[PPT]Slide 1 - Universitas · Web viewMelakukanmanipulasi data dengancaratransformasi “skala” dari ordinal menjadi interval, selainbertujuanuntuktidakmelanggarkelaziman, jugauntukmengubah

SUMBER KESALAHAN DALAM ……….

METODE SURVEI KAJIAN LINGKUNGAN

FOTO: smno.kampus.ub.febr2012

SUMBER KESALAHAN• Social desirability

– Giving politically correct answers

• Response sets– All yes, or all no

responses• Acquiescence

– Telling you what you want to hear

• Personal bias– Wants to send a

message

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• Response order– Recency - Respondent stops reading

once s/he gets to the response s/he likes

– Primacy - Remember better the initial choices

– Fatigue

• Item order– Answers to later items may be affected

by earlier items (simple, factual items first)

– Respondent may not know how to answer earlier questions

MENILAI INSTRUMENT

Three issues to consider– Validity: Does the instrument measure

what its supposed to measure– Reliability: Does it consistently repeat the

same measurement– Practicality: Is this a practical instrument

Sumber: Dr.Ir. Pudji Muljono, Msi. Disampaikan pada Lokakarya Peningkatan Suasana AkademikJurusan Ekonomi FIS-UNJ tanggal 5 sampai dengan 9 Agustus 2002

Diunduh dari: https://docs.google.com/viewer?a=v&q=cache:k1SsN7H88fAJ:repository.ipb.ac.id/bitstream/handle/

MENILAI INSTRUMENT

Proses Validasi Konsep Melalui Panel1. Memeriksa instrumen mulai dari konstruk sampai penyusunan butir

Dalam kaitan ini, beberapa hal yang perlu diperhatikan antara lain :

1. Apakah dimensi yang dirumuskan sudah merupakan jabaran yang tepat dari konstruk yang telah dirumuskan dan sesuai untuk mengukur konstruk dari variabel yang hendak diukur ?

2. Apakah indikator yang dirumuskan sudah merupakan jabaran yang tepat dari dimensi yang telah dirumuskan dan sesuai untuk mengukur konstruk dari variabel yang hendak diukur ?

3. Apakah butir-butir instrumen yang dibuat telah sesuai untuk mengukur indikator-indikator dari variabel yang hendak diukur ?

2. Menilai butir Item

Butir yang sudah dibuat diberikan kepada sekelompok panel untuk dinilai dengantetap mengacu pada tolok ukur di atas.

Metode penilaian butir dapat dilakukan dengan beberapa cara, misalnya dengan Metode Thurstone dan Pair Comparison.

Sumber: Dr.Ir. Pudji Muljono, Msi. Disampaikan pada Lokakarya Peningkatan Suasana AkademikJurusan Ekonomi FIS-UNJ tanggal 5 sampai dengan 9 Agustus 2002

Diunduh dari: https://docs.google.com/viewer?a=v&q=cache:k1SsN7H88fAJ:repository.ipb.ac.id/bitstream/handle/

TIPE-TIPE VALIDITAS• Face validity

– Does the instrument, on its face, appear to measure what it is supposed to measure

• Content validity– Degree to which the content of the items

adequately represent the universe of all relevant items under study

– Generally arrived at through a panel of experts

TIPE-TIPE VALIDITAS

Content validity“Validity refers to the degree to which evidence and theory support the

interpretations of test scores entailed by proposed uses of tests (AERA/APA/NCME, 1999).

Content validity refers to the degree to which the content of the items reflects the content domain of interest (APA, 1954)

Content validity addresses the adequacy and representativeness of the items to the domain of testing purposes

Content validity is not usually quantified possibly due to :1.) subsuming it within construct validity; 2.) ignoring it as important; and/or 3.) relying on accepted expert agreement procedures

Diunduh dari: plaza.ufl.edu/.../CONTENT%20VALIDITY.p... 25/8/2012

• Criterion related– Degree to which the predictor

is adequate in capturing the relevant aspects of criterion

– Uses Correlation analysis– Concurrent validity

• Criterion data is available at the same time as predictor score- requires high correlation between the two

– Predictive validity• Criterion is measured after the

passage of time• Retrospective look at the

validity of the measurement• Known-groups

TIPE-TIPE VALIDITAS

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• Criterion related– Degree to which the predictor is

adequate in capturing the relevant aspects of criterion

– Uses Correlation analysis– Concurrent validity

• Criterion data is available at the same time as predictor score- requires high correlation between the two

– Predictive validity• Criterion is measured after the

passage of time• Retrospective look at the validity of

the measurement• Known-groups

• Stability– Test-retest: Same test is administered twice to the same subjects over a short interval (3 weeks

to 6 months)– Look for high correlation between the test and retest– Situational factors must be minimized

TIPE-TIPE RELIABILITAS

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• Equivalence– Degree to which alternative forms of the same measure produce same or similar results– Give parallel forms of the same test to the same group with a short delay to avoid fatigue– Look for high correlation between the scores of the two forms of the test– Inter-rater reliability

• Internal Consistency– Degree to which instrument items are homogeneous and reflect the same

underlying constructs– Split-half testing where the test is split into two halves that contain the same types

of questions– Uses Cronbach’s alpha to determine internal consistency. Only one administration

of the test is required– Kuder-Richardson (KR20) for items with right and wrong answers

PRAKTIKALITAS• Is the survey economical

• Cost of producing and administering the survey• Time requirement• Common sense!

• Convenience• Adequacy of instructions• Easy to administer

• Can the measurement be interpreted by others• Scoring keys• Evidence of validity and reliability• Established norms

Diunduh dari: ………….. 23/8/2012

A comparison of Likert scale and traditional measures of self-efficacy.By Maurer, Todd J.; Pierce, Heather R.

Journal of Applied Psychology, Vol 83(2), Apr 1998, 324-329.

This study addressed whether a Likert-type measurement format can be used as an alternative to the traditional format for measuring self-efficacy. Classical reliability, observed correlations with relevant criteria,

and confirmatory factor analyses were used to assess the similarity of the two formats in a sample of 128 college students.

The results indicated that Likert-type and traditional measures of self-efficacy have similar reliability–error variance, provide equivalent levels of prediction, and have similar factor structure and similar discriminability.

