Selasa, 10 Mei 2011

I HATE STATISTICS

Just try to google "I hate Statistics", surprisingly there's a huge of it. Apparently, statistics  is so popular (lol). There's even a group on facebook named I HATE STATISTICS. Wow!
Let's take a look why those people do so.

 Jovan
I, for one, hate statistics for the following reasons:
- It’s pseudomathematics. It dresses up as a concise set of theories and methods, when these would more properly be referred to as cookbooks.
- It’s simplistic. It gives a false sense of understanding about complex systems where no understanding exists. It prevents people from searching for mechanistic explanations that could indeed provide valuable insights.
- It’s self-adulatory. Its practitioners have the courage to call every little possible way to plot data a “tool” or a “method”.
- It’s too widespread. Most college programs that lack the most basic mathematics have their statistics courses (humanities and sciences), which helps spread misconceptions and misuse.
 (http://flowingdata.com)

Lil_Fig_Newton
So sleepy... three more chapters to read... I HATE stats. WORST SUBJECT EVER! Why oh why did I choose to take it the last semester of my senior year??? Who gives a shit about the null hypothesis???

I'm cycling between extreme bouts of sleepiness and horrible anxiety about my exam, which is in 4 hours (holy fuck, how did time go by so fast?!?!) I need to make a 66 on the final to pass. Please cross your fingers, say prayers, or what ever thing you do for good luck. I have to pass this class to graduate and it is my next to last final. OK, must study more now. More info to CRAM into my exhausted brain.
(http://www.atforumz.com)

Paul Dalton
 There- do you understand? I don’t for what it is worth, but I accept that it is true. I have to do that a lot in my work. I have never been really good with math, yet my work requires looking at statistics. Concepts like confidence intervals and power equations are beyond my ability to truly understand- but I look at them and use them in my work all the time. Is that a weakness for me as an treatment activist- probably.
And that is why I hate statistics.
(http://blogs.poz.com)

Deb
I agree that most people think the field should be renamed "sadistics" but I am not 100% sure why it's so despised.
(http://www.stat.columbia.edu)


Cheryl 
Statistics does suck. It is useless garbage that I will NEVER use. I am 54 years old and I have NEVER used it at work or even running my own business for 18+ years, so what the hell do I need if for now? I have to take it to graduate with my degree.
I bore two boys, raised them, I have undergone open heart surgery and I have NEVER experienced the level of frustration and pain as I have had in this statistics class.
The textbook is POORLY written and the online venue? DON’T have anything to do with Pearson!
I would rather eat glass, drive a pencil through my eye AND walk on coals then to put up with this crap.
There has been nothing my whole life, that could not be figured out by using just addtion, subtraction, multiplying and dividing. The plus? No STUPID rules, that if this happens, then use this or if there is this do this. PLAAEEEEZE! Who thought this junk up????
(http://flowingdata.com)


Patrick 
I hate statistics for a number of reasons:
- My intro professor was without a doubt the worst professor I have ever had. This was essentially intro to statistics for non-statisticians and she took powerpoint slides right from the textbook and threw them up on a screen. Needless to say, it was absolutely useless. Then, during the lab session, she was trying to teach us R without giving us a good background on the concepts. Thankfully, I found a book that barely got me through the class and gave me a great appreciation for some of the concepts. The worst professors are those who lecture for 90 minutes, then say “Any questions.” At which point you don’t even know where to start because s/he lost you in minute two and didn’t care. This was stats for me.
(http://flowingdata.com)


actually all of my CS professors were pretty dynamic. It’s the projects I didn’t like :)
(http://flowingdata.com)


Steve
I may have mentioned it before, but statistics is the worst subject ever to be inflicted on a student. It's even worse than maths.
(http://thedeskinthecorner.blogspot.com)

