SEYS
777
Dr.
Brian Murfin

Discuss
in groups of 3: Warm-up: “thinking about data analysis”

Part 1

a. A
college professor is interested in determining if there is a
relationship
between passing and failing her calculus course and whether or
not the students
took a pre-calc. course.

b.
What is the relationship between writing ability and critical
thinking ability?

c. A
seventh-grade teacher wants to know how the students in his
class stand in
relationship to one another on a standardized math test.

d.
All eighth grade students are administered a science
achievement test. The
teachers want some information about the spread of the scores.

e.
The study skills tests is administered to a group of students
at the beginning
of the school year. They participate in a special
instructional program and are
re-administered the test at the end of the school year. The
teachers want to
know if their gain in achievement is significant.

f.
The high school supervisor wants to determine the effect of
cooperative
learning, peer tutoring, and individualized instruction on the
reading
achievement of the students in the remedial program.

g.
Ms. Rodriguez is interested in determining the average
achievement of the
students in her class on a teacher-made biology test.

**Part 2**

h.
Locate a research article in a research journal.
What was the question or problem being
investigated? What data was used? How was the data analyzed? Reported?
What conclusions were reached?
Did you feel the conclusions were warranted?

i.
Create a question based on a research interest you have. What data can be
collected to answer the
question? What would you do
with the
data to find the answer(s) to your question?

**Common
Statistical
Terms**

**ALPHA LEVEL** - A number set in advance of an
experimental or
correlational study to indicate the level of probability the
researcher is
willing to accept of mistakenly rejecting the null hypothesis. For example, an alpha level of .05,
written p
< .05 indicates that the researcher is willing to accept a
5% chance that a
statistically significant finding will be in error.

**ANALYSIS OF
VARIANCE (ANOVA**) - A
statistical
method that compares two or more group means to determine if
differences
between the adjusted means are statistically significant.

**CHI-SQUARE
(X²) **–
Parametric and non-parametric
statistical method that compares number of responses
(frequencies) of different
groups or cases in different categories.

**CORRELATION
COEFFICIENT** - A
statistic,
usually designated r, indicating the degree to which two
variables are
correlated. May take on values
from -1.0
(perfect negative correlation, when variable A is high,
variable B is low, and
vice versa) to +1.0

(perfect
positive correlation, when variable A is high, variable B is
high and vice
versa). A correlation coefficient of 0 indicates that variable
A and B are
unrelated.

**CORRELATIONAL
DESIGN** - A
non-experimental research
design in which the researcher collects data on two or more
variables to
determine if they are related (if they consistently vary in
the same or
opposite directions).

**DESCRIPTIVE
RESEARCH** - Research
conducted to describe
some phenomenon as it exists, rather than finding
relationships between
variables (correlational research) or varying treatments to
observe the
outcomes (experimental research). Examples are surveys,
assessment and
evaluation research, ethnography, and historical research.

**DISTRIBUTION** - A pattern of scores on some
variable. For example, a normal
distribution of scores
is one in which most scores are near the mean of the set of
scores and other
scores cluster around the mean in a bell-shaped pattern.

**EXPERIMENTAL
RESEARCH** - Research designs
in which the
experimenter decides how and when subjects will receive
certain treatments and
observes the effect of the treatments on one or more dependent
variables.

**INDEPENDENT
VARIABLE** - A variable (such
as treatment)
hypothesized to cause one or more outcomes (dependent
variables).

**MULTIPLE
REGRESSION** - A
statistical method that
evaluates the effects of one or more

independent variable(s) on a
dependent (outcome) variable.

**One-Way ANOVA** - Analysis of variance with a
single factor.

**RELIABILITY** - Consistency of a measure over
time, across subjects,
tests, observers, or within a test or scale.

**t-TEST** - A statistic used to test the
difference between two
means for statistical significance. Also
used
to test correlation coefficients and regression coefficients
for
statistical significance (for groups < 30).

**VALIDITY** - The degree to which an instrument
(eg. test or
questionnaire) actually measures the concept or construct it
is supposed to measure.
Also, the degree to which the results of a study can be
attributed to
treatments, rather than to flaws in the research design
(internal validity), or
the degree to which the results of a study have relevance to
subjects and
settings other than the ones involved in the research
(external validity).

**VARIABLE** - Anything that can take on more
than one value (eg.
age, sex, science achievement score).

**VARIANCE** – A measure of the
variation/dispersion of scores in a
distribution.

Other:

**SCALES OF
MEASUREMENT** –
Scales used to measure variables,
based on their degree

of precision.

** Nominal**: Classification
of objects into categories
based on some characteristic e.g. gender

No order implied between or
among categories.

** Ordinal**: Classification
based on a logical order
between categories, a ranking.

** Interval**: Classification
has rank, equal units,
arbitrary zero point e.g. temperature scales

** Ratio**: Most
precise scale. Interval scale
with a known zero point that
reflects the absence of the characteristic being
measured e.g.
weight and height measures.

**CASE STUDY** – An in-depth analysis of one or
more events, settings,
programs, social groups, individuals or other “bounded
systems.” Usually an
investigation of one entity, carefully defined and
characterized by time and
place. Could be a single study or multiple cases.

**NORMAL CURVE** – A bell-shaped curve based on laws
of probability
concerning random deviations from a population mean. From it,
predications can
be made about the distributions of naturally occurring
phenomena in sample
populations. Parametric
statistics based
on normal curve distributions.