Overall, considering both practicality and the apparent similarity of empirical results from the two methods, a Likert scale seems to offer an acceptable alternative method of measuring self-efficacy.

Limitations and suggestions for future research are discussed.

Development of a Multi-item Scale

Develop Theory

Generate Initial Pool of Items: Theory, Secondary Data, and Qualitative Research

Collect Data from a Large Pretest Sample

Statistical Analysis

Develop Purified Scale

Collect More Data from a Different Sample

Final Scale

Select a Reduced Set of Items Based on Qualitative Judgement

Evaluate Scale Reliability, Validity, and Generalizability

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EVALUASI SEKALA

Discriminant NomologicalConvergent

Test/ Retest

Alternative Forms

Internal Consistency Content Criterion Construct

GeneralizabilityReliability Validity

Scale Evaluation

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Transformasi data ordinal ke interval dengan Method of Succesive Interval (MSI)

Diunduh dari: http://jurnal-sdm.blogspot.com/2007/12/transformasi-data-ordinal-ke-interval.html ………….. 24/8/2012

Untuk dapat diolah menjadi analisis regresi, data ordinal yang biasanya didapat dengan menggunakan skala likert, dll (skor kuesioner), maka terlebih dahulu data ini harus ditrasformasikan menjadi data interval salah satu cara yang dapat digunakan

adalah Method of Succesive Interval (MSI).

Sepintas memang terlihat sangat susah karena kita harus membuat frekuensi, kemudian menentukan proporsi, membuat proporsi komulatif dst.

Langkah-langkah Method of Succesive Interval (MSI).sebagai berikut:

1. Membuat ferkuensi dari setiap butir jawaban pada masing-masing kategori pertanyaan.2. Membuat proporsi dengan cara membagi frekuensi dari setiap butir jawaban dengan seluruh jumlah responden.3. Membuat proporsi kumulatif4. Menentukan nilai z untuk setiap butir jawaban berdasarkan nilai frekuensi yang telah diperoleh dengan bantuan tabel z

riil.5. Menghitung nilai skala, dengan rumus:

6. Penyertaan nilai skala

Nilai penyertaan inilah yang disebut skala interval dan dapat digunakan dalam perhitungan analisis regresi.

http://bp3.blogger.com/_kubpHI6MtGI/R1zvcxsug_I/AAAAAAAAAYc/qgNa55nwgbU/s1600-h/skala.GIF

TRANSFORMASI DATA ORDINAL MENJADI INTERVAL

Diunduh dari: myunanto.staff.gunadarma.ac.id/.../Transformasi+Data+Ordinal+Men...………….. 24/8/2012

Data primer adalah data yang direspon langsung oleh responden berdasarkan wawancara ataup daftar pertanyaan yang dirancang, disusun, dan disajikan dalam bentuk skala; baik skala nominal, ordinal, interval

maupun ratio.

Teknik pengumpulan data seperti ini lazim digunakan karena selain bisa langsung menentukan skala pengukuranya, juga dapat melengkapi hasil wawancara yang dilakukan dengan responden.

Melakukan manipulasi data dengan cara transformasi “skala” dari ordinal menjadi interval, selain bertujuan untuk tidak melanggar kelaziman, juga untuk mengubah agar syarat distribusi normal dapat dipenuhi ketika

menggunakan statistika parametrik. Menurut Sambas Ali Muhidin dan Maman Abdurahman, “salah satu metode transformasi yang sering digunakan adalah metode succesive interval (MSI)”.

Ada dua pendapat berbeda tentang bagaimana skor-skor yang diberikan terhadap alternatif jawaban pada skala pengukuran Likert.

Pendapat pertama mengatakan bahwa skor 1, 2, 3, 4, dan 5 adalah data interval. Pendapat ke dua, menyatakan bahwa jenis skala pengukuran Likert adalah ordinal.

Alasannya skala Likert merupakan Skala Interval adalah karena skala sikap merupakan dan menempatkan kedudukan sikap seseorang pada kesatuan perasaan kontinum yang berkisar dari sikap “sangat positif”, artinya

mendukung terhadap suatu objek psikologis terhadap objek penelitian, dan sikap “sangat negatif”, yang tidak mendukung sama sekali terhadap objek penelitian.

Ciri spesifik yang dimiliki oleh data yang diperoleh dengan skala pengukuran ordinal, adalah bahwa, data ordinal merupakan jenis data kualitatif, bukan numerik, berupa kata-kata atau kalimat, seperti misalnya sangat setuju, kurang setuju, dan tidak setuju, jika pertanyaannya ditujukan terhadap persetujuan tentang suatu event. Atau bisa juga respon terhadap keberadaan suatu Bank “PQR” dalam suatu daerah yang bisa dimulai dari sangat

tidak setuju, tidak setuju, ragu-ragu, Setuju, dan sangat setuju. Data interval adalah termasuk data kuantitatif, berbentuk numerik, berupa angka, bukan terdiri dari kata-kata,

atau kalimat. Peneliti melakukan penelitian dengan menggunakan pendekatan kuantitatif, termasuk di dalamnya adalah data interval, data yang diperoleh dari hasil pengumpulan data bisa langsung diolah dengan

menggunakan model statistika. Akan tetapi data yang diperoleh dengan pengukuran skala ordinal, berbentuk kata-kata, kalimat, penyataan, sebelum diolah, perlu memberikan kode numerik, atau simbol berupa angka

dalam setiap jawaban.

PERLUKAH DATA ORDINAL DI TRANSFORMASI KE INTERVAL DENGAN MSI?Posted by: Muji Gunarto on: 25 Desember 2008

Diunduh dari: http://mujigunarto.wordpress.com/2008/12/25/perlukah-data-ordinal-di-transformasi-ke-interval-dengan-msi/………….. 24/8/2012

Data ordinal dengan Skala Likert STS(1), TS(2), R(3), S(4), SS(5) jika diubahskalanya menjadi interval maka skore interval akan mirip sama urutannya dengan skore asli ordinal dan berkorelasi sebesar

99%.

Jadi data asli ordinal sama dengan interval dan dapat dianggap interval.

Hal yang membedakan adalah interpretasi model dari hasil analisis anatara data ordinal dengan data interval.

