adek ku tersayang........

rindu mak n bapak ku~~='[

hati siapa untuk apa...apakah persoalan??

family 1st...aku...adlh aku...
suatu ketika aku lah bintang..penceria suasana..
kdg2..aku jdi bunga..elok rupenye...
kdg2 aku jdi lilin..menerangi yg lain...

seronoknya mjadi bintang..
bunga yg cantik..rasa dihargai..
lilin yg diberi kepercayaan mlindungi org lain...
sungguh indah..
teramat bahagia...

namun..bintang ini kdg2 jdi api...
bunga gugur tinggal lah kayu...
& lilin ini mgkin bleh padam ditiup angin...
api yg merosak suasana...kayu yg memukul org lain...
menyakiti org lain...diri yg dibawa angin...
hanya mngikut kehendak hati...

apakah ini... haruskah begini...
itulah asam garam hidup duniawi..

1 family...taman bahagia..
rantaian sibling...
1000 ragam...1000 kegembiraan...1000 dugaan...
1000 kegagalan..1000 kemenangan
ada kekurangan ada kelebihan...
adil nya yg Esa....

Namun hakikat ini...
1 kedukaan yg ku alami dari taman ini..
1000 kelukaan dlm diri ini... benarlah ibarat..
air dicincang takkan putus... walau badai melanda..
laut tetap bersama pantai

haruskah..
kedukaan ini dari taman ini...
kerana aku tiada daya...
kalau taman adalah sgalanya..
dia lah tunjang pedoman hidup
bagaimana kalau ia mendukakan
di mana harus ku mengadu..

perlukah aku jadi api..kayu..atau angin...
atau mungkin...
bintang yg malap...bunga yg layu...
atau lilin yg bertarung dengan angin

egois boleh mjadi punca...
amat menyakitkan...
bila ada ombak memukul pantai...

Ya Allah Ya Tuhan ku..
sesungguhnya Engkau lbh tahu..
apa yg terbuku di hati ini...

ampunilah aku...
maafkan aku....
kalau aku jd api... kayu...
@angin...
maka jadikan lah aku..
bintang yg bersinar..
bunga yg mekar.. lilin yg terang....
dengan izin Mu

hanya ku ingin kn cahaya taman...
bersama memupuk kebahagiaan..
penuh penghayatan..
izinkan lah ya Tuhanku..
agar kebahagiaan ini sentiasa mjadi milik kami diberkati..
n dikasihi olehMu..
perkenankan lah hajat kami..
dan permudahkan kami...
jadikan kami golongan yg sentiasa disisimu...
amin Insya Allah~

(dikesempatan ini...i nk wish utk sume sibling i..
yg telah byk menyokong n membantu
dlm apa jua hal dulu kini n slamanya..
THANKS A LOT)

maaf ku pohon...
Love u all selamanya...
semoga Allah memberkati kita....amin..

HAPPY MOTHER'S DAY~

Selamat Hari Ibu buat ibuku tersayang...Zariah bt.Tahir..
jasamu tak ternilai...kasih sayangmu tiada tandingan..
Semoga Mak stiasa diberi kekuatan & kesihatan dlm menjalani hidup ini..anakmu mendoakan agar mak sentiasa dimurahkn rezeki dan dipnjangkan umur..dirahmati-Nya & diberkati-Nya..yg paling pnting mak dpt apa yg dihajati...kerana hajat mak adlh doa utk ku...

I love U so much...

Multivariate Data Analysis

Multivariate Data Analysis refers to any statistical technique used to analyze data that arises from more than one variable. This essentially models reality where each situation, product, or decision involves more than a single variable. The information age has resulted in masses of data in every field. Despite the quantum of data available, the ability to obtain a clear picture of what is going on and make intelligent decisions is a challenge. When available information is stored in database tables containing rows and columns, Multivariate Analysis can be used to process the information in a meaningful fashion.

Multivariate analysis methods typically used for:

• Consumer and market research
• Quality control and quality assurance across a range of industries such as food and beverage, paint, pharmaceuticals, chemicals, energy, telecommunications, etc
• Process optimization and process control
• Research and development

With Multivariate Analysis you can:

Principal Component Analysis


MVA for Spectral data

• Obtain a summary or an overview of a table. This analysis is often called Principal Components Analysis or Factor Analysis. In the overview, it is possible to identify the dominant patterns in the data, such as groups, outliers, trends, and so on. The patterns are displayed as two plots
• Analyze groups in the table, how these groups differ, and to which group individual table rows belong. This type of analysis is called Classification and Discriminant Analysis
• Find relationships between columns in data tables, for instance relationships between process operation conditions and product quality. The objective is to use one set of variables (columns) to predict another, for the purpose of optimization, and to find out which columns are important in the relationship. The corresponding analysis is called Multiple Regression Analysis or Partial Least Squares (PLS), depending on the size of the data table

Multivariate statistics

Multivariate statistics help the researcher to summarize data and reduce the number of variables necessary to describe it.

How are these techniques used?

