Diunduh dari: ….. 12/9/2012

Bahan kajian dalam MK Metode Penelitian Interdisiplin dalam Kajian

Lingkungan

QUANTITATIVE RESEARCH METHODE

DIABSTRAKSIKAN OLEH:Smno.psl.ppsub.agst2012

Foto: smno.kampus.ub.nop2012

RISET

Riset adalah aktivitas manusia yang didasarkan atas investigasi intelektual dan bertujuan untuk

menemukan, interpretasi, dan memperbaiki pengetahuan tentang berbagai aspek dunia nyata.

Research can use the scientific method, but need not do so.

Scientific research relies on the application of scientific methods based on scientific

paradigm.

This research provides scientific information and theories for the explanation of the nature

and properties of humans and the whole Universe.

Hasil riset dimungkinkan untuk aplikasi praktis.

RISET DASARBasic research (also called fundamental or pure research) has as

its primary objective the advancement of knowledge and the theoretical understanding of the relations among variables.

It is exploratory and often driven by the researcher’s curiosity, interest, or intuition. It is conducted without any practical end in

mind, although it may have unexpected results pointing to practical applications.

The terms “basic” or “fundamental” indicate that, through theory generation, basic research provides the foundation for

further, sometimes applied research. As there is no guarantee of short-term practical gain, researchers may find it difficult to

obtain funding for basic research. Research is a subset of invention.

METODE RISET

The goal of the research process is to produce new knowledge, which takes three main forms:

Exploratory research, which structures and identifies new problems

RISET KONSTRUKTIF, mengembangkan alternatif solusi untuk menjawab suatu masalah

RISET EMPIRIK, menguji kelayakan alternatif solusi berdasarkan bukti-bukti empirik.

Research can also fall into two distinct types, Primary research and Secondary research.

Research methods used by scholars include:Action research

Cartography Case study

Experience and intuition Experiments Interviews

Mathematical models Participant observation

SimulationStatistical analysis Statistical surveys

Content or Textual Analysis Ethnography

PROSES RISET

METODE ILMIAHGenerally, research is understood to follow a certain structural

process. Though step order may vary depending on the subject matter

and researcher, the following steps are usually part of most formal research, both basic and applied:

Formation of the topic Hypothesis

Conceptual definitionsOperational definitions

Gathering of data Analysis of data

Test, revising of hypothesis Conclusion, iteration if necessary

A common misunderstanding is that by this method a hypothesis can be proven.

Generally a hypothesis is used to make predictions that can be tested by observing the outcome of an

experiment.

If the outcome is inconsistent with the hypothesis, then the hypothesis is rejected.

However, if the outcome is consistent with the hypothesis, the experiment is said to support the

hypothesis.

This careful language is used because researchers recognize that alternative

hypotheses may also be consistent with the observations.

In this sense, a hypothesis can never be proven, but rather only supported by

surviving rounds of scientific testing and, eventually, becoming widely thought of as true (or better, predictive), but this is not the same

as it having been proven.

A useful hypothesis allows prediction and within the accuracy of observation of the time, the prediction will

be verified.

As the accuracy of observation improves with time, the hypothesis may no longer provide an accurate

prediction.

In this case a new hypothesis will arise to challenge the old, and to the extent that the new hypothesis makes more accurate predictions than the old, the new will

supplant it.

METODE HISTORIS

The historical method comprises the techniques and guidelines by which historians use historical sources

and other evidence to research and then to write history.

There are various history guidelines commonly used by historians in their work, under the headings of

external criticism, internal criticism, and synthesis. This includes higher criticism and textual criticism.

Though items may vary depending on the subject matter and researcher, the following concepts are usually part of most formal historical research:

1. Identification of origin date 2. Evidence of localization 3. Recognition of authorship 4. Analysis of data 5. Identification of integrity 6. Attribution of credibility.

ETIMOLOGI

The word research derives from the French recherche, from rechercher, to

search closely where "chercher" means "to search"; its literal meaning is 'to

investigate thoroughly'.