Misalkan ada model regresi sebagai berikut:

Y = a + b1X1 +b2X2

Y = 0.50 +0.25X1 +0.30X2

Jika data interval misal Y = Produksi padi (ton/Ha), X1 = Pupuk UREA (kg/Ha) dan X2 = Bibit (kg/Ha), maka interpretasinya adalah kalau pupuk dinaikan 10% maka produksi padi akan naik 2.5%, kalau bibit naik 10%, maka produksi padi naik 3%.

Kalau data ordinal (kualitatif) misalnya Y= kepuasan kerja, X1=Komitmen, X2=motivasi, maka tidak bisa diinterpretasikan jika komitmen naik 10% maka kepuasan naik 2.5% (karena datanya kualitatif) jadi hanya bisa dikatakan bahwa komitmen

berpengaruh (signifikan) terhadap kepuasan kerja seberapa besar pengaruhnya tidak tahu (karena kualiatif).

Walaupun data ordinal tadi sudah menjadi interval tetap saja kita tidak bisa interpretasi seperti data kuantitatif karena data aslinya adalah kualitatif.

Diunduh dari: http://www.cengage.com/marketing/book_content/1439080674_zikmund/book/ch15.pdf ………….. 25/8/2012

For a questionnaire to fulfill a researcher’s purposes, the questions must meet the basic criteria of relevance and accuracy.

To achieve these ends, a researcher who is systematically planning a questionnaire’s design will be required to make several decisions—

typically, but not necessarily, in the following order:

1. What should be asked?2. How should questions be phrased?3. In what sequence should the questions be arranged?4. What questionnaire layout will best serve the research objectives?5. How should the questionnaire be pretested? Does the

questionnaire need to be revised?

Questionnaire design

What Should Be Asked?

Certain decisions made during the early stages of the research process will influence the questionnaire design. The preceding chapters stressed good problem definition and clear

research questions. This leads to specific research hypotheses that, in turn, clearly indicate what must be

measured.

Different types of questions may be better at measuring certain things than are others. In addition, the communication medium used for data collection—that is, telephone interview,

personal interview, or self-administered questionnaire—must be determined.

This decision is another forward linkage that influences the structure and content of the questionnaire. Therefore, the specific questions to be asked will be a function of previous

decisions made in the research process.

At the same time, the latter stages of the research process will also have an important impact on questionnaire wording and measurement.

For example, when designing the questionnaire, the researcher should consider the types of statistical analysis that will be conducted.


Questionnaire design

Questionnaire designA survey is only as good as the questions it asks

Diunduh dari: http://jurnal-sdm.blogspot.com/2011/06/penyusunan-kuesioner-penelitian.html………….. 24/8/2012

Langkah-Langkah Pembuatan Quesioner:

Langkah 1:• Menentukan Hipotesis• Menentukan tipe survey yang akan digunakan• Menentukan pertanyaan-pertanyaan survey• Menentukan kategori jawaban• mendesain letak survey

Langkah 2:• Rencanakan bagaimana data akan dikumpulkan• Uji awal alat pengukuran

Langkah 3:• tentukan target populasi• tentukan teknik sampling (random sampling, non random sampling)• tentukan ukuran sampel• pilih sampel

Langkah 4:• Temukan responden• lakukan interview/wawancara• kumpulkan data dengan teliti

Langkah 5:• Masukkan data kedalam komputer• periksa ulang seluruh data• lakukan analisis statistik pada data yang diperoleh

Langkah 6:• Jelaskan metode dan penemuan dalam laporan penelitian• Presentasikan untuk mendapatkan masukan dan evaluasi

What should you ask?

• The questions asked are a function of previous decisions

• The questions asked are a function of future decisions (such as statistical analysis)

Diunduh dari: http://www.trunity.net/oceanresource/topics/view/55385/ ………….. 25/8/2012

Ecosystem services (also called environmental services or nature’s services) are benefits provided by ecosystems to humans, that contribute to making human life both possible and worth living. Many of these goods and services are traditionally viewed as free benefits to society, or "public goods" - wildlife habitat and diversity, watershed services, carbon storage, and scenic landscapes, for example. Lacking a formal market, these natural assets are traditionally absent from society’s balance

sheet; their critical contributions are often overlooked in public, corporate, and individual decision-making.

Key criteria• Questionnaire relevancy

– No unnecessary information is collected and only information needed to solve the problem is obtained. Be specific about your data needs; tie each question to an objective

• Questionnaire accuracy– Information is both reliable and valid


What is LCA? In the context of environmental challenges and the need for more sustainable production modes, Life

Cycle Assessment (LCA) has been brought forward as an important and comprehensive method for analyzing the environmental impact of products and services. While its has long been used in the industry,

LCA has only been applied to agricultural systems for the last 10 years. (http://lca-rice.cirad.fr/what_is_lca)

LCA is defined and framed by ISO standards. It involves 4 typical phases:1. Goal and scope definition (where system is delineated, indicators are chosen, functional unit is

selected, ways of presenting results are decided upon, etc.)2. Inventory analysis (where all inputs and resources used are inventoried and quantified, related to the

given functional unit; it is a kind of mass and energy balance, focused on environmentally relevant flows)

3. Impact assessment (where environmental impact indicators are calculated, involving classification and characterization stages)

4. Interpretation and presentation of results (with necessary caution regarding indicators -uncertainty and errors should be considered, sensitivity analysis should be carried out).

Key criteria


http://lca-rice.cirad.fr/var/projets/storage/images/media/media_lca_rice/licorice_system/43133-1-fre-FR/licorice_system_reference.jpg

Questionnaire RelevancyA questionnaire is relevant to the extent that all information collected addresses a research question that will help the decision maker address the current business

problem. Asking a wrong question or an irrelevant question is a common pitfall. If the task is to pinpoint store image problems, questions asking for political opinions are likely

irrelevant. The researcher should be specific about data needs and have a rationale for each item

requesting information. Irrelevant questions are more than a nuisance because they make the survey needlessly long. In a study where two samples of the same group of

businesses received either a one-page or a three-page questionnaire, the response rate was nearly twice as high for the one-page survey.

Conversely, many researchers, after conducting surveys, find that they omitted some important questions. Therefore, when planning the questionnaire design, researchers must think about possible omissions. Is information on the relevant demographic and

psychographic variables being collected?

Would certain questions help clarify the answers to other questions? Will the results of the study provide the answer to the manager’s problem?