~Alison
I'm half way thru my online statistics course & I too hate it. It makes no sense to me. i can do the work & give them an answer, but I'm not really learning it. Thankfully only 2 tests left.
It does really stink though. It's confusing to me. Maybe taking it online was not a good idea. it might hve made more sense if I had a teacher lecturing on the material.
(http://allnurses.com)

Jon Peltier
I don’t think people dislike statistics because they are bad at math (though they may be bad at math).
I don’t think the uncertainty is the reason, or the order it imposes.
I think the major reason people dislike statistics is that it was poorly taught in whatever classes they took. Perhaps the instructor didn’t get it, or didn’t do the examples well.
A related reason that people don’t like statistics is that any examples they ever saw were not relevant to something they understood or cared about.
I wasn’t wild about the classroom statistics I had, but what I’ve learned since then has been interesting.
(http://flowingdata.com)

Tony
It sucks soo bad. I get headaches doing this crap (http://amplicate.com/hate/statistics)

So, why do you think you should love statistics? :D 



 






Minggu, 08 Mei 2011

How to Use the Likert Scale in Statistical Analysis

A Likert scale (pronounced /ˈlɪkərt/,[1] also /ˈlaɪkərt/) is a psychometric scale commonly used in questionnaires, and is the most widely used scale in survey research, such that the term is often used interchangeably with rating scale even though the two are not synonymous. When responding to a Likert questionnaire item, respondents specify their level of agreement to a statement. The scale is named after its inventor, psychologist Rensis Likert.[2]
Sample question presented using a five-point Likert item

An important distinction must be made between a Likert scale and a Likert item. The Likert scale is the sum of responses on several Likert items. Because Likert items are often accompanied by a visual analog scale (e.g., a horizontal line, on which a subject indicates his or her response by circling or checking tick-marks), the items are sometimes called scales themselves. This is the source of much confusion; it is better, therefore, to reserve the term Likert scale to apply to the summated scale, and Likert item to refer to an individual item.

A Likert item is simply a statement which the respondent is asked to evaluate according to any kind of subjective or objective criteria; generally the level of agreement or disagreement is measured. Often five ordered response levels are used, although many psychometricians advocate using seven or nine levels; a recent empirical study[3] found that a 5- or 7- point scale may produce slightly higher mean scores relative to the highest possible attainable score, compared to those produced from a 10-point scale, and this difference was statistically significant. In terms of the other data characteristics, there was very little difference among the scale formats in terms of variation about the mean, skewness or kurtosis.

The format of a typical five-level Likert item is:

   1. Strongly disagree
   2. Disagree
   3. Neither agree nor disagree
   4. Agree
   5. Strongly agree

Likert scaling is a bipolar scaling method, measuring either positive or negative response to a statement. Sometimes a four-point scale is used; this is a forced choice method[citation needed] since the middle option of "Neither agree nor disagree" is not available.

Likert scales may be subject to distortion from several causes. Respondents may avoid using extreme response categories (central tendency bias); agree with statements as presented (acquiescence bias); or try to portray themselves or their organization in a more favorable light (social desirability bias). Designing a scale with balanced keying (an equal number of positive and negative statements) can obviate the problem of acquiescence bias, since acquiescence on positively keyed items will balance acquiescence on negatively keyed items, but central tendency and social desirability are somewhat more problematic.
Scoring and analysis

After the questionnaire is completed, each item may be analyzed separately or in some cases item responses may be summed to create a score for a group of items. Hence, Likert scales are often called summative scales.

Whether individual Likert items can be considered as interval-level data, or whether they should be considered merely ordered-categorical data is the subject of disagreement. Many regard such items only as ordinal data, because, especially when using only five levels, one cannot assume that respondents perceive all pairs of adjacent levels as equidistant. On the other hand, often (as in the example above) the wording of response levels clearly implies a symmetry of response levels about a middle category; at the very least, such an item would fall between ordinal- and interval-level measurement; to treat it as merely ordinal would lose information. Further, if the item is accompanied by a visual analog scale, where equal spacing of response levels is clearly indicated, the argument for treating it as interval-level data is even stronger.