Most commonly multivariate statistics are employed:

• for developing taxonomies or systems of classification
• to investigate useful ways to conceptualize or group items
• to generate hypotheses
• to test hypotheses

One researcher has this to say about factor analysis, a comment that could apply to all three techniques:

When I think of factor analysis, two words come to mind: "curiosity" and "parsimony." This seems a rather strange pair -- but not in relation to factor analysis. Curiosity means wanting to know what is there, how it works, and why it is there and why it works ... Scientists are curious. They want to know what's there and why. They want to know what is behind things. And they want to do this in as parsimonious a fashion as possible. They do not want an elaborate explanation when it is not needed ... This ideal we can call the principle of parsimony (Kerlinger, 1979).

How do these techniques differ from regression?

In multiple regression and analysis of variance, several variables are used, however one -- a dependent variable -- is generally predicted or explained by means of the other(s) -- independent variables and covariates. These are called dependence methods.

Factor analysis, multidimensional scaling (MDS) and cluster analysis look at interrelationships among variables. They are not generally used in prediction, there is no p-value, and the researcher interprets the output of the analysis and determines the best model. This can be frustrating! (See cautions for novice researchers.)

What are the assumptions of multivariate analyses?

All of the models require that input data be in the form of interrelationships -- this means correlations for factor analysis. MDS and cluster analysis can use a variety of different input data -- distances, or measures of similarity or proximity. This means that MDS and cluster analysis can be somewhat more flexible than factor analysis.

A big assumption of these methods is that the data itself is valid . (See Trochim's Knowledge Base for a discussion of validity, especially construct validity.) Because these methods do not use the same logic of statistical inference that dependence methods do, there are no robust measures that can overcome problems in the data. So, these methods are only as good as the input you have. The "garbage in-garbage out" rule definately applies.

What does the output look like?

In each case, the output will look somewhat different, but in all of the techniques, the researcher is required to look at the results and make some determination of how many factors, dimensions or clusters to use in further analysis in order to represent the data. What the researcher should not forget is that each case or variable used in the analysis is simultaneously classified on all the dimensions. While this is most apparent in multidimensional scaling, it applies equally well to the other techniques.

RESEARCH METHOD

Scales of Measurement

Statistical information, including numbers and sets of numbers, has specific qualities that are of interest to researchers. These qualities, including magnitude, equal intervals, and absolute zero, determine what scale of measurement is being used and therefore what statistical procedures are best. Magnitude refers to the ability to know if one score is greater than, equal to, or less than another score. Equal intervals means that the possible scores are each an equal distance from each other. And finally, absolute zero refers to a point where none of the scale exists or where a score of zero can be assigned.

When we combine these three scale qualities, we can determine that there are four scales of measurement. The lowest level is the nominal scale, which represents only names and therefore has none of the three qualities. A list of students in alphabetical order, a list of favorite cartoon characters, or the names on an organizational chart would all be classified as nominal data. The second level, called ordinal data, has magnitude only, and can be looked at as any set of data that can be placed in order from greatest to lowest but where there is no absolute zero and no equal intervals. Examples of this type of scale would include Likert Scales and the Thurstone Technique.

The third type of scale is called an interval scale, and possesses both magnitude and equal intervals, but no absolute zero. Temperature is a classic example of an interval scale because we know that each degree is the same distance apart and we can easily tell if one temperature is greater than, equal to, or less than another. Temperature, however, has no absolute zero because there is (theoretically) no point where temperature does not exist.

Finally, the fourth and highest scale of measurement is called a ratio scale. A ratio scale contains all three qualities and is often the scale that statisticians prefer because the data can be more easily analyzed. Age, height, weight, and scores on a 100-point test would all be examples of ratio scales. If you are 20 years old, you not only know that you are older than someone who is 15 years old (magnitude) but you also know that you are five years older (equal intervals). With a ratio scale, we also have a point where none of the scale exists; when a person is born his or her age is zero.

Table 8.1: Scales of Measurement

Scale Level	Scale of Measurement	Scale Qualities	Example(s)
4	Ratio	Magnitude Equal Intervals Absolute Zero	Age, Height, Weight, Percentage
3	Interval	Magnitude Equal Intervals	Temperature
2	Ordinal	Magnitude	Likert Scale, Anything rank ordered
1	Nominal	None	Names, Lists of words

Critical Analysis on Research Method

Introduction

While many professionals in education, psychology, management, and other social science fields perform research and use statistics to analyze results, many more read the results of research and apply it to the real world. Therefore it is vitally important to be able to critically analyze a research report to determine if the methods and results are valid and if they apply to you as a professional. This chapter will look at each of the major sections of the research report and will provide ideas for what to look for, how to apply the information, and how to determine if a specific study is worth incorporating into your work.