.RISET ILMIAH.Application of scientific method to the investigation of relationships among natural phenomenon, or to solve a

medical or technical problem.Read more:

http://www.businessdictionary.com/definition/scientific-research.html#ixzz26ZZId7bN

Scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous

knowledge. To be termed scientific, a method of inquiry must be based on empirical

and measurable evidence subject to specific principles of reasoning. The Oxford English Dictionary says that scientific method is:

"a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and

experiment, and the formulation, testing, and modification of hypotheses.

Sumber: http://en.wikipedia.org/wiki/Scientific_method

. TEORI ILMIAH .

Diunduh dari: http://en.wikipedia.org/wiki/Scientific_theories ….. 12/9/2012

A scientific theory is "a well-substantiated explanation of some aspect of the natural world, based on a body of facts that

have been repeatedly confirmed through observation and experiment.“

Scientists create scientific theories from hypotheses that have been corroborated through the scientific method, then gather

evidence to test their accuracy.

As with all forms of scientific knowledge, scientific theories are inductive in nature and do not make apodictic

propositions; instead, they aim for predictive and explanatory force.

http://en.wikipedia.org/wiki/Scientific_theories

. KRITERIA ESENSIAL DARI TEORI.

Diunduh dari: http://en.wikipedia.org/wiki/Scientific_theories ….. 12/9/2012

“Teori” harus memenuhi kriteria berikut:

1. It makes falsifiable predictions with consistent accuracy across a broad area of scientific inquiry (such as mechanics).

2. It is well-supported by many independent strands of evidence, rather than a single foundation. This ensures that it is probably a good approximation, if not completely correct.

3. It is consistent with pre-existing theories and other experimental results. (Its predictions may differ slightly from pre-existing theories in cases where they are more accurate than before.)

4. It can be adapted and modified to account for new evidence as it is discovered, thus increasing its predictive capability over time.

5. It is among the most parsimonious explanations, sparing in proposed entities or explanations.

Diunduh dari: www2.le.ac.uk/...research...research...ppt/.../fil... ….. 12/9/2012

TIGA PEMIKIRAN TENTANG “TEORI”

1. That which underpins research design

Teori sebagai paradigma

2. That which may inform our understanding of the phenomenon under investigation

Theori sebagai ‘lensa , cermin’3. That which may emerge from our

studyTheori sebagai pengetahuan baru

. TEORI SEBAGAI PARADIGMA.

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1. Asumsi filosofis yang melandasi realita sosial (ontology)

2. What we accept as valid evidence of that reality (epistemology)

3. The means by which we investigate that context (methodology)

4. The means by which we gather evidence (methods)

. TEORi sebagai suatu LENS.


Teori-teori yang ada, yang mencoba menjelaskan bagaimana bekerjanya aspek-aspek (sosial) dari suatu realita’ (model). :

– Model-model Belajar• Behaviouristik (Skinner); Konstruktivistik

(Piaget); Konstruktivistik Sosial (Vygotsky); Belajar Mendalam learning (Anderson)

– Model-model Profeisonal / Pengembangan Keahlian• Situated learning; Communities of practice

(Lave; Wenger)– Models of second language acquisition

• Krashen’s SLA theory; Oxford’s S2R;

. TEORI SEBAGAI PENGETAHUAN BARU.


1. Adaptasi, revisi atau konfirmasi teori yang telah ada

2. Menghasilkan teori baru

3. Berhubungan dengan Kerangka Konsep

KERANGKA KONSEP

Penyajian tertulis atau visual yang :

“explains either graphically, or in narrative form, the main things to be studied – the key factors, concepts or variables - and the presumed relationship among them”

(Miles and Huberman, 1994)

KERANGKA KONSEP• Riset Kuantitatif

• Dikembanghkan setelah kajian pustaka• Provides the structure/content for the whole

study based on literature and personal experience

• Direvisi pada kesimpulan penelitian.