When treated as ordinal data, Likert responses can be collated into bar charts, central tendency summarised by the median or the mode (but some would say not the mean), dispersion summarised by the range across quartiles (but some would say not the standard deviation), or analyzed using non-parametric tests, e.g. chi-square test, Mann–Whitney test, Wilcoxon signed-rank test, or Kruskal–Wallis test.[4] Parametric analysis of ordinary averages of Likert scale data is also justifiable by the Central Limit Theorem, although some would disagree that ordinary averages should be used for Likert scale data.

Responses to several Likert questions may be summed, providing that all questions use the same Likert scale and that the scale is a defendable approximation to an interval scale, in which case they may be treated as interval data measuring a latent variable. If the summed responses fulfill these assumptions, parametric statistical tests such as the analysis of variance can be applied. These can be applied only when more than 5 Likert questions are summed.[citation needed]

Data from Likert scales are sometimes reduced to the nominal level by combining all agree and disagree responses into two categories of "accept" and "reject". The chi-square, Cochran Q, or McNemar test are common statistical procedures used after this transformation.

Consensus based assessment (CBA) can be used to create an objective standard for Likert scales in domains where no generally accepted standard or objective standard exists. Consensus based assessment (CBA) can be used to refine or even validate generally accepted standards.
Level of measurement

The five response categories are often believed to represent an Interval level of measurement. But this can only be the case if the intervals between the scale points correspond to empirical observations in a metric sense. In fact, there may also appear phenomena which even question the ordinal scale level. For example, in a set of items A,B,C rated with a Likert scale circular relations like A>B, B>C and C>A can appear. This violates the axiom of transitivity for the ordinal scale.
Rasch model

Likert scale data can, in principle, be used as a basis for obtaining interval level estimates on a continuum by applying the polytomous Rasch model, when data can be obtained that fit this model. In addition, the polytomous Rasch model permits testing of the hypothesis that the statements reflect increasing levels of an attitude or trait, as intended. For example, application of the model often indicates that the neutral category does not represent a level of attitude or trait between the disagree and agree categories.

Again, not every set of Likert scaled items can be used for Rasch measurement. The data has to be thoroughly checked to fulfill the strict formal axioms of the model.
Pronunciation

Rensis Likert, the developer of the scale, pronounced his name 'lick-urt' with a short "i" sound.[5][6] It has been claimed that Likert's name "is among the most mispronounced in [the] field."[7] Although many people use the long "i" variant ('lie-kurt'), those who attempt to stay true to Dr. Likert's pronunciation use the short "i" pronunciation ('lick-urt').

From Wikipedia, the free encyclopedia



The Likert scale is commonly used in survey research. It is often used to measure respondents' attitudes by asking the extent to which they agree or disagree with a particular question or statement. A typical scale might be "strongly agree, agree, not sure/undecided, disagree, strongly disagree." On the surface, survey data using the Likert scale may seem easy to analyze, but there are important issues for a data analyst to consider.



