Download Basics of Data Analysis

Basic Data Analysis
Levels of Scale Measurement & Suggested
Descriptive Statistics
Creating & Interpreting Tabulation
• Tabulation
– Orderly arrangement of data in a table or other
summary format showing the number of responses to
each response category.
– Called “Tallying” when the process is done by hand.
• Frequency Table
– Table showing the different ways respondents
answered a question.
– Sometimes called a marginal tabulation.
A Typical Table
Gender
Female
Male
Missing
Total
Frequency Percentage
Valid %
100
= 100/150
= 100/145
45
= 45/150
= 45/145
5
= 5/150
150
= = (100+45)
(100+45+5)
/ 145
/150
CROSS-TABULATION
• Analyze data by groups or categories
• Compare differences
• Percentage cross-tabulations
Different Ways of Depicting the Cross-Tabulation of
Biological Sex and Target Patronage
Another Typical Cross-Tab Table
Gender X
E-Commerce
Customer
Female
Male
Totals
Customer
Non-Customer
Totals
100
50
150
75
60
135
175
110
285
Data Transformation
• A.K.A data conversion
• Changing the original form of the data
to a new format
• More appropriate data analysis
• New variables
– Summated
– Standardized
Degrees of Significance
• Mathematical differences
• Statistically significant differences
• Managerially significant differences
Hypothesis Testing Procedure
• The specifically stated hypothesis is derived from
the research objectives.
• Sample is obtained & relevant variable measured.
• Measured sample value is compared to value
either stated explicitly or implied in the
hypothesis.
– If the value is consistent with the hypothesis, the
hypothesis is supported, or not rejected.
– If the value is not consistent with the hypothesis, the
hypothesis is not supported, or is rejected.
Type I & Type II Errors
• Type I Error
– An error caused by rejecting the null hypothesis when it is true.
– Has a probability of alpha (α).
– Practically, a Type I error occurs when the researcher concludes that a
relationship or difference exists in the population when in reality it does
not exist.
• Type II Error
– An error caused by failing to reject the null hypothesis when the
alternative hypothesis is true.
– Has a probability of beta (β).
– Practically, a Type II error occurs when a researcher concludes that no
relationship or difference exists when in fact one does exist.
The Law and Type I & Type II Errors
• Our legal system is based on
the concept that a person is
innocent until proven guilty
(null hypothesis)
• If we make a Type I error, we
will send an innocent person to
prison, so our legal system
takes precautions to avoid
Type I errors.
• A Type II error would set a
guilty person free.
Differences Between Groups
•
•
•
•
Primary tests used are ANOVA and MANOVA
ANOVA = Analysis of Variance
MANOVA = Multiple Analysis of Variance
Significance Standard:
– Churchill (1978) Alpha or Sig. less than or equal to
0.05
• If Sig. is less than or equal to 0.05, then a
statistically significant difference exists between
the groups.
Example
• Hypothesis: No difference exists
between females and males on
technophobia.
• If a statistically significant difference
exists, we reject the hypothesis.
• If no s.s. difference exists, we fail to
reject.
Example
• Hypothesis: Males are more technophobic
then females (i.e., a difference does exist)
• If a statistically significant difference
exists, and it is in the direction predicted,
we fail to reject the hypothesis.
• If no s.s. difference exists, or if females are
statistically more likely to be technophobic,
we reject the hypothesis.
Testing for Significant Causality
• Simple regression or Multiple regression
• Same standard of significance (Churchill 1978)
• Adj. R2 = percentage of the variance in the
dependent variable explained by the regression
model.
• If Sig. is less than or equal to 0.05, then the
independent variable IS having a statistically
significant impact on the dependent variable.
• Note: must take into account whether the impact
is positive or negative.
Example
• Hypothesis: Technophobia positively
influences mental intangibility.
• If a technophobia is shown to
statistically impact mental intangibility
(Sig. is less than or equal to 0.05),
AND.
• The impact is positive, we fail to reject
the hypothesis.
• Otherwise, we reject the hypothesis.