Statistics Handout

© 2014 Laureate Education, Inc. Page 1 of 5
Week 4: A Short Course in Statistics Handout
This information was prepared to call your attention to some basic concepts underlying
statistical procedures and to illustrate what types of research questions can be
addressed by different statistical tests. You may not fully understand these tests without
further study. However, you are strongly encouraged to note distinctions related to type
of measurement used in gathering data and the choice of statistical tests. Feel free to
post questions in the “Contact the Instructor” section of the course.
Statistical symbols:
µ mu (population mean)
α alpha (degree of error acceptable for incorrectly rejecting the null hypothesis,
probability that results are unlikely to occur by chance)
≠ (not equal)
≥ (greater than or equal to)
≤ less than or equal to)
ᴦ (sample correlation)
ρ rho (population correlation)
t r (t score)
z (standard score based on standard deviation)
Chi square (statistical test for variables that are not interval or ratio scale, (i.e.
nominal or ordinal))
p (probability that results are due to chance)
Descriptives are statistical tests that summarize a data set.
They include calculations of measures of central tendency (mean, median, and mode),
and dispersion (e.g., standard deviation and range).
Note: The measures of central tendency depend on the measurement level of the
variable (nominal, ordinal, interval, or ratio). If you do not recall the definitions for these
levels of measurement, see
You can only calculate a mean and standard deviation for interval or ratio scale
For nominal or ordinal variables, you can examine the frequency of responses. For
example, you can calculate the percentage of participants who are male and female; or
the percentage of survey respondents who are in favor, against, or undecided.
Often nominal data is recorded with numbers, e.g. male=1, female=2. Sometimes
people are tempted to calculate a mean using these coding numbers. But that would be
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meaningless. Many questionnaires (even course evaluations) use a likert scale to
represent attitudes along a continuum (e.g. Strongly like … Strongly dislike). These too
are often assigned a number for data entry, e.g. 1–5. Suppose that most of the
responses were in the middle of a scale (3 on a scale of 1–5). A researcher could
observe that the mode is 3, but it would not be reasonable to say that the average
(mean) is 3 unless there were exact differences between 1 and 2, 2 and 3 etc. The
numbers on a scale such as this are ordered from low to high or high to low, but there is
no way to say that there is a quantifiably equal difference between each of the choices.
In other words, the responses are ordered, but not necessarily equal. Strongly agree is
not five times as large as strongly disagree. (See the textbook for differences between
ordinal and interval scale measures.)
Inferential Statistics:
Statistical tests for analysis of differences or relationships are Inferential,
allowing a researcher to infer relationships between variables.
All statistical tests have what are called assumptions. These are essentially rules that
indicate that the analysis is appropriate for the type of data. Two key types of
assumptions relate to whether the samples are random and the measurement levels.
Other assumptions have to do with whether the variables are normally distributed. The
determination of statistical significance is based on the assumption of the normal
distribution. A full course in statistics would be needed to explain this fully. The key point
for our purposes is that some statistical procedures require a normal distribution and
others do not.
Understanding Statistical Significance
Regardless of what statistical test you use to test hypotheses, you will be looking to see
whether the results are statistically significant. The statistic p is the probability that the
results of a study would occur simply by chance. Essentially, a p that is less than or
equal to a predetermined (α) alpha level (commonly .05) means that we can reject a null
hypothesis. A null hypothesis always states that there is no difference or no relationship
between the groups or variables. When we reject the null hypothesis, we conclude (but
don’t prove) that there is a difference or a relationship. This is what we generally want to
Parametric Tests:
Parametric tests are tests that require variables to be measured at interval or ratio
scale and for the variables to be normally distributed.
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These tests compare the means between groups. That is why they require the data to
be at an interval or ratio scale. They make use of the standard deviation to determine
whether the results are likely to occur or very unlikely in a normal distribution. If they are
very unlikely to occur, then they are considered statistically significant. This means that
the results are unlikely to occur simply by chance.
The T test
Common uses:
 To compare mean from a sample group to a known mean from a population
 To compare the mean between two samples
o The research question for a t test comparing the mean scores between
two samples is: Is there a difference in scores between group 1 and group
2? The hypotheses tested would be:
H0: µgroup1 = µgroup2
H1: µgroup1 ≠ µgroup2
 To compare pre- and post-test scores for one sample
o The research question for a t test comparing the mean scores for a
sample with pre and posttests is: Is there a difference in scores between
time 1 and time 2? The hypotheses tested would be :
H0: µpre = µpost
H1: µpre ≠ µpost
Example of the form for reporting results: The results of the test were not statistically
significant, t (57) = .282, p = .779, thus the null hypothesis is not rejected. There is not a
difference in between pre and post scores for participants in terms of a measure of
knowledge (for example).
An explanation: The t is a value calculated using means and standard deviations and a
relationship to a normal distribution. If you calculated the t using a formula, you would
compare the obtained t to a table of t values that is based on one less than the number
of participants (n-1). n-1 represents the degrees of freedom. The obtained t must be
greater than a critical value of t in order to be significant. For example, if statistical
analysis software calculated that p = .779, this result is much greater than .05, the usual
alpha-level which most researchers use to establish significance. In order for the t test
to be significant, it would need to have a p ≤ .05.
ANOVA (Analysis of variance)
Common uses: Similar to the t test. However, it can be used when there are more than
two groups.
The hypotheses would be
H0: µgroup1 = µgroup2 = µgroup3 = µgroup4
H1: The means are not all equal (some may be equal)
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Common use: to examine whether two variables are related, that is, they vary together.
The calculation of a correlation coefficient (r or rho) is based on means and standard
deviations. This requires that both (or all) variables are measured at an interval or ratio
The coefficient can range from -1 to +1. An r of 1 is a perfect correlation. A + means that
as one variable increases, so does the other. A – means that as one variable increases,
the other decreases.
The research question for correlation is: “Is there a relationship between variable 1 and
one or more other variables?”
The hypotheses for a Pearson correlation:
H0: ρ = 0 (there is no correlation)
H1: ρ ≠ 0 (there is a real correlation)
Non-parametric Tests
Nonparametric tests are tests that do not require variable to be measured at
interval or ratio scale and do not require the variables to be normally distributed.
Chi Square
Common uses: Chi square tests of independence and measures of association and
agreement for nominal and ordinal data.
The research question for a chi square test for independence is: Is there a relationship
between the independent variable and a dependent variable?
The hypotheses are:
H0 (The null hypothesis) There is no difference in the proportions in each category of
one variable between the groups (defined as categories of another variable).
The frequency distribution for variable 2 has the same proportions for both categories of
variable 1.
H1 (The alternative hypothesis) There is a difference in the proportions in each category
of one variable between the groups (defined as categories of another variable).
The calculations are based on comparing the observed frequency in each category to
what would be expected if the proportions were equal. (If the proportions between
observed and expected frequencies are equal, then there is no difference.)
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See the SOCW 6311: Week 4 Working With Data Assignment Handout to explore the
Crosstabs procedure for chi square analysis.
Other non-parametric tests:
Spearman rho: A correlation test for rank ordered (ordinal scale) variables.

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