Instructions

   1. Get your data ready for analysis by coding the responses. For example, let's say you have a survey that asks respondents whether they agree or disagree with a set of positions in a political party's platform. Each position is one survey question, and the scale uses the following responses: Strongly agree, agree, neutral, disagree, strongly disagree. In this example, we'll code the responses accordingly: Strongly disagree = 1, disagree = 2, neutral = 3, agree = 4, strongly agree = 5.
   2. Remember to differentiate between ordinal and interval data, as the two types require different analytical approaches. If the data are ordinal, we can say that one score is higher than another. We cannot say how much higher, as we can with interval data, which tell you the distance between two points. Here is the pitfall with the Likert scale: many researchers will treat it as an interval scale. This assumes that the differences between each response are equal in distance. The truth is that the Likert scale does not tell us that. In our example here, it only tells us that the people with higher-numbered responses are more in agreement with the party's positions than those with the lower-numbered responses.
   3. Begin analyzing your Likert scale data with descriptive statistics. Although it may be tempting, resist the urge to take the numeric responses and compute a mean. Adding a response of "strongly agree" (5) to two responses of "disagree" (2) would give us a mean of 4, but what is the significance of that number? Fortunately, there are other measures of central tendency we can use besides the mean. With Likert scale data, the best measure to use is the mode, or the most frequent response. This makes the survey results much easier for the analyst (not to mention the audience for your presentation or report) to interpret. You also can display the distribution of responses (percentages that agree, disagree, etc.) in a graphic, such as a bar chart, with one bar for each response category.
   4. Proceed next to inferential techniques, which test hypotheses posed by researchers. There are many approaches available, and the best one depends on the nature of your study and the questions you are trying to answer. A popular approach is to analyze responses using analysis of variance techniques, such as the Mann Whitney or Kruskal Wallis test. Suppose in our example we wanted to analyze responses to questions on foreign policy positions with ethnicity as the independent variable. Let's say our data includes responses from Anglo, African-American, and Hispanic respondents, so we could analyze responses among the three groups of respondents using the Kruskal Wallis test of variance.
   5. Simplify your survey data further by combining the four response categories (e.g., strongly agree, agree, disagree, strongly disagree) into two nominal categories, such as agree/disagree, accept/reject, etc.). This offers other analysis possibilities. The chi square test is one approach for analyzing the data in this way.


Read more: How to Use the Likert Scale in Statistical Analysis | eHow.com http://www.ehow.com/how_4855078_use-likert-scale-statistical-analysis.html#ixzz1LGrJsRUS


Opinion:

There's a huge debate ongoing in the social / behavioral sciences over whether Likert scales should be treated as ordinal or interval.


Count me as one who thinks it's OK to treat them as interval.


I would analyze the data both ways - with chi-square and with ANOVA, and see how it turns out - if the outcomes are the same, you're all set. If you get something different with each method, then you have something interesting...


Overall, you can treat the scales as interval and run methods that compare means, such as ANOVA. The scales are close enough to interval so that these methods shouldn't lead you astray.

Yes, Tukey would be fine for a post-hoc test. It's "middle-of-the-road" in terms of liberal/conservative (Fisher's LSD is liberal, Bonferroni is conservative).


In terms of how you would use chi-square, you could set up a comparison between the groups you want to contrast, and do the analysis on the frequency of each choice, between the groups (i.e., did one group choose "agree" more often than another group). Yes, it would be a chi-square test of independence. The contingency table could be set up with groups as rows, and scale items as 8 columns. The cells of the table would contain the response frequencies.


For chi-square post-hoc, use a simple comparison of two independent proportions with a z test.


You wouldn't necessarily report means with a chi-square analysis, since your interest is in comparing frequencies, but that's not to say you wouldn't do some sort of basic descriptive statistics comparison (means, medians, std dev, etc.)

Kamis, 05 Mei 2011

Statistics for Management and Economics Answers

Introduction to statistics for the management and economics answers:
                    Statistics for the management and economics answers deals with the statistics mechanisms which is used to plot the data in a tabularized form and the term economics deals with the production and consumption, the data used in the economics are represented using the statistical methods so that we can manage the economical easily. In this article we deal with the statistics for the management and economics answers.