Abstract

The abstract is almost always the first section we read in a research report. As such, it should provide valuable information about what is included in the remainder of the report. According to the APA manual, the abstract should be no more than 150 words in order to provide enough information about the paper without replicating large sections. In other words, a good abstract will include, in general, one or two sentences for each of the following:

(1) Statement of the problem;
(2) Brief summary of the literature;
(3) Brief Summary of the methods used in the present research;
(4) Brief summary of the results found in the present research; and
(5) The significance of the present study and/or need for further research

Introduction (Literature Review)

The purpose of the introduction is to give the reader a solid background of the phenomena being studied. It should provide the reader with information that will lead to the statement of the problem. This information can include a need to replicate previous studies due to shortcomings or to apply previous research to a different population. It can also discuss a new theory and the qualitative and quantitative data that has lead to this new idea about a relationship. In general, by the time the introduction section is read, the reader should be able to understand the need for the present study and have a solid understanding of the researcher’s theory.

Methods

The methods section is often the most precisely written part of a research report. Since replication and analyzing methods is so important, a good deal of time should be spent analyzing this section. As a consumer of research, it is imperative for you to understand the foundation of each study and be able to critically analyze how the data that will lead to the results section was derived.

When reading the methods section you should look for information regarding the subjects and the manner in which the subjects were selected. You should be able to discuss the pitfalls of not using randomization, or of various types of randomization. You should be able to understand the strengths and weaknesses of the type of design used and how the researchers used control groups or groups that were not equivalent. The use of standardized procedures is also important, as we ideally want every group to experience the same environment except for the variable(s) being measured. If confounding variables are not controlled for, you should be able to discuss how this lack of control might impact the results of the study.

Issues related to internal and external validity should be carefully addressed as well. Look for how the researchers addressed subject maturation in longitudinal or longer term studies, how they handled extreme scores and the tendency for scores to regress toward the mean, and how they dealt with differences in mortality or drop out rates between groups. And finally, you should be aware of what assessment procedures were used, whether these instruments are valid and reliable, and whether or not they were used correctly

Results

The results section will likely require at least some basic understanding of statistics as this section is often the most technical. The main ideas that should be analyzed in a results section include the statistical procedures used, the reporting of the numerical findings, and the determination of significance. The procedures used should correspond with the data they are working with. For instance, if the data is nominal or ordinal and the procedure used was parametric (See Summary of Statistics), then the results will be skewed at best and completely invalid at worst.

The numerical results of the statistics should also be reported so if the study shows a significant t test, the actual t-test score, the degrees of freedom, and the probability of error (p) as compared with the acceptable error (a) should all be reported. Finally, the decision for how much error was acceptable for the researchers should be addressed. Typically .05 or .01 are used, representing an acceptable error level of 5% or 1%, respectively. If the level is set different from these two, the rationale should be addressed.

Discussion

The discussion section allows the researchers to qualify their results and to discuss areas of concern regarding their research and areas of further study that may be needed. A good researcher will want to critique his or her own study before others do. This provides the reader with a more qualitative understanding of the findings. Shortcomings such as a lack of randomization, failure to use a control group, as well as many other issues, should be addressed and explained. Not only does this show that the researcher didn’t just arbitrarily omit steps, but it may help future researchers improve upon the present study.

The results of the research and the relevance to the topic at large should also be addressed. This is mainly to provide insight into what research might be completed in the future to either fix problems in the current study or advance the original theory. The researcher is typically well versed in the topic of study, or at least should be, and can help lead the profession to further advancements and knowledge through his discussion of the current study. The discussion section is the most qualitative part of a research report and therefore may be the most important section of the entire paper.

References

References allow the reader to look up information and read more about particular phenomena or research that was discussed in the current study. While reading the study, the quality of the references should be considered. If, for instance, a study uses research from unprofessional or questionable sources, the validity of his arguments will also be questionable. Look for journals that are known and respected in the profession as this will help to determine the strength of the supporting material that led to the development of the original theory.

Appendices

A research study can have many appendices or it can have none. When addressing this section, look at the need for the data in the first place. Did the author place information here just to take up space? Is the information confusing and difficult to understand? Was it addressed in he paper or just included arbitrarily? Does it provide information that helps the reader understand the methods or results of the study? And finally, does it provide assistance in analyzing the study as a whole and the need for replication or further research?

Chapter Conclusion

While many professionals in education and the social sciences perform research, the majority of us use this information in real life application. Teachers use research regarding teaching methods and learning style to help improve the education of their students. Therapists use research to provide better treatment for specific mental illnesses. Managers use research to help them improve retention rates, worker satisfaction, or communication. The purpose of research, then, is not merely to gather information, but to communicate this information to the research consumers.

Through this text, you should have a solid understanding of the importance of research, the methods of developing a hypothesis, and the specific designs used for particular types of research. You should understand the importance of standardization, randomization, controlling for confounds, and assuring internal and external validity. You should have a basic understanding of descriptive and inferential statistics, and be aware that the foundation for your study and the discussion of results are often more important than the results themselves.

Perhaps most importantly though, you should understand how to critically analyze a research report from start to finish. You should be able to address the strengths and weaknesses of particular designs and the specific methods of a study. You should be able to discuss results and combine studies in order to develop your own theories and to apply this information to your professional life. Research is not an end in itself, nor is it a means to an end. Research is merely a continuing and vital part of our need to understand and grow.