L643: Evaluation of Information Systems Week 13: March, 2008

2 Data Collection 1. Zipcar 2. Evergreen 3. Quandrem 4. Unicoop 5. LibraryThing 6. Fluvog Shoes & Boots

3 Ten Ways to A great Figure (Salkind, 2007, p.83) Keep it simple

4 Ten Ways to A great Figure (Salkind, 2007, p.83) Keep it simple

5 Ten Ways to A great Figure (Salkind, 2007, p.83) Keep it simple

6 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

7 Graphic Presentation A line chart to show a trend in the data at equal intervals

8 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, , 45-54, 55-64, above 64) variables]

9 Graphic Presentation Times series charts => variables change over time

10 Influence of Research(ers) The Hawthorne Effect Individual behaviors altered because they know they are being studied See more info at: Effect.html Effect.html

11 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)

12 Measures of Central Tendency 3 most common measures of central tendency: Mean (average) Median (midpoint) Mode (mode (most frequent value(s))

13 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)

14 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)

15 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 ~

16 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

17 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

18 Measures of Central Tendency Normal distribution

19 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

20 Curves

21 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

22 Curves

23 Curves

24 Measurement Scales Nominal (categorical or qualitative) scale E.g., what type of car do you have? Cf., Salkind chapter 2 Mode

25 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

26 Measures of Dispersion Variability Mean (4) 7, 6, 3, 3, 1 3, 4, 4, 5, 4 4, 4, 4, 4, 4

27 Measures of Dispersion The most common measures of scatter, or dispersion, are: Range Standard deviation Variance

28 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

29 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 

30 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.

31 Measures of Dispersion To calculate the variance The variance is simply the standard deviation squared, i.e., s 2. S = N-1  (x – X) 2 2

32 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

33 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

34 Correlation Variable XVariable YType of Correlation ValueExample X increases in value Y increases in value DirectPositive.00 to The more memory a machine has, the faster the machine becomes X decreases in value Y decreases in value DirectPositive.00 to The fewer the links to a website, the lower the ranking on google appears X increases in value Y decreases in value IndirectNegative to.00 The more time you spend time on an IS, the lower the productivity shows X decreases in value Y increases in value IndirectNegative to.00 The less time spent on training, the mistakes on data entry increases

35 Correlation r = N  XY -  X  Y [N  X – (  X) ] [N  Y - (  Y) ] XY Weak or no relationship Weak relationship Moderate relationship Strong relationship Very strong relationship Note: The association between 2 or more variables has nothing to do with causality (e.g., ice cream & crime rate)

36 Groups of Correlations The correlation matrix Info qualityUser satisfaction AttitudeProductivity Info quality User satisfaction Attitude Productivity

37 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