Statistics for the Management and Economics Answers:

Managing the data:
    The economical data are represented using statistical methods like graphs, charts like tables, charts, graphs or in a standard format and also in finding the probability of the random variables using the different probability distributions like poisson,binomial we can manage the economical data.
The following are the points that represents the importance  of statistics to economics, they are
  • Quantitative expression of economic problem
  • Inter sectoral and inter temporal comparisons
  • working out  cause and effect relationship
  • construction of economic theories and models
  • economic forecasting
  • Formulation of policies
  • economic equilibrium

Example Problems - Statistics for the Management and Economics Answers

Some of the statistical methods to manage the data are given below,
Here we will see how the sample problems are solved using the different types of graphs like bar graph, histogram, pie- chart,line graph, scatter plot graphs.
Depends on the types of data, we can select the types of graphs.
Example problem 1- statistics for the management and economics answers
Let us consider the following organised data given in the table, Manage the given organised data using statistical graphs.
The following table shows, the numbers of visitors in the bank in a week.Solve the organised data.
DaysVisitors
135
265
324
460
571
642
796


Solution:
The given data can be solved using the statistical graphs. The given days and number of visitors can be plotted using the bar graph where the x axis takes the days and y -axis takes the visitors range.
Bar graph - statistics for the management and economics answers
Example problem 2 - statistics for the management and economics answers
Calculate the value of the Poisson probability distribution in which the , value is 4, x value is 7 and e = 2.718
Solution:  
Step 1: Given:
  = 4
x = 7
Step 2: Formula:
Poisson probability distribution =  
Step 3: To find e:
e-4 = (2.718)-4
     = 0.01831
Step 4: Solve:
  = 4
   x = 7
= (4) = 16384
Step 4: Substitute:
  =
                    =
                     = 0.06
Result: Poisson probability Distribution = 0.06

http://www.tutorvista.com

Statistika : Kesederhanaan yang dapat membangun sebuah keputusan yang tepat





 By: Daniel Agustinus Nababan


 





Pendahuluan
Fenomena berpikir tanpa berpikir atau yang lebih dikenal dengan BLINK sangat marak di dunia pemasaran. Pemasar mulai mencari insight yang membantu mereka dalam membentuk sebuah keputusan yang tepat untuk memecahkan masalah di dunia pemasaran. Decision Support System yang harus dimiliki pemasar harus benar-benar lengkap dan integrated system. Decision Support System itu tidak harus sampai kepada informasi intelligence tapi cukup hanya dalam ranah data sederhana dengan tampilan yang sederhana-pun, data itu mampu berbicara banyak dan merangsang pemasar untuk menjadikan sebuah program yang sukses dan berkelanjutan.
Malcolm Galdwell dalam bukunya mengatakan bahwa kita perlu 10,000 hours untuk menjadi seorang ahli. Tapi bukan hanya semata-mata menghabiskan 10,000 jam tapi tidak melakukan apa-apa. Tapi bekerja dengan sekeras mungkin. Di dalam bekerja sekeras mungkin itu (extremely worked hard), itulah akan muncul sebuah tingkat intelegensi yang dapat memadukan beberapa data sederhana menjadi sebuah pengetahuan yang dapat ditindaklanjuti.

