Download Week 3 - Wei

Intro to SPSS
Lab 3
Kenny Chen
MIDTERM REVIEW!
Hypothesis Template
In a comparison of (unit of analysis),
those who are (one part of independent
variable) are more likely to (dependent
variable) than those who are (other
part of independent variable).
Eg. In a comparison of individuals, those who
are women are more likely to have higher
annual income than those who are men.
Hypothesis formulation exercise
•  Gun leads to increased rates of homicide.
•  Some states have stricter gun laws than
other states.
•  We are curious whether or not we are able
to observe lower homicide rates in states
with strict gun laws.
How would you hypothesize this
relationship using our template?
Null and Alternative Hypothesis
•  Alternative Hypothesis (H1): what
researchers interested in showing to be
true (aka ‘research hypothesis’). There is
a relationship between the two variables of
interest.
•  Null Hypothesis (H0): some form of
‘nothing happening. There is no
relationship between the two variables in
the population
H1, H0 Examples
Example: Aspirin and Heart Attacks
•  Null Hypothesis: There is no
relationship between taking aspirin and
risk of heart attack in the population.
•  Alternative Hypothesis: There is a
relationship between taking aspirin and
risk of heart attack in the population.
Philosophy of Science
Don’t assume it’s unimportant
•  Popper’s scientific revolution: deductive
method? inductive method? What are they
and which one did Popper support and why?
1.  All men are mortal. Harold is a man.
Therefore, Harold is mortal
2.  Harold is a grandfather. Harold is
bald. Therefore, all grandfathers are
bald
•  Compare and contrast quantitative and
qualitative methods (Mahoney & Goertz)
Levels of Measurement
•  Nominal variable: gender, party id
•  Ordinal variable: income range, ranks,
agreement
•  Interval variable: income by dollar
•  Ration Variable (absolute zero): height,
weight
DV and IV
•  DV (outcome variable):The presumed effect
in an experimental study. The values of the
dependent variable depend upon another
variable, the independent variable.
•  IV(explanatory variable): The presumed
cause in an experimental study. All other
variables that may impact the dependent
variable are controlled. The values of the
independent variable are under experimenter
control.
Measures of central tendency
Range= Upper bound-lower bound
Frequency
Normal distribution: mean=median=mode
(bell curve)
•  Central limit theorem: with some
conditions, if you draw a large enough
random independent variables, the mean
will look like a normal distribution.
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Skewness and Kurtosis
Sample vs Population
Capitalization
In general, capital letters refer to population attributes (i.e., parameters); and lower-case letters
refer to sample attributes (i.e., statistics). For example,
P refers to a population proportion; and p, to a sample proportion.
X refers to a set of population elements; and x, to a set of sample elements.
N refers to population size; and n, to sample size.
Population Parameters
By convention, specific symbols represent certain population parameters. For example,
μ refers to a population mean.
σ refers to the standard deviation of a population.
σ2 refers to the variance of a population.
ρ is the population correlation coefficient, based on all of the elements from a population.
N is the number of elements in a population.
Sample Statistics
By convention, specific symbols represent certain sample statistics. For example,
x^bar refers to a sample mean.
s refers to the standard deviation of a sample.
s2 refers to the variance of a sample.
r is the sample correlation coefficient, based on all of the elements from a sample.
n is the number of elements in a sample.
SD and Z-score
SD
•  a “standard” way of knowing what’s normal
amongst a bunch of data.
•  SD=sqrt(variance)
•  From our dog example, we can statistically define
what type of dog is particularly “Large” or “Small”
based on SD.
Z-score
•  Essentially, we quantify how many SDs from the
mean of particular value is.
•  The number of SDs a data value is above/below
the mean is called the Z score.
•  Z score=(data value-mean)/SD
Problem Set 2: quick review
•  Please submit your work in a SINGLE
document!
•  No percentages in crosstab: Fine for now,
but not in the future, as your
interpretation may likely to be wrong!
•  Interpretation is more difficult than
running SPSS!
Q1
•  NES2012=> libcon3+gender (descriptive
statistics)=>form a hypothesis based on
the template=>cross tab (gender=IV;
libcom3=DV)
Q1 Solution
•  Open NES2012
•  Analyze=>Descriptive
Statistics=>Frequencies=>gender, libcon3
into variable list=>Statistics=>skewness,
kurtosis, mean, median, mode, min, max..=>
Continue=>OK
•  Interpretation?
•  Hypothesis?
•  Analyze=>Descriptive Statistics=>Cross
tabs=>gender in column, libcon3 in
row=>cell=>Column
percentages=>continue=>OK
•  Interpretation?
Q2
•  World=> polity+gdp_cap3 (descriptive
statistics)=>form a hypothesis based on
the template=>mean
comparison(gdp_cap3=IV; polity=DV)
Q2 Solution
•  Open World.sav
•  Analyze=>Descriptive
Statistics=>Frequencies=>polity, gdp_cap3
into variable list=>Statistics=>skewness,
kurtosis, mean, median, mode, min, max..=>
Continue=>OK
•  Interpretation?
•  Hypothesis?
•  Analyze=>Compare
Means=>Means=>gdp_cap3 as IV polity as
DV=>OK
•  Interpretation: The mean polity score of countries
that fall under low GDP is 2.90.