MELJUN CORTES research lectures_evaluating_data_statistical_treatment

1. Evaluating data for statistical
treatment
ESSENTIALITIES
AND
COMPLEXITIES IN
THESIS WRITING
MELJUN CORTES

2. Sequence of
Presentation
How important statistics is in research
Dangers of (mis)using statistics
Why data should be statistically treated
Purposes of Statistics (in Research Writing)
The Data Analysis Process
What to measure and how
Levels of Measurement
Matrix for Statistical Treatment of Data
Common Statistical Operations
Statistical Tests

3. How important statistics
is in research
In theory they are very important. Without
statistics it is almost impossible to come to an
informed conclusion in any piece of research.
The use of statistics is wide ranging in the field
of research and without the use of statistics it is
virtually impossible to interpret a true meaning
of what the research shows. Not to exaggerate,
statistics is the BACKBONE OF A RESEARCH.

4. Dangers of (mis)using
statistics
Statistics, no matter how carefully collected,
can always be flawed e.g. without a sample
of thousands of people (ensuring they are
representative of the whole population), you
cannot be certain that the results can be
wholly generalized.
Statistical information can be easily
manipulated to show very different results.

5. Why data should be
statistically treated
Data come in different volume
and form.
Data are subject to different
interpretations.
“Words (data) differently
arranged have different
meanings; meanings differently
arranged have different
impacts.”11 att. to Charles Babbage, Father of Modern
Computer

6. Purposes of Statistics
(in Research Writing)
Essentially, statistics
helps organize the data. (Tables and
graphs are the essential non-letter cues for
interpretation)
makes inferring guided, which yields to
more meaningful interpretations. It makes
use of descriptive statistics for collection of
data and inferential statistics for drawing
inferences from this set of data.
provides platform for research

7. The Data Analysis Proces

8. What to Measure and How
Identify the observable characteristics of the
concepts being investigated → record and order
observations of those behavioral characteristics.
Quantitative measurements employ
meaningful numerical indicators to ascertain the
relative amount of something.
Qualitative measurement employ
symbols to indicate the meaning people have of
something.

9. Levels of Measurement
(N)ominal variables are differentiated on the basis of
type or category.
(O)rdinal measurement scales not only classify a
variable into nominal categories but also rank order
those categories along some dimension. (The number
does not express the size of the difference.)
(I)nterval measurement scales not only categorize a
variable and rank order it along some dimension but also
establish equal distances between each of the adjacent
points along the measurement scale.
(R)atio measurement scales not only categorize and
rank order a variable along a scale with equal intervals
between adjacent points but also establish an absolute,
or true, zero point where the variable being measured
ceases to exist.

10. Matrix for
Statistical Treatment of Data

11. Matrix for Statistical Treatment of
Regularly Gathered Data
Variab
les
Treatments
Gender f, %
Age,
Height,
Weight,
Mo.
Income
f, %, mean,
sd
Educl.
Attainm
ent
f, %
Percept
ions
WM, Ave.
WM, Grand

12. Common Statistical
Operations
Measures of Central Tendency indicate what is
typical of the average subject. E.g. Mean,
Median, Mode
Measures of Variance indicate the distribution of
the data around the center. E.g. standard
deviation and variance
Correlation and regression analysis deals with
the degree (extent) to which two variables move
in sync with one another. E.g. pearson product-
moment of correlation and spearman rank.
Test of significant difference/
relationships.

13. Statistical Tests –
Two-sided vs. one-sided test
These tests for comparison, for instance between methods A
and B, are based on the assumption that there is no significant
difference (the "null hypothesis").
In other words, when the difference is so small that a tabulated
critical value of F or t is not exceeded, we can be confident
(usually at 95% level) that A and B are not different.
Two fundamentally different questions can be asked
concerning both the comparison of the standard deviations s1
and s2 with the F-test, and of the means¯x1, and ¯x2, with the
t-test:
1. are A and B different? (two-sided test)
2. is A higher (or lower) than B? (one-sided test).

14. Statistical Tests –
F-test (Fisher’s Test)
The F-test (or Fisher's test) is a comparison of the
spread of two sets of data to test if the sets belong to
the same population, in other words if the precisions
are similar or dissimilar.
The test makes use of the ratio of the two variances:
If Fcal ≤ Ftab one can conclude with 95% confidence
that there is no significant difference in precision (the
"null hypothesis" that s1, = s, is accepted). Thus, there
is still a 5% chance that we draw the wrong conclusion.
In certain cases more confidence may be needed, then a
99% confidence table can be used.

15. References
Retrieved from 4 Aug to 10 Aug 2012
http://www.blurtit.com/q799907.html
http://wiki.answers.com/Q/What_is_the_importance_of_statistics_in
http://www.bcps.org/offices/lis/researchcourse/data_process.html
http://ion.chem.usu.edu/~sbialkow/Classes/3600/Overheads/Stat%
http://www.fao.org/docrep/W7295E/w7295e0a.htm#TopOfPage