Questionnaire AccuracyOnce a researcher decides what should be asked, the criterion of accuracy becomes the

primary concern. Accuracy means that the information is reliable and valid. While experienced researchers generally believe that questionnaires should use simple, understandable, unbiased, unambiguous, and nonirritating words, no step-by-step

procedure for ensuring accuracy in question writing can be generalized across projects. Obtaining accurate answers from respondents depends strongly on the researcher’s ability to design a questionnaire that will facilitate recall and motivate respondents to

cooperate. Respondents tend to be more cooperative when the subject of the research interests them. When questions are not lengthy, difficult to answer, or ego threatening,

there is a higher probability of obtaining unbiased answers.

Question wording and sequence also substantially influence accuracy, which can be particularly challenging when designing a survey for technical audiences. The

Department of Treasury commissioned a survey of insurance companies to evaluate their offering of terrorism insurance as required by the government’s terrorism

reinsurance program. But industry members complained that the survey misused terms such as “contract” and “high risk,” which have precise meanings for insurers, and asked for policy information “to date,” without specifying which date. These questions caused

confusion and left room for interpretation, calling the survey results into question.


Phrasing Questions• Open ended response versus fixed alternative questions

“?”• Decision criteria: type of research; time; method of delivery; budget; concerns regarding researcher bias

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Open-ended response questions pose some problem or topic and ask respondents to answer in their own words. If the question is asked in a personal interview, the interviewer may probe for more information, as in the following

examples:

1. What names of local banks can you think of?2. What comes to mind when you look at this advertisement?3. In what way, if any, could this product be changed or improved? I’d like you to tell me anything you can 4. think of, no matter how minor it seems.5. What things do you like most about working for Federal Express? What do you like least?6. Why do you buy more of your clothing in Nordstrom than in other stores?7. How would you describe your supervisor’s management style?8. Please tell us how our stores can better serve your needs.

Open-ended response questions are free-answer questions. The fixed-alternative questions—sometimes called closed-ended questions—which give respondents

specific limited-alternative responses and ask them to choose the one closest to their own viewpoints.

For example:Did you use any commercial feed or supplement for livestock or poultry in 2010?

Yes No

Would you say that the labor quality in Japan is higher, about the same, or not as good as it was 10 years ago?

Higher About the same

Not as good

Avoid• Leading questions (pertanyaan yang “menggiring”)• Overly complex questions • Use of jargon• Loaded questions (can use a counterbiasing statement)• Ambiguity• Double barreled questions• Making assumptions


Avoid Leading and Loaded Questionsleading question = A question that suggests or implies certain answers.

Leading and loaded questions are a major source of bias in question wording. A leading question suggests or implies certain answers.

A study of the dry cleaning industry asked this question:

Many people are using dry cleaning less because of improved wash-and-wear clothes. How do you feel wash-and-wear clothes have affected your use of dry cleaning facilities in the past 4 years?

Use less No change Use more

It should be clear that this question leads the respondent to report lower usage of dry cleaning. The potential “bandwagon effect” implied in this question threatens the study’s validity.

loaded question = A question that suggests a socially desirable answer or is emotionally charged.A loaded question suggests a socially desirable answer or is emotionally charged.

Consider the following question from a survey about media influence on politics:What most influences your vote in major elections?

1. My own informed opinion2. Major media outlets such as CNN3. Newspaper endorsements4. Popular celebrity opinions5. Candidate’s physical attractiveness6. Family or friends7. Video advertising (television or Web video)8. Other

Order?• Order bias results from an alternative answer’s position in a set of answers or from the

sequencing of questions– Funneling technique: general to specific helps understand the frame of reference first

• Anchoring effect: the first concept measured tends to become a comparison point from which subsequent evaluations are made

COUNTERBIASING STATEMENT

An introductory statement or preamble to a potentially embarrassing question that reduces a respondent’s reluctance to answer by suggesting that certain behavior is not unusual.

An introductory counterbiasing statement or preamble to a question that reassures respondents that their “embarrassing” behavior is not abnormal may yield truthful responses:

Some people have time to brush three times daily but others do not. How often did you brush your teeth yesterday?

If a question embarrasses the respondent, it may elicit no answer or a biased response. This is particularly true with respect to personal or classification data such as income or education. The problem may be

mitigated by introducing the section of the questionnaire with a statement such as this:

To help classify your answers, we’d like to ask you a few questions. Again, your answers will be kept in strict confidence.


AVOID AMBIGUITY: BE AS SPECIFIC AS POSSIBLE

Items on questionnaires often are ambiguous because they are too general. Consider such indefinite words as often, occasionally, regularly, frequently, many, good, and poor. Each of these words has many different

meanings.


For one consumer, frequent reading of Fortune magazine may be reading all 25 issues in a year, while another might think 12, or even 6 issues a year is frequent. Earlier, we used the following question as an example of a checklist question:

Please check which, if any, of the following sources of information about investments you regularly use.

What exactly does regularly mean? It can certainly vary from respondent to respondent. How exactly does hardly any differ from occasionally? Where is the cutoff? It is much better to use specific time periods whenever possible.

A brewing industry study on point-of-purchase advertising (store displays) asked their distributors:

How often does the company shut down production for sanitary maintenance?1. Annually (once a year)2. Semiannually (once every six months)3. Quarterly (about every three months)4. At least once monthly 5. Less frequently (less often than once a year)

Here the researchers clarified the terms permanent, semipermanent, and temporary by defining them for the respondent. However, the question remained somewhat ambiguous. Beer marketers often use a variety of point-of-purchase devices to serve different purposes—in this

case, what is the purpose? In addition, analysis was difficult because respondents were merely asked to indicate a preference rather than a degree of preference. Thus, the meaning of a question may not be clear because the frame of reference is inadequate for interpreting the

context of the question.

A student research group asked this question: What media do you rely on most?6. Television7. Radio8. Internet9. Newspapers

This question is ambiguous because it does not provide information about the context. “Rely on most” for what—news, sports, entertainment? When—while getting dressed in the morning, driving to work, at home in the evening? Knowing the

specific circumstance can affect the choice made.

• Ranking, sorting, rating or choice?• How many categories or response positions?• Balanced or unbalanced?• Forced choice or nonforced choice?• Single measure or index?