• Riset Kualitatif• Kerangka konsep awal setelah kajian pustaka• Dikembangkan lebih lanjut setelah

mengumpulkan dan menganalisis pandangan participant dan isu-isu yang relevan.

TUJUAN RISET


Riset EksploratoriEksploratori :1. Menemukan (mengungkapkan) apa yang terjadi,2. Mendapatkan pemahaman baru,3. Menanyakan pertanyaan-pertanyaan4. Menilai fenomena dalam perspektif yang baru.

Tiga prinsip untuk melakukan riset Eksploratori:5. Kajian literatur6. Wawancara ‘experts’ di bidangnya7. Melakukan wawancara kelompok terfokus


EKSPLANATORI adalah:

1. Untuk menentukan hubungan kausalita di antara peubah-peubah

2. Untuk analisis data kuantitatif untuk membuktikan suatu hubungan

3. Untuk analisis data kualitatif untuk menjelaskan alasan-alasan atas suatu isu tertentu.

RISET EKSPLANATORI


Deskriptif adalah:

1. the researcher observes and then describes what was observed.

2. to portray an accurate profile of persons, events or situations.

3. an extension of an exploratory/explanatory research.

RISET DESKRIPTIF

.STRATEGI RISET.

Diunduh dari: icbiec.cau.edu.cn/...research.../Ch%205.ppt ….. 12/9/2012

RISET QUANTITATIVE

The “N” side in the Paradigm War

Marilyn K. Simon, Ph.D.

PARADIGMA KUANTITATIF

“an inquiry into a social or human problem based on testing a theory composed of variables, measured with numbers, and

analyzed with statistical procedures, in order to determine whether the predictive

generalizations of the theory hold true.”(Creswell, J. Research Design: Qualitative and Quantitative Approaches. Sage:

1994.)

"a formal, objective, systematic process in which numerical data are utilized to obtain

information about the world" (Burns & Grove, as cited by Cormack, 1991, p. 140).

NUMERICAL DATA


Numerical means expressed in numbers or relating to numbers.

Numerical data is data measured or identified on a numerical scale.

Numerical data can be analyzed using statistical methods, and results can be displayed using tables, charts,

histograms, and graphs.

Read more: http://wiki.answers.com/Q/Definition_of_numerical_data#ixzz26TzuwVFU

http://wiki.answers.com/Q/Definition_of_numerical_data

. NUMERICAL DATA.

Diunduh dari http://www.dhr.history.vt.edu/modules/eu/intro/evidence_charts.html ….. 12/9/2012

Absolute Population:France, Germany, and the United Kingdom,

1950-2005

. NUMERICAL DATA.

Diunduh dari http://serc.carleton.edu/introgeo/mathstatmodels/index.html ….. 12/9/2012

Statistical Models:

include issues such as statistical characterization

of numerical data, estimating the probabilistic future behavior of a system

based on past behavior, extrapolation or

interpolation of data based on some best-fit, error

estimates of observations, or spectral analysis of data or model generated output.

. NUMERICAL SCALE.

Diunduh dari: http://www.burnsurvivorsttw.org/articles/painass1.html….. 12/9/2012

Pain Assessment By: Bram Riegel, M.D.

Numeric Pain Intensity Scale

. NUMERICAL SCALE.

Diunduh dari http://web-technos.blogspot.com/2012/04/long-and-short-numeric-scales.html ….. 12/9/2012

Long and short numeric scales

. NUMERICAL SCALE.

Diunduh dari http://www.jiscinfonet.ac.uk/InfoKits/risk-management/numeric-scales ….. 12/9/2012

Moving from Qualitative to Quantitive Assessment - Assigning Numeric Scales

At this point you may wish to add a numeric scale and use

some form of traffic light system to break the risks into

groups requiring different response strategies.

This table uses the same linear scale for both axes:

. NUMERICAL SCALE.