Statistika dan Statistik


Ketika mendengar kata statistik, orang-orang pasti akan cenderung berpendapat negative dibandingkan dengan pendapat positif. Statistik memang lebih dekat kepada sebuah kelompok data yang ribet, berupa baris dan kolom, deretan dan susunan angka-angka bahkan dengan kata “menyusahkan”-pun sangat dekat. Tapi di balik itu, apa yang bisa kita dapatkan dari statistik itu?
Wikipedia menjelaskan bahwa statistik adalah data, informasi, atau hasil penerapan algoritma statistika pada suatu data. Sedangkan ilmu yang mempelajarinya adalah Statistika. Salah satu alat yang paling sederhana dalam tehnik analisis statistik adalah analisis deskriptif.
Tehnik analisis statistik deskriptif adalah metode-metode yang berkaitan dengan pengumpulan dan penyajian suatu gugus data sehingga memberikan informasi yang berguna. Contoh statistika deskriptif yang sering muncul adalah, tabel, diagram, grafik, dan besaran-besaran lain. Dengan Statistika deskriptif, kumpulan data yang diperoleh akan tersaji dengan ringkas dan rapi serta dapat memberikan informasi inti dari kumpulan data yang ada.
Hal inti menjadi sorotan utama dari statistika deskriptif. Bagaimana inti dari kumpulan data yang diambil menjadi sebuah system yang dapat mendukung kita dalam mengambil sebuah kesimpulan. Tabel, diagram dan grafik tidak menjadi bentuk transformasi dari tabulasi-tabulasi dan deret angka-angka yang ditampilkan dari analisis statistic yang dilakukan melalui software-software statistik yang ada.
Ada beberapa hal yang perlu diperhatikan dalam transformasi data :
1.Transformasi yang dimaksud mencitrakan angka dan data yang benar dan ada.
2.Transformasi yang ditampilkan dapat meringkas dan berbicara lebih cepat.
3.Transformasi yang terbentuk mempercepat kita memahami karakteristik yang terpola.
4.Transformasi yang diwujudkan merangsang kita untuk lebih mengerti.
Dari keempat hal itulah akan muncul sebuah BLINK yang menjadikan statistic itu berdaya guna dan dapat ditindaklanjuti. Kenyataan bahwa statistik yang sederhana itu semakin terwujud ketika grafik dan diagram itu menceritakan gejala dan karakteristik yang ada.


Kesederhanaan yang dapat membangun sebuah keputusan yang tepat

Melalui data-data dan grafik sederhana dapat muncul sebuah keputusan yang tepat. Bukan harus melalui alat statistic atau metoda analisis yang advance atau inferens bisa muncul keputusan yang tepat. Memang metoda analisis lanjutan tetap dibutuhkan untuk mempelajari lebih dalam, tapi dengan melihat gejala pusat yang tepat, sebenarnya kita bisa membuat keputusan yang tepat dengan tingkat kepercayaan yang kita yakini.
Intinya adalah melihat dengan tepat sesuatu yang tepat. Mencermati statistic yang tepat dengan metoda yang tepat. Bukan semata-mata menampilkan grafik dan table yang hebat, namun bukan menggambarkan karakteristik yang ingin diteliti.
Data atau statistic itu dibutuhkan oleh setiap orang. Dan statistic itu sekarang ada banyak dan berlimpah. Namun, apakah orang sudah menggunakannya dengan tepat? Apakah data yang tepat sudah berdaya guna atau masih menjadi tumpukan file yang hanya menunjukkan kumpulan data saja tanpa didayagunakan? Sesuatu yang besar itu tidak selalu datang dari sesuatu yang besar. Bisa saja dari sebuah hal yang kecil bahkan sederhana. Tapi, jika dengan cara yang tepat mengerjakan dengan tepat apa yang tepat itu akan menjadi besar dan berdayaguna.


Book Review: How to Lie with Statistics

How to Lie with Statistics, by Darrel Huff, should be required reading for everyone. The cachet of numbers are used all the time in modern society. Usually to end arguments–after all, who can argue with “facts”? Huff shows how the same set of numbers can be tweaked to show three different outcomes, depending on where you start and what you use. The fundamental lesson I learned from this book is that mathematical calculation involves a whole set of conditions, and any number derived from such a calculation is meaningless without understanding those conditions.
He also mentions that colleagues have told him that the flurry of meaningless statistics is due to incompetence–he dispatches this argument with a simple query: “Why, then, do the numbers almost always favor the person quoting them?” Huff also provides five questions (not unlike the five d’s of dodgeball) for readers to ask, when confronted with a statistic:
1. Who says so?
2. How does he know?
3. What’s missing?
4. Did somebody change the subject?
5. Does it make sense?
All this is wrapped up in a book with simple examples (no math beyond arithmetic, really) and quaint 1950s prose. In addition humor runs from the beginning (the dedication is “To my wife with good reason”) to the end (on page 135, Huff says “Almost anybody can claim to be first in something if he is not too particular what it is”). This book is well worth a couple hours of your time.

source: www.mooreds.com