Decisions

Diunduh dari: http://aapnews.aappublications.org/content/25/6/279.2.full ………….. 25/8/2012

The Air Quality Index (AQI) is an index for reporting daily air quality. The Environmental Protection Agency calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate

matter), carbon monoxide, sulfur dioxide and nitrogen dioxide. The higher the AQI value, the greater the level of air pollution and the greater the health concern.

• Single dichotomy or dichotomous-alternative questions“Are you currently registered in a course at the University of

Lethbridge?Yes____ No____”

• Respondent chooses one of two alternatives (yes/no; male/female)

• What scale would this data create?

Types of fixed alternative questions…

• Multi-choice alternative– Respondent chooses from several alternatives– Many types…

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• Determinant choice– Choose only one from several possible responses“Which faculty are you currently registered in at the University of

Lethbridge?Management ___Education ____Arts/Science____Health sciences____Combined degree____

• What type of scale would these data create?

Multi-choice alternative questions…

• Frequency determination– Asks for an answer about frequency of occurrenceIn a typical week, how often do you purchase chocolate chip

cookies?__never__ once__ 2 or more times

What type of scale would these data create?Diunduh dari: ………….. 23/8/2012

• Check list– Provide multiple answers to a single question– Should be mutually exclusive and exhaustive

“What brands of chocolate chip cookies have you, to the best of your memory, purchased in the past month (check all that apply?)”

__ Dare__ Chips A’hoy__ Presidents Choice Decadent etc. etc.

• What type of scale would these data create?

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CHECK LIST

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CHECK LIST

The checklist question allows the respondent to provide multiple answers to a single question. The respondent indicates past experience, preference, and the like merely

by checking off items. In many cases the choices are adjectives that describe a particular object.

A typical checklist question might ask the following:

Please check which, if any, of the following sources of information about investments you regularly use.

1. Personal advice of your broker(s)2. Brokerage newsletters3. Brokerage research reports4. Investment advisory service(s)5. Conversations with other investors6. Web page(s)7. None of these8. Other (please specify) __________

Attitude:An enduring disposition to consistently respond to various aspect of the world, including persons, events and objectsTypically seen as having three components:– Cognitive– Affective– Behavioural

Diunduh dari: http://www.managementstudyguide.com/attitude-scales.htm………….. 24/8/2012

ATTITUDE RATING SCALES

Scaling Techniques for Measuring Data Gathered from RespondentsThe term scaling is applied to the attempts to measure the attitude objectively. Attitude is a resultant of number of external and internal factors. Depending upon the attitude to be measured, appropriate scales are designed. Scaling is a technique used for measuring qualitative responses of respondents such as those related to their feelings, perception, likes, dislikes,

interests and preferences.

Nominal ScaleThis is a very simple scale. It consists of assignment of facts/choices to various alternative categories which are usually

exhaustive as well mutually exclusive. These scales are just numerical and are the least restrictive of all the scales. Instances of Nominal Scale are - credit card numbers, bank account numbers, employee id numbers etc. It is simple and widely used

when relationship between two variables is to be studied. In a Nominal Scale numbers are no more than labels and are used specifically to identify different categories of responses.

How do you stock items at present?[ ] By product category[ ] At a centralized store[ ] Department wise[ ] Single warehouse.

Ordinal ScaleOrdinal scales are the simplest attitude measuring scale used in Marketing Research. It is more powerful than a nominal

scale in that the numbers possess the property of rank order. The ranking of certain product attributes/benefits as deemed important by the respondents is obtained through the scale.

Rank the following attributes (1 - 5), on their importance in a microwave oven.a. Company Name b. Functions c. Price d. Comfort e. Design

http://www.managementstudyguide.com/marketing_research.htm

AffectiveThe feelings or emotions toward an object

Diunduh dari: ………….. 23/8/2012

CognitiveKnowledge and beliefs

BehavioralPredisposition to action

IntentionsBehavioral expectations

Attitude Scales: Scaling DefinedThe term scaling refers to procedures for attempting to determine quantitative

measures of subjective and sometimes abstract concepts. It is defined as a procedure for the assignment of numbers to a property of

objects in order to impart some of the characteristics of numbers to the properties in question.

Diunduh dari: ………….. 23/8/2012

Unidimensional Scaling

Multidimensional Scaling

Procedures designed to measure only one

attribute of a respondent or object

Procedures designed to measure several dimensions of a

respondent or object

PROSES MENGUKUR ATTITUDE• Ranking• Rating• Sorting• Choice

Diunduh dari: http://en.wikipedia.org/wiki/Ranking ………….. 24/8/2012

A ranking is a relationship between a set of items such that, for any two items, the first is either 'ranked higher than', 'ranked lower than' or 'ranked equal to' the second.

In mathematics, this is known as a weak order or total preorder of objects. It is not necessarily a total order of objects because two different objects can have the same ranking.

The rankings themselves are totally ordered. For example, materials are totally preordered by hardness, while degrees of hardness are totally ordered.

By reducing detailed measures to a sequence of ordinal numbers, rankings make it possible to evaluate complex information according to certain criteria. Thus, for example, an Internet search engine may rank the pages it finds according to an estimation of their relevance, making it possible for the user quickly to select

the pages they are likely to want to see.

Analysis of data obtained by ranking commonly requires non-parametric statistics.In statistics, "ranking" refers to the data transformation in which numerical or ordinal values are replaced by

their rank when the data are sorted. For example, the numerical data 3.4, 5.1, 2.6, 7.3 are observed, the ranks of these data items would be 2, 3, 1 and 4 respectively.

For example, the ordinal data hot, cold, warm would be replaced by 3, 1, 2. In these examples, the ranks are assigned to values in ascending order. (In some other cases, descending ranks are used.) Ranks are related to the indexed list of order statistics, which consists of the original dataset rearranged into ascending order.

Some kinds of statistical tests employ calculations based on ranks:Friedman test

Kruskal-Wallis testRank products

Spearman's rank correlation coefficientWilcoxon rank-sum test

Wilcoxon signed-rank test.Ranks can sometimes have non-integer values for tied data values. Thus, in one way of treating tied data values, when there is

an even number of copies of the same data value, the statistical rank (being the median rank of the tied data) can end in ½.

