Evaluating data for statistical treatment

ESSENTIALITIES AND COMPLEXITIES IN THESIS WRITING

Sequence of Presentation

1. How important statistics is in research

2. Dangers of (mis)using statistics

3. Why data should be statistically treated

4. Purposes of Statistics (in Research Writing)

5. The Data Analysis Process

6. What to measure and how

7. Levels of Measurement

8. Matrix for Statistical Treatment of Data

9. Common Statistical Operations

10. Statistical Tests

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.

Dangers of (mis)using statistics

1. 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.

2. Statistical information can be easily

manipulated to show very different results.

Why data should be statistically treated

1. Data come in different volume and form.

2. Data are subject to different interpretations.

3. “Words (data) differently arranged have different meanings; meanings differently arranged have different impacts.”1

1 att. to Charles Babbage, Father of Modern Computer

Purposes of Statistics (in Research Writing)

Essentially, statistics 1. helps organize the data. (Tables and

graphs are the essential non-letter cues for interpretation)

2. 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.

3. provides platform for research

What to Measure and How

Identify the observable characteristics of the concepts being investigated record and order observations of those behavioral characteristics. 1.Quantitative measurements employ meaningful numerical indicators to ascertain the relative amount of something. 2.Qualitative measurement employ symbols to indicate the meaning people have of something.

Levels of Measurement 1. (N)ominal variables are differentiated on the basis of

type or category.

2. (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.)

3. (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.

4. (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.

Matrix for

Statistical Treatment of Data

Matrix for Statistical Treatment of Regularly Gathered Data

Variables Treatments

Gender f, %

Age, Height, Weight, Mo. Income

f, %, mean, sd

Educl. Attainment f, %

Perceptions WM, Ave. WM, Grand WM

Choice f, %, rank

Correlations Pearson, Spearman

Test of Significance t-test (z-test) Chi-square

Rank Kendall’s Tau and Coefficient of Concordance

Test standardization Item Analysis

Common Statistical Operations

1. Measures of Central Tendency indicate what is typical of the average subject. E.g. Mean, Median, Mode

2. Measures of Variance indicate the distribution of the data around the center. E.g. standard deviation and variance

3. 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.

4. Test of significant difference/

relationships.

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).

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.

References

Retrieved from 4 Aug to 10 Aug 2012 1.http://www.blurtit.com/q799907.html 2.http://wiki.answers.com/Q/What_is_the_importance_of_statistics_in_research 3.http://www.bcps.org/offices/lis/researchcourse/data_process.html 4.http://ion.chem.usu.edu/~sbialkow/Classes/3600/Overheads/Stat%20Narrative/statistical.html 5. http://www.fao.org/docrep/W7295E/w7295e0a.htm#TopOfPage