Diunduh dari http://photographytraining.tpub.com/14129/css/14129_273.htm ….. 12/9/2012

LIKERT SCALE

KARAKTERISTIK KAJIAN KUANTITATIF

• Quantitative research is about quantifying the relationships between variables. – We measure them, and – construct statistical models to explain

what we observed.• The researcher knows in advance

what he or she is looking for.

• TUJUAN: Prediksi, Kontrol, Konfirmasi, Uji hipothesis.

. HUBUNGAN ANTAR PEUBAH : PREDIKSI

Diunduh dari: http://www.emeraldinsight.com/journals.htm?articleid=882647&show=html ….. 12/9/2012

Ashok Jashapara, (2003) "Cognition, culture and competition: an empirical test of the learning organization", Learning Organization, The, Vol. 10 Iss: 1, pp.31 - 50

Diunduh dari: http://www.emeraldinsight.com/journals.htm/journals.htm?issn=0969-6474&volume=12&issue=4&articleid=1502625&show=html ….. 12/9/2012

Irene M. Herremans, Robert G. Isaac, (2005) "Management planning and control: Supporting knowledge-intensive organizations", Learning Organization, The, Vol. 12

Iss: 4, pp.313 - 329

. HUBUNGAN ANTAR PEUBAH : KONTROL

Diunduh dari: http://www.sciencedirect.com/science/article/pii/S1071581906000772 ….. 12/9/2012

The effects of post-adoption beliefs on the expectation-confirmationmodel for information technology continuance

James Y.L. Thong, Se-Joon Hong, Kar Yan Tam.International Journal of Human-Computer Studies. Volume 64, Issue 9, September 2006, Pages 799–810.

. HUBUNGAN ANTAR PEUBAH : KONFIRMASI

http://www.sciencedirect.com/science/journal/10715819/64/9


Investigating latent trait and life course theories as predictors of recidivism among an offender sampleDaniel J O'Connell.

Journal of Criminal Justice. Volume 31, Issue 5, September–October 2003, Pages 455–467.

. HUBUNGAN ANTAR PEUBAH : UJI HIPOTESIS

http://www.sciencedirect.com/science/journal/00472352/31/5

Diunduh dari: http://www.sciencedirect.com/science/article/pii/S0019850111002379….. 12/9/2012

Value creation and firm sales performance: The mediating roles of strategic account management and relationship perception

Ursula Y. Sullivan, Robert M. Peterson, Vijaykumar Krishnan, Industrial Marketing Management. Volume 41, Issue 1, January 2012, Pages 166–173.

. HUBUNGAN ANTAR PEUBAH : UJI HIPOTESIS

CIRI-CIRI KAJIAN KUANTITATIF• All aspects of the study are carefully

designed before data are collected. • Quantitative research is inclined to

be deductive -- it tests theory. This is in contrast to most qualitative research which tends to be inductive --- it generates theory

• The researcher tends to remain objectively separated from the subject matter.

MAJOR TYPES OF QUANTITATIVE STUDIES

• Descriptive research – Correlational research – Evaluative– Meta Analysis

• Causal-comparative research

• Experimental Research – True Experimental– Quasi-Experimental– Shared with full permission from IDTL

Journal.

DESCRIPTIVE RESEARCH

• Descriptive research involves collecting data in order to test hypotheses or answer questions regarding the participants of the study. Data, which are typically numeric, are collected through surveys, interviews, or through observation.

• In descriptive research, the investigator reports the numerical results for one or more variable(s) on the participants (or unit of analysis) of the study.

. DESCRIPTIVE RESEARCH .

Diunduh dari: http://www.okstate.edu/ag/agedcm4h/academic/aged5980a/5980/desres/DESRES/tsld004.htm ….. 12/9/2012

Used to obtain information concerning the current status of

a phenomena.

Purpose of these methods is to describe “what exists” with

respect to situational variables.

1. Status descriptive survey

2. Explanatory descriptive studies

Descriptive Research Steps

1. Statement of the problem. 2. Identification of information. 3. Selection or development of

data gathering instruments. 4. Identification of target

population and sample. 5. Design of information collection

procedure. 6. Collection of information. 7. Analysis of information. 8. Generalization and/or

predictions.