Types of attitude scales• Simple attitude scales• Most basic form – respondent responds to a single question• Do not allow for fine distinctions or placement on continua

– You are at a company party and are feeling nervous, but you are obligated to be there. Do you:__ find someone you know to buddy up with__ take it as an opportunity to meet new people

What type of scale would these data create?

Diunduh dari: http://famusoa.net/mpowers/trandp/docs/14%20Attitude%20and%20Rating%20Scales%20by%20Sommer.pdf ………….. 24/8/2012

Attitude Scales

An attitude scale is a special type of questionnaire designed to produce scores indicating the intensity and direction (for or against) of a person's feelings about an object or event. There are several types of scales that can be

constructed, but the most common is the Likert -type. The scale is constructed so that all its questions concern a single issue.

Attitude scales are often used in attitude change experiments. One group of people is asked to fill out the scale twice, once before some event, such as reading a persuasive argument, and again afterward. A control group fills out the scale twice without reading the argument. The control group is used to measure exposure or practice effects. The change in the scores of the experimental group relative to the control group, whether their attitudes have become

more or less favorable, indicates the effects of the argument.

Likert-type Scale

A Likert -type scale, named for Rensis Likert (1932) who developed this type of attitude

measurement, presents a list of statements on an issue to which the respondent indicates degree of agreement using categories such as :

Strongly Agree, Agree, Undecided, Disagree, and Strongly Disagree.

• Category scales– More sensitive; provides more information– Overall, how satisfied are you with the high speed performance of

your Mercedes:__ very satisfied__ somewhat satisfied__ neither satisfied nor dissatisfied__ somewhat dissatisfied__ very dissatisfiedIf you could choose, how long would each term be?___26 weeks __ 13 weeks __ 6 weeks ___4 weeks


Diunduh dari: http://chemse.oxfordjournals.org/content/1/3/307.abstract………….. 24/8/2012

CATEGORY SCALES

Diunduh dari: http://chemse.oxfordjournals.org/content/1/3/307.abstract………….. 24/8/2012

CATEGORY SCALES

RATIO SCALES AND CATEGORY SCALES OF ODOUR INTENSITYJ. R. PIGGOTT and R. HARPER.

Chem. Senses (1975) 1 (3): 307-316. doi: 10.1093/chemse/1.3.307

The relation between a ratio scale obtained by magnitude estimation and a category scale of the odour intensity of 1-butanol was studied, together with individual variations in the ratio scale. Series of solutions of butanol in water in small bottles were presented to a panel for judgement, half using

the method of magnitude estimation, the other half a category scale. Plots were made of the category scale against the ratio scale, and the ratio scales of individual members of the panel were

analysed.

A power function exponent of 0.48 was found for the panel's ratio scale, with individual values ranging from 0.25 to 0.49.

The category scale was curved relative to the ratio scale; variability of the magnitude estimates was approximately proportional to the magnitude estimates; and a small time-order error was found.

Odour intensity exhibits the three tested characteristics of a prothetic continuum, and the variability of individual exponents was not as great as sometimes suggested.

• Summated rating scales – the Likert scale– Respondents indicate their attitudes by checking how strongly they agree or disagree with statements

– Chocolate chip cookies are my preferred variety of cookieStrongly disagree Disagree Uncertain Agree Strongly Agree (1) (2) (3) (4) (5)


Diunduh dari: http://iris.lib.neu.edu/psych_fac_pubs/15/………….. 24/8/2012

Summated rating scales – the Likert scale

Ratio scales, category scales, and variability in the production of loudness and softness.Bruce Schneider, and Harlan Lane.

J. Acoust. Soc. Am. Volume 35, Issue 12, pp. 1953-1961 (December 1963).

Several studies have shown that category scales are nonlinearly related to ratio scales of subjective magnitude. A variability model has been proposed previously to account for this departure from linearity.

This article examines the model in the light of the empirical relations that enter into it: the ratio scale of subjective magnitude, the corresponding category scale, and the variability of judgments in both physical and psychological units.

These relations are determined, through repeated measurement with a single observer, for the psychological continuum, loudness, and its inverse, softness. The ratio scales are shown to be reciprocals, and the category scales complements. The category scale of softness is more

concave downward, relative to its magnitude scale, than is the category scale of loudness.

This outcome is also derived mathematically from the empirical equations relating the four scales to physical magnitude.

Variability is found to increase with increasing stimulus magnitude at the same rate for both loudness and softness productions, expressed either in physical units or in psychological units.

Hence, the variability model is found not to accord with the observed difference in concavity between softness and loudness category scales relative to their respective psychological magnitude scales.

Semantic Differential Rating scale– An attitude measure consisting of a series of seven-point bipolar rating scales allowing response to a “concept”Think of your favorite type of cookie. Rate it on each of the following continua:Hard------------------------------------------------------SoftLots of chips---------------------------------------Fewer chipsCrispy---------------------------------------------------chewy


Diunduh dari: http://academic.brooklyn.cuny.edu/economic/friedman/rateratingscales.htm………….. 24/8/2012

SEMANTIC DIFFERENTIAL RATING SCALE

Journal of Marketing Management, Vol. 9:3, Winter 1999, 114-123. ©1999 RATING THE RATING SCALES

Hershey H. Friedman, and Taiwo Amoo

Rating scales are used quite frequently in research, especially in surveys. Typically, an itemized rating scale asks subjects to choose one response category from several arranged in hierarchical order.

Dishonest researchers can, of course, purposefully manipulate the outcome of their research, if they wish, but such biasing may also be totally unintentional.

This paper examines issues involved in creating a relatively unbiased rating scale. These include: (1) Connotations of category labels; (2) Response alternative effects; (3) Implicit assumptions of the question; (4) Forced-choice vs. non-forced-choice rating scales; (5) Unbalanced and balanced rating scales; (6) Order effects; (7) Direction of comparison; (8) Optimal

number of points; (9) Context effects; (10) Rating approach, e.g., improvement needed, performance, comparison to expectations, comparison to ideal, etc.

Numerical Rating scale– Similar to a semantic differential except that it uses numbers as response options

to identify response positions instead of verbal descriptionsThink of your favorite type of cookie. Rate it on each of the following continua:Hard------------------------------------------------------------------------Soft

8 7 6 5 4 3 2 1This scale is called an 8 point numerical scale, why?


