l643: evaluation of information systems week 13: march, 2008
TRANSCRIPT
L643: Evaluation of Information Systems
Week 13: March, 2008
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Data Collection
1. Zipcar
2. Evergreen
3. Quandrem
4. Unicoop
5. LibraryThing
6. Fluvog Shoes & Boots
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Ten Ways to A great Figure (Salkind, 2007, p.83)
Keep it simple
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Ten Ways to A great Figure (Salkind, 2007, p.83)
Keep it simple
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Ten Ways to A great Figure (Salkind, 2007, p.83)
Keep it simple
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Ten Ways to A great Figure (Salkind, 2007, p.83)
Label everything so nothing is left to the misunderstanding of the audience
A chart alone should convey what you want to say
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Graphic Presentation
A line chart to show a trend in the data at equal intervals
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Graphic Presentation
A pie chart to show the proportion of an item that makes up a series of data points [usually for nominal (e.g., level of computer experience) and ordinal (e.g., age 18-34, 35-44, 45-54, 55-64, above 64) variables]
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Graphic Presentation
Times series charts => variables change over time
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Influence of Research(ers)
The Hawthorne Effect Individual behaviors altered because they know
they are being studied See more info at:
http://www.envisionsoftware.com/articles/Hawthorne_Effect.html
http://www.psy.gla.ac.uk/~steve/hawth.html
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Descriptive Statistics
Why do we need statistics? 2 ways to summarize or describe a set of
data According to how the individual pieces of
information cluster together (measuring central tendency)
According to how individual cases spread apart (measures of dispersion)
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Measures of Central Tendency
3 most common measures of central tendency: Mean (average) Median (midpoint) Mode (mode (most frequent value(s))
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Central Tendency (Salkind, 2000)
Mean the arithmetic average of all scores
Median the point that divides the distribution of scores in
half
Mode the most frequently occurring score(s)
XX
n
1
2
N
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Measures of Central Tendency
Mean Is a very accurate measure of central tendency
with fairly equal distribution Is the most important statistically of central
tendency (c.f., t-test; the analysis of variance)
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Measures of Central Tendency
Mean The sum of the individual values for each variable
divided by the the number of cases
X =Sum of scores
Number of scores
~
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Measures of Central Tendency
Mean Mean tells you the balance point, or the average
of the set of values
With a normal distribution, it is likely to be the same # of scores both above and below the mean
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Measures of Central Tendency
Median The midpoint of a set of ordered numbers To find the median, arrange the numbers from
smallest to largest It’s useful for distributions that are positively or
negatively skewed
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Measures of Central Tendency
Normal distribution
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Measures of Central Tendency
Normal distribution Skewed distribution
Positively skewed—with a few very high scores
Negatively skewed—with a few very low scores
Note: in positively skewed distributions, the mean is likely to be misleadingly high
In negatively skewed distributions, the mean is likely to misleadingly low
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Curves
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Measures of Central Tendency
Mode The most frequent score(s) in a distribution Why use the mode?
It’s not so useful with a normal distribution It is useful for categorical data
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Curves
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Curves
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Measurement Scales Nominal (categorical or qualitative) scale
E.g., what type of car do you have? Cf., Salkind chapter 2
Mode
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Measures of Central Tendency
In summary If a measure of central tendency of categorical
data, use only the mode Use the median when you have extreme
scores Use the mean when no extreme scores and
no categorical data
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Measures of Dispersion
Variability Mean (4)
7, 6, 3, 3, 1 3, 4, 4, 5, 4 4, 4, 4, 4, 4
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Measures of Dispersion
The most common measures of scatter, or dispersion, are: Range Standard deviation Variance
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Measures of Dispersion
Range It is calculated by subtracting the lowest score
(minimum) from the highest score (maximum) in a distribution of values
It is not at all sensitive to the distribution of scores between min and max
What’s the range of the following set? 7, 6, 3, 3, 1 3, 4, 4, 5, 4
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Measures of Dispersion
Standard deviation It is the average distance from the mean
1. Each score is subtracted from the mean
2. The difference is squared to eliminate any negative values and to give additional weight to extreme cases
3. These squared differences are added together & divided by the number of scores
S = N - 1
(x – X)2
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Measures of Dispersion
Standard deviation The larger the standard deviation, the more spread out the
values are, and the more different they are from one another
Unlike the range, the SD is sensitive to every score in a distribution of scores
If the standard deviation = 0, there is no variability in the set of scores, and they are identical in value, which rarely happens.
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Measures of Dispersion
To calculate the variance The variance is simply the standard
deviation squared, i.e., s2.
S = N-1
(x – X)22
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Standard Deviation vs. Variance
Both measures of variability, dispersion, or spread
SD is stated in the original units from which it is derived
Variance is in units that are squared
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Relationships
Relationships are important to examine because: answering research questions to examine, e.g.,
relationships between independent variables and dependent variables
suggesting new hypotheses and/or Qs
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Correlation
Variable X Variable Y Type of Correlation
Value Example
X increases in value
Y increases in value
Direct Positive
.00 to +1.00
The more memory a machine has, the faster the machine becomes
X decreases in value
Y decreases in value
Direct Positive
.00 to +1.00
The fewer the links to a website, the lower the ranking on google appears
X increases in value
Y decreases in value
Indirect Negative
-1.00 to .00
The more time you spend time on an IS, the lower the productivity shows
X decreases in value
Y increases in value
Indirect Negative
-1.00 to .00
The less time spent on training, the mistakes on data entry increases
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Correlation
r = N XY - X Y
[NX – (X) ] [NY - (Y) ] 2 2 2 2
.0 .2 .4 .6 .8 1.0
XY
Weak or norelationship
Weak relationship
Moderaterelationship
Strongrelationship
Very strongrelationship
Note: The association between 2 or more variables has nothing to do with causality (e.g., ice cream & crime rate)
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Groups of Correlations
The correlation matrix
Info quality User satisfaction
Attitude Productivity
Info quality --- .574 -.08 .291
User satisfaction
.574 --- -.149 .199
Attitude -.08 -.149 --- -.169
Productivity .291 .199 -.169 ---
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Summary of Descriptive Statistics
Descriptive statistics are summaries of distributions of measures or scores
These summaries are useful because of the large and complex nature of different quantitative studies, such as surveys, content analyses, or experiments