. Causal-comparative research .

Diunduh dari: ndundam.people.cofc.edu/.../CHAPTER%201... ….. 12/9/2012

1. Causal-comparative research is sometimes treated as a type of descriptive research since it describes conditions that already exist.

2. ausal comparative research attempts to determine reasons, or causes, for the existing condition

3. n causal-comparative or ,ex-post facto, research the researcher attempts to determine the cause, or reason, for preexisting differences in groups of individuals Such research is referred to as ex post facto (Latin for “after the fact”) since both the

effect and the alleged cause have already occurred and must be studied in retrospect4. The basic causal-comparative approach involves starting with an effect and seeking possible

causes5. The basic approach starts with cause and investigates its effects on some variable6. The basic approach is sometimes referred to as retrospective causal-comparative research

(since it starts with effects and investigates causes)7. The variation as prospective causal-comparative research (since it starts with causes and

investigates effects)8. Retrospective causal-comparative studies are far more common in educational research9. Causal-comparative studies attempt to identify cause-effect relationships; correlational

studies do not10. Causal-comparative studies typically involve two (or more) groups and one independent

variable, whereas correlational studies typically involve two or more variables and one group11. Causal-comparative studies involve comparison, correlational studies involve relationship

Causal-Comparative• Causal-comparative research

attempts to establish cause-effect relationships among the variables of the study.

• The attempt is to establish that values of the independent variable have a significant effect on the dependent variable.

Causal-Comparative• This type of research usually involves group

comparisons. The groups in the study make up the values of the independent variable, for example gender (male versus female), preschool attendance versus no preschool attendance, or children with a working mother versus children without a working mother.

• In causal-comparative research the independent variable is not under the researchers control, that is, the researcher can't randomly assign the participants to a gender classification (male or female) or socio-economic class, but has to take the values of the independent variable as they come. The dependent variable in a study is the outcome variable.

Causal-Comparative Research

• The aim of causal-comparative research is to determine the cause of existing differences among groups.

– Whereas correlational research involves collecting data on TWO or more variables on ONE group, causal comparative research involves the collection of data on ONE independent variables for TWO or more groups.

Causal-Comparative Research is Differentiated from Experimental

Reserarch

• In an experiment, the independent variable is manipulated by the researcher.

• In causal comparative research the independent has already occurred.– Examples of independent variables

include socioeconomic status, pre-school history, number of siblings, and so on.

Causal-Comparative Designs:Similarities to Experimental Designs

• Purpose– Trying to determine cause-effect

relation between variables• Designs used

– Single-factor– Two-factor– Multi-factor

• Analysis of data

CAUSE-EFFECT RELATION BETWEEN VARIABLESPrimary cause loop

diagram of basic variables in the

system.

Prediction of China's coal production-environmental

pollution based on a hybrid genetic

algorithm-system dynamics model.

Shiwei Yu, Yi-ming Wei.

Energy Policy.Volume 42, March

2012, Pages 521–529

CAUSE-EFFECT RELATION BETWEEN VARIABLES

Hypothesized model of relationships among variables

Variables Predicting Students’ First

Semester Achievement in a Graduate-Entry Dental School in

Korea

Minkang Kim and Jae Il Lee

Journal of Dental Education April 1, 2007 vol. 71 no. 4

550-556

Causal-Comparative Designs vs Experimental Designs

• Start with effect, then seek causes (retrospective)– Less often start with cause (prospective)

• No manipulation of variables– Cannot be manipulated (SES, race, sex)– Should not be manipulated (# cigarettes

smoked/day)– Were not manipulated (method of reading

instruction)

Causal-Comparative Designs vs Experimental Designs

• Assignment of subjects to groups– In experimental, assignment MUST be

random– In causal-comparative, assignment is

based on preexisting characteristics• Determination of cause is not as

robust– It is more that of a relationship, with a

suggestion of cause

Causal Comparative Research

• Groups…– are classified according to common

preexisting characteristic, and – compared on some other measure

• There is NO – intervention, – manipulation, or – random assignment

Example: What causes lung cancer?• Finding: People with lung cancer

smoke more than people without lung cancer. There are no other differences in lifestyle characteristics between the groups.