Diunduh dari: http://wildpro.twycrosszoo.org/S/00Ref/KeywordsContents/n/Numerical_rating_scale.htm ………….. 24/8/2012

NUMERICAL RATING SCALE

Numerical rating scale“A scale used for the subjective measurement of a clinical sign/syndrome, in which numerical scores are

given (e.g. 0-4). A description is given for each score. The observer chooses, for each individual observed, the number on

the scale which they consider most closely matches that individual.“This system groups information in discrete units, which may place a constraint on the observer.

The NRS can also be used without a descriptor for each score, but is improved by the addition of the descriptions.

http://wildpro.twycrosszoo.org/S/00Ref/JournalContents/j13.htm

Diunduh dari: http://wildpro.twycrosszoo.org/S/00Ref/KeywordsContents/n/Numerical_rating_scale.htm ………….. 24/8/2012

NUMERICAL RATING SCALE

Validation of the numerical rating scale for pain intensity and unpleasantness in pediatric acute postoperative pain: sensitivity to change over time

Pagé, M. Gabrielle; Katz, Joel; Stinson, Jennifer; Isaac, Lisa; Martin-Pichora, Andrea L.; Campbell, Fiona.Journal of Pain, 13(4), 359-369. (2012) Date: 2012.

This study evaluates the construct validity (including sensitivity to change) of the numerical rating scale (NRS) for pain intensity (I) and unpleasantness (U) and participant pain scale preferences in

children/adolescents with acute postoperative pain. Eighty-three children aged 8 to 18 years (mean = 13.8, SD = 2.4) completed 3 pain scales including NRS, Verbal Rating Scale (VRS), and faces scales (Faces Pain Scale-Revised [FPS-R] and Facial Affective Scale

[FAS], respectively) for pain intensity (I) and unpleasantness (U) 48 to 72 hours after major surgery, and the NRS, VRS and Functional Disability Index (FDI) 2 weeks after surgery. As predicted, the NRSI correlated highly with the VRSI and FPS-R and the NRSU correlated highly with the VRSU and FAS 48 to 72 hours after

surgery. The FDI correlated moderately with the NRS at both time points. Scores on the NRSI and NRSU at 48 to 72 hours were significantly higher than at 2 weeks after surgery. Children found the faces scales the easiest to

use while the VRS was liked the least and was the hardest to use. The NRS has adequate evidence of construct validity including sensitivity for both pain intensity and unpleasantness. This study further

supports the validity of the NRS as a tool to measure both intensity and unpleasantness of acute pain in children.

Diunduh dari: http://pi.library.yorku.ca/dspace/handle/10315/14340

Constant Sum Scales– Attributes based on their importance to the person. Respondents are asked to divide a constant sum to indicate the relative importance of

attributes

Example: Suppose the photocopy budget per professor was $100 per month. How much should be allocated to the following. Divide the $100 according to your preference:

____ photocopying for student needs; ____ photocopying for research needs; ____ photocopying for committee needs.====$100 TOTAL

Diunduh dari: http://www.scribd.com/doc/82071910/157/Constant-Sum-Scales ………….. 24/8/2012

CONSTANT SUM SCALES

Constant-Sum ScalesA scale that helps the researcher discover proportions is the constant-sum scale. 1. With this scale, the participant allocates points to more than one attribute or propertyindicant, such that they total a

constant sum, usually 100 or 10.2. In the Exhibit 13-2 example, two categories are presented that must sum to 100.

In the restaurant example, the participant distributes 100 points among four categories to indicate the relative importance of each attribute:

_____ Food Quality _____ Atmosphere _____ Service _____ Price100 TOTAL

3. Up to 10 categories may be used, but both participant precision and patience suffer when toomany stimuli are proportioned and summed.

4. A participant’s ability to add is also taxed in some situations; this is not a responsestrategy that can be effectively used with children or the uneducated.

5. The advantage of the scale is its compatibility with percent (100 percent) and the fact thatalternatives that are perceived to be equal can be so scored—unlike most ranking scales.

6. The scale is used to record attitudes, behavior, and behavioral intent.7. The constant-sum scale produces interval data.

Graphic Rating Scales– An attitude measure consisting of a graphic continuum that allows respondents to rate an object

by choosing any point on the continuum

Diunduh dari: http://www.scribd.com/doc/82071910/157/Constant-Sum-Scales………….. 24/8/2012

GRAPHIC RATING SCALES

GRAPHIC RATING SCALES 1. The graphic rating scale was originally created to enable researchers to discern fine differences.

Theoretically, an infinite number of ratings are possible if participants are sophisticatedenough to differentiate and record them.

2. They are instructed to mark their response at any point along a continuum.Usually, the score is a measure of length (millimeters) from either endpoint.The results are treated as interval data.

3. The difficulty is in coding and analysis; this scale requires more time than scales with predetermined categories. Never __X___________ Always

4. Other graphic rating scales use pictures, icons, or other visuals to communicate with the rater and represent a variety of data types.

5. Graphic scales are often used with children, whose more limited vocabulary prevents the useof scales anchored with words

Rank-Order ScalesScales in which the respondent compares one item with another or a group of items against each other and ranks them.

Diunduh dari: http://changingminds.org/explanations/research/measurement/rank_ordering.htm ………….. 23/8/2012

A Rank Order scale gives the respondent a set of items and asks them to put the items in some form of order.The measure of 'order' can include such as preference, importance, liking, effectiveness and so on.

The order is often a simple ordinal structure (A is higher than B). It can also be done by relative position (A scores 10 whilst B scores 6).

ExamplePlease write a letter next to the four evening activities below to show your preference. Use A for your most preferred

activity, B for the next preferred, then C for the next and then D for the least preferred.__ Staying in and watching television__ Going bowling__ Going out for a meal__ Going to a bar with a friend

DiscussionSorting of ordinal data can be done in several ways:

1. Priority sorting looks for the most important first, then the next most important and so on.2. Block sorting sorts items in to sub groups and then sorts the sub-groups (this is more important, that is less important --

then sort the 'more important' group).3. Score sorting gives an absolute score to each item.4. Pairwise sorting compares pairs of items, moving the more important item higher or giving it a higher score.5. Q-Sorting is done by writing items on cards (Q-cards) and asking the subject to place these in order.6. Swap-sorting uses pairwise comparison on cards or Post-It Notes in a vertical column, swapping each pair in turn until

the whole column is in order.

