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Basic Statistics
Vernon E. Reyes
Topics
1. What is statistics in relation to social science
research?
2. Organizing data
3. Measures of central tendency
4. Measures of variability
5. Probability, samples and population
6. Testing differences between means
7. ANOVA
8. Chi-Square
9. Correlation
10. Spearmans ro
Social Science Research
• Social science = study of the obvious?
Thus there is a need to test hypotheses
Example: Mass Murderers
*which of the following are obvious and
therefore not worth studying?
Mass murderers?
• Mass murderers are
almost ALWAYS insane
(a person who is sane will
never kill people who are
having lunch)
• Mass murderers are
usually strangers to their
victims who are unlucky
enough to be at the
wrong place and at the
wrong time
• Mass murderers typically
run amok, expressing
themselves in a
spontaneous outpouring
of anger
• Mass murderers look
different from the rest of
us
However…
• 697 mass killers from 1976 – 1995
- Mass murderers are rarely insane, they know
what they are doing and NOT because “voices
of demons”
- Random shootings in public places are rare,
most killings happen within families or
acquiantances
- Most murderers are methodical and selctive
NOT impulsive or spontaneous
- Murderers do not look different, they look just
ordinary people
Social science research
• Social scientists attempt to explain and predict
human behavior
• In the process, soc. Scientists examine
characteristics of human behaviors called
variables – characteristics that differ from one
individual to another (i.e. age, social class,
attitude) or from one point in time to another (i.e.
unemployment, crime, population).
• Constant: gender of mothers
• Variable: age, race, mental health of mothers
Are there different variables?
• Independent variable/s (IV)
- This is the one presumed to cause the
changes in the DV
• Dependent variable/s (DV)
- This is one presumed to to change or be
the effect because of your IV.
Give your own examples…
Series of numbers for social
science research
• Series of numbers have a level of measurement.
1. Classify or categorize = nominal level of
measurement
2. Rank or order = ordinal levels of measurement
3. Assign a score = interval level (no true value of
zero, i.e. temperature) or ratio (has an absolute
zero i.e. weight, height, etc) of measurement
NOMINAL LEVEL
• Involves naming or labeling, meaning we
place cases into categories and count
frequency
• Examples: gender (male, female), political
party (Lakas, Liberal, Nacionalista), time
(past, present, future) etc
Notes for nominal data
• Every case must be placed in one, and
only 1, category = categories must not be
overlapping or mutually exclusive
• Must be exhaustive = a place for every
case that arises
• Nominal data are not graded, ranked or
scaled… i.e. better or worse, higher or
lower, more or less
Ordinal Level
• Degree of ranking
• Example: instead of just categorizing
nationalists versus not nationalist, we
might want to study the degree of
nationalism
• Not a measure of magnitude. i.e. we do
not know how nationalist one respondent
from another.
• Example, beauty contests
Interval/Ratio level
• Not only shows order but also exact
distance of one score to another.
• Uses constant units of measurements
(pesos, celsius, yards, feet, minutes) and
yield equal intervals
What is what?!
• There is clear distinction between nominal
data and ordinal data
Example:
color of hair [black, brown, blonde] (nominal) vs
condition of hair [dry, normal, oily] (ordinal)
• Distinction between ordinal and interval is not
always clear cut
Scale value
Rank of
professor
Attitude toward
professor
1
University professor Very favorable
2
Full professor
Favorable
3
Assoc. professor
Somewhat favorable
4
Asst. Professor
Neutral
5
Instructor
6
Lecturer
Somewhat
unfavorable
Unfavorable
7
Teaching asst.
Very unfavorable
From the previous table
• Difference between instructor and lecturer is minimal in
terms of prestige, salary or qualifications
• The difference between instructor and asst. professor is
substantial wherein there should be at least a doctorate
and higher salary
• Attitude is evenly spaced
ie strongly agree – strongly diagree
 instead, whenever possible, we treat ordinal
variables as interval ONLY if safe to assume that
the scale has relatively equal intervals!
Attitude = interval and Rank of Prof = ordinal only!
Functions of Statistics
• Description
• Decision making / inferences