• Conclusion: Smoking is a possible cause of lung cancer.

• Caution: A third factor? Proper matching?

Value of Causal Comparative Research

• Uncovers relationships to be investigated experimentally.

• Used to establish cause-effect when experimental design not possible.

• Less expensive and time consuming than experimental research.

• Note: if you conduct a quantitative research study it most likely will be a causal-comparative study.

Strengthening Causal Comparative Designs

• Strong inference (theory). • Time sequence (presumed cause

precedes presumed effect).• Incorporate other, possible, causes in the

design (measure common antecedents) .• Use designs that control for extraneous

causes: – matched group design – Extreme groups design – Statistical control (Analysis of Covariance)

Wide Variety of Statistical Procedures

• t tests, ANOVA, ANCOVA when two or more groups are being compared.

• Regression analysis when there are multiple independent variables.

• MANOVA, and multivariate regression, when there are multiple dependent variables.

• Path analysis and structural equation modeling when the theoretical causal paths are being investigated.

Diunduh dari: http://medical-dictionary.thefreedictionary.com/Experimental+research ….. 12/9/2012

EXPERIMENTAL RESEARCH

The objective, systematic, controlled investigation for the purpose of

predicting and controlling phenomena and examining probability and causality

among selected variables.

. RISET EKSPERIMENT .Experimental research is guided by a hypotheses (or several

hypothesis) that states an expected relationship between two or more variables.

An experiment is conducted to support or disconfirm this experimental hypothesis.

Experimental research, although very demanding of time and resources, often produces the soundest evidence concerning

hypothesized cause-effect relationships (Gay, 1987).

source: http://www.unm.edu/~lkravitz/Article%20folder/understandres.htmlRead more:

http://wiki.answers.com/Q/What_is_the_definition_of_experimental_research#ixzz26UaMgsHm

METODE EKSPERIMEN.

Diunduh dari: http://www.experiment-resources.com/experimental-research.html ….. 12/9/2012

is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls and

measures any change in other variables.

Experimental Research is often used where:There is time priority in a causal relationship (cause precedes effect)

There is consistency in a causal relationship (a cause will always lead to the same effect)

The magnitude of the correlation is great.

Aims of Experimental ResearchExperiments are conducted to be able to predict phenomenons.

Typically, an experiment is constructed to be able to explain some kind of causation. Experimental research is important to society - it helps us

to improve our everyday lives.


EXPERIMENTAL RESEARCH METHODSSteven M. Ross (The University of Memphis)Gary R. Morrison (Wayne State University)

The experimenter’s interest in the effect of environmentalchange, referred to as “treatments,” demanded designs

using standardized procedures to hold all conditions constant except the independent (experimental) variable.

This standardization ensured high internal validity (experimental control) in comparing the experimental group to the control

group on the dependent or “outcome” variable. That is, when internal validity was high, differences between groups could be

confidently attributed to the treatment, thus ruling out rival hypotheses attributing effects to extraneous factors.

Research Strategy: EXPERIMENT

Experiment:

1.Define a theoretical hypothesis2.Selection of samples of individuals from the

population3.Random allocation of samples to different

experimental conditions: the experimental vs. control group

4.Introduction of intervention to one more of the variables

5.Measurement on a small number of dependent variables

6.Control of all other variables

Figure 5.2 A classic experiment strategy

Research Strategy: EXPERIMENT

Diunduh dari: …. http://www.uic.edu/classes/socw/socw560/EXPERMT/tsld011.htm … 13/9/2012

True Experimental Design• Experimental research like causal-comparative

research attempts to establish cause-effect relationship among the groups of participants that make up the independent variable of the study, but in the case of experimental research, the cause (the