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.