the use of statistics in outcomes assessment
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Definition of Statistics Statistics is a science of discipline. It is
a branch of mathematics that deals with
the collection, organization, presentation,
computation and interpretation of data
which are the, outcomes of learning”
(Santos et al, 2000)
uses methods to summarize a collection
of data by describing what was observed using
numbers or graphs.
also called predictive statistics, uses
methods to draw patterns in the collected data,
and then makes conclusions, predictions or
forecasts about a group or about a process being
studied.
Statistics is useful in the teaching-learning process,
along several research-based inquiries:
These inquiries investigates causes, in
addition to drawing conclusions on the effect of
changes in elements (called variables) being
studied.
Data are gathered and the correlations
between intervention (predictors) and the result
derived from a single group is investigated.
A) Planning the research-based inquiry
around size, hypothesis, variability,
subjects, etc;
B) designing the experiment by blocking to
reduce error; random assignment for
unbiased estimates, and mapping the
procedures;
C) implementation and analyzing data;
D) documentation and presentation of
results of the study.
It is a process where instructor communicate with concepts about external realities.
There are three classes of phenomena which can be statistically measured:
(a)Direct observables
(b) Indirect observables
(c) Constructs or creations we form in the mind resulting from observations.
• Indicator is a sign of the presence of
a concept (variable) under study.
• Dimension is a specific aspect of a
concept combined into groups or sub-
groups, such as compassion toward
neighbors/fellow
nationals/foreigners/animals/plants.
Concepts can become more
clarified by a process of clarification
called specification. This can also be
done by categorization or the ordering
or ranking of data.
Conceptualization (process to specify what we mean)
Nominal definition (assigned to term, not the real entity)
Operational definition (specifies how a concept is measured)
Real definition (better clarified status of a real thing)
• Variables are a logical set of attributes,
e.g. gender. On the other hand, an
attribute is a quality or characteristics of
something, e.g.. male, female. Attributes
may represent any of the four levels of
measurement:
A level of measurement describing
a variable that has attributes which are
different, e.g. gender, birthplace,
college, major, etc.
A level of measurement describing
a variable with attributes that can be in a
rank-order along some dimension.
A level of measurement describing a
variable whose attributes are rank-ordered
and have equal distances between adjacent
attributes.
A level of measurement describing a
variable with attributes that have all the
qualities of nominal, ordinal and interval and
based on a ”true zero” point, e.g. age, length
of residence in a place, etc.
• The use of an index or scale may help to
explain and elicit understanding of concepts in
a range of conceptual variations.
• In an index, scores for individual attributes are
constructed. On the other hand in a scale,
scores are assigned to patterns of ideas.
• A scale is constructed by assigning scores to
patterns of responses according to higher and
lower degrees of civic participation.
– Is a classification of observations in terms of
attributes on two or more variables.
Use to subject data or concepts for better
understanding, analysis, or statistical
interpretation.
– This is an analysis of a single variable for
purposes of description.
A bivariate relationship refers to two
variables.
Given the results of findings, there are
inferential statistics which can assist in
pursuading the audience/readers as to
the significance, strength, deserved
interest in a completed research. These
are called parametric tests of
significance.
This is useful in social science and
is based o the null hypothesis: the
assumption that there is no
relationship between two variable.
Expected Frequencies Men Women Total
Attended alumni affair 28 42 70Did not attend affair 12 18 30Total 40 60 100
Observed frequenciesAttended 20 50 70Did not attend 20 10 30Total 40 60 100
Observed minus ExpectedDivided by ExpectedAttended 2.29 1.52X = 12.70Did not attend 5.33 3.56 = <.001
It is casual model for understandingrelationship between many variables. It is auseful graphic illustration of relationshipsamong several variables which assumes thatthe values of one variable are used byanother.
Global Free
Trade
Neo-colonialism
3rd World Economies
Developed Economies
National Sectors:
labor/business/industry
This represents changes in one or more
variables over time.
This is used to discover patterns among the
variations in values of several factors.
Cases under study are combined into groups
representing an independent variable, and the
extent to which the group differ from one
another is analyzed in relation to some
dependent variable.
Other multivariate techniques are the
Discriminant Analysis, Log Linear
models and the Geographic Information
systems which require more
sophisticated statistical procedures
which can be learned in Statistics or
formal Research classes.