Rank order items are analyzed using Spearman or Kendall correlation.The Rank Order scale is also known as the Ranking scale.

http://changingminds.org/explanations/research/measurement/types_data.htm

http://changingminds.org/explanations/research/analysis/spearman.htm

http://changingminds.org/explanations/research/analysis/kendall.htm

LIKERT SCALE


The Likert scale is the most frequently used variation of the summated rating scale.Summated rating scales consist of statements that express either a favorable or anunfavorable attitude toward the object of interest.

1. The participant is asked to agree or disagree with each statement.2. Each response is given a numerical score to reflect its degree of attitudinal

favorableness,and the scores may be summed to measure the participant’s overall attitude.

3. Summation is not necessary and in some instances may actually be misleading.

The participant chooses one of five levels of agreement.4. The numbers indicate the value to be assigned to each possible answer, with 1 the

leastfavorable impression of Internet superiority and 5 the most favorable.5. Likert scales also use 7 and 9 scale points.

The Likert scale has many advantages that account for its popularity.6. It is easy and quick to construct.7. It is more reliable and provides more data than many other scales.8. It produces interval data.

LIKERT SCALE


Originally, creating a Likert scale involved a procedure know as item analysis.

•In the first step, a large number of statements were collected that met two criteria:Each statement was relevant to the attitude being studied;Each reflected a favorable or unfavorable position on that attitude.

• People similar to those who are going to be studied were asked to read each statementand to state the level of their agreement with it, using a 5-point scale.

• A scale value of 1 indicated a strongly unfavorable attitude (strongly disagree). Theother intensities were 2 (disagree), 3 (neither agree nor disagree), 4 (agree), and 5(strongly agree), a strongly favorable attitude.

• To ensure consistent results, the assigned numerical values are reversed if thestatement is worded negatively (1 is always strongly unfavorable and 5 is alwaysstrongly favorable).

• Each person’s responses are then added to secure a total score.• The next step is to array these total scores and select some portion representing thehighest

and lowest total scores (generally the top and bottom 10 to 25 percent).• The middle group (50 to 80 percent of participants) are excluded from the

subsequentanalysis.

Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model

EDWARD MOREY, JENNIFER THACHER, and WILLIAM BREFFLEEnvironmental & Resource Economics (2006) 34: 91–115

Diunduh dari: http://www.colorado.edu/economics/morey/papers/MoreyThacherBreffle2006.pdf ………….. 25/8/2012

A latent-class model of environmental preference groups is developed and estimated with only the answers to a set of attitudinal questions.

Economists do not typically use this type of data in estimation. Group membership is latent/unobserved. The intent is to identify and characterize heterogeneity in the preferences for environmental amenities in

terms of a small number of preference groups. The application is to preferences over the fishing characteristics of Green Bay. Anglers answered a number of attitudinal questions, including the importance of boat fees, species catch rates, and fish consumption advisories on site choice.

The results suggest that Green Bay anglers separate into a small number of distinct classes with varying preferences and willingness to pay for a PCB-free Green Bay.

The probability that an angler belongs to each class is estimated as function of observable characteristics of the individual.

Estimation is with the expectation–maximization (E–M) algorithm, a technique new to environmental economics that can be used to do maximum-likelihood estimation with incomplete information.

As explained, a latent-class model estimated with attitudinal data can be melded with a latent-class choice model.

Relating Environmental Ethical Attitudes and Contingent Valuation Responses Using Cluster Analysis, Latent Class Analysis, and the NEP: A Comparison

G. Aldrich, K. Grimsrud, J. THACHER, and M. KotchenSeptember 1, 2005

Diunduh dari: http://www2.bren.ucsb.edu/~kolstad/events/OccWkshp/Aldrich.pdf ………….. 25/8/2012

Environmental ethics and attitudes may be an important source of heterogeneity when considering the welfare e ects and equity implications of policy changes dealing with ff

environment and natural resources.

The New Ecological Paradigm (NEP) Scale is a set of 15 likert questions and is intended to indicate whether an individual holds pro-environmental or anti-environmental beliefs.

This paper provide an overview and comparison of three methodologies that may be applied to NEP survey data to detect environmental ethics groups: total NEP score,

latent class analaysis, and cluster analysis methods.

We find that while environmental attitudes do not significantly a ect average ffwillingness to pay measures, there are significant di erences in willingness to pay ff

across environmental attitude groups.

The willingness to pay estimates for each attitudinal group are consistent across the di erent analystical measures.ff

Environmental and Resource Economics 14: 95–117, 1999.The Validity of Environmental Benefits Transfer: Further Empirical Testing

ROY BROUWER and FRANK A. SPANINKS.1

Diunduh dari: http://www.fnu.zmaw.de/fileadmin/fnu-files/courses/ere4_val/erebouwerspaninks.pdf ………….. 25/8/2012

This paper provides further empirical evidence of the validity of environmental benefits transfer based on CV studies by expanding the analysis to include control factors which have not been accounted for in previous

studies. These factors refer to differences in respondent attitudes.

Questionnaires complying with Dillman’s (1978) ‘total design method’formailsurveys were sent to randomly selected households. Since management agreements in peat meadow areas

usually concentrate on the protection of meadow birds and ditch-side vegetation, these elements received most attention in the questionnaires. Except for some minor differences in wording, both studies used the same

valuation scenarios.

Traditional population characteristics were taken into account, but these variables do not explain why respondents from the same socio-economic group may still hold different beliefs, norms or values and hence

have different attitudes and consequently state different WTP amounts.

The test results are mixed. The function transfer approach is valid in one case, but is rejected in the 3 other cases investigated in this paper. We provide further evidence that in the case of statistically valid benefits

transfer, the function approach results in a more robust benefits transfer than the unit value approach.

We also show that the equality of coefficient estimates is a necessary, but insufficient condition for valid benefit function transfer and discuss the implications for previous and future validity testing.

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[PPT]Slide 1 - Universitas · Web viewMelakukanmanipulasi data dengancaratransformasi “skala” dari ordinal menjadi interval, selainbertujuanuntuktidakmelanggarkelaziman, jugauntukmengubah