Min scores.

Page 14: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion1. Now, let’s consider the standard deviation, which is

the most commonly used measure of dispersion (i.e., the counterpart to the mean).

2. Potential Pop Quiz Question: What information does the standard deviation provide, in your own words, (no equations here).

3. To compute the standard deviation, we first need to compute a few important quantities. One of these is called the Sum of Squares or SS…

Page 15: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion

1. The first step is to get the deviation of each score from the mean. Here Mean = 8.

2. Then, we square the deviations, and sum them…

Page 16: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion

1. So, the SS is the sum of the squared deviations from the mean.

2. In this case SS = 44.

Page 17: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion

1. Here are two ways to look at the SS.

2. Often, we have a definitional formula, and an equivalent computational formula.

Page 18: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion1. The next step in computing the standard

deviation is to determine the “variance”.

2. Variance - the average squared deviation from the mean (so, the variance, itself, is a mean).

3. We can compute the variance of either a population (i.e., every member of a group) or a sample (i.e., just a subset of a group)…

Page 19: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion

1. Note: The only difference is whether we divide by N (for population), or n-1 (for sample).

2. Assuming SS is constant, which formula will generate a larger variance?

Sigma “s”

Page 20: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion1. Finally, to get from squared units to “regular”

units, we need to take the square root of the variance.

2. The standard deviation is the square root of the variance. (Say it with me.)

3. This is true whether we’re talking about the SD of a population, or a sample…

Page 21: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion

The standard deviation is the square root of the variance!!

Sigma “s”

Page 22: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion

Another way to express the Standard Deviation(we’ve substituted the SS formula in the numerator)

Page 23: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion1. Phew!! That was a lot!!! Let’s review the

concepts in dispersion.

2. We have focused on two measures of dispersion; Range and Standard Deviation (SD);

3. Range is simple. It’s the Max - Min.

Page 24: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Dispersion1. The standard deviation indicates, approximately,

how far, on average, a score departs from the mean.

2. The standard deviation depends on some important quantities - the SS and the Variance.

3. The formula for the population variance (based on N) is slightly different than that for the sample variance (based on N-1).

4. The standard deviation is the square root of the variance!

Page 25: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Part 3

Graphs

Page 26: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Graphs1. Let’s learn some terminology about graphs.

2. The x-axis (the horizontal axis) is called the abscissa.

3. The y-axis (the vertical axis) is called the ordinate.

4. By convention, which axis contains the IV, and which axis contains the DV?

5. When describing a graph verbally, we typically state “..in this graph, (DV) is plotted as a function of (IV).”

Page 27: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Graphs

Describe how the variables are plotted in this graph.How many “levels” does the IV have, and what are they?

Page 28: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Graphs1. Sometimes an experiment has more than one IV. This

is called a factorial experiment.

2. Graphs from factorial experiments typically plot one of the IV’s on the abscissa, and the other IV by using different symbols (sometimes called parameters) in the legend.

3. Verbally, we state, “.. in this graph, (DV) is plotted as a function of IV#1, with IV#2 as parameters.”

Page 29: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Graphs

Describe how the variables are plotted in this graph.How many IV’s, and how many levels of each?

Page 30: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Graphs

Describe how the variables are plotted in this graph.How many IV’s, and how many levels of each?

Page 31: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Graphs

Here are the data from the preceding graph.Graphs are interpreted more quickly than tables are.

Page 32: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Graphs1. Another important point about graphs is that

the ordinate should start at zero.

2. If not, the graph will lose proportionality, and become very misleading.

3. Small differences could appear huge…

Page 33: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Graphs

Which graph is “messed up”?

Page 34: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Part 3

Excel Practice:Computing the S.D.

Page 35: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Part 4

SPSS Practice:Existing Files

Page 36: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

SPSS: Existing Files1. SPSS data files have an extension of “.sav”

2. In variable view, each row corresponds to a new variable. The columns indicate how the variable is ‘defined’ to SPSS.

3. In data view, each row corresponds to a different participant (called a ‘case’ in SPSS). Each column pertains to a different variable.

4. Note that variables are in rows in variable view, but in columns in data view. (“It rotates by 90 degrees.”)

Page 37: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

Part 5

SPSS Practice:Entering Data

Page 38: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

SPSS: Entering Our Own Data1. When entering your data in SPSS, always begin in the

“VARIABLE VIEW”.

2. Each variable is in a separate row in variable view.

3. Here’s a good habit…make your first variable one that identifies the participant in some way.

4. In the type column, you should use ‘numeric’ unless you want to directly enter words on the data view. If so, you should change the type column to ‘string’.

5. In the measure column, you have three choices; Nominal, Ordinal, or Scale (which is interval and ratio).

Page 39: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

SPSS: Entering Our Own Data1. Value labels, can facilitate data coding! These are

defined in variable view.

2. Example on Religion: 1=Buddhist; 2=Hindu;3=Islamic; 4=Jewish; 5=Christian;

3. In data view, under the view menu the value labels can be ‘turned on’ or ‘off’!

Page 40: Descriptive Statistics. Outline of Today’s Discussion 1.Central Tendency 2.Dispersion 3.Graphs 4.Excel Practice: Computing the S.D. 5.SPSS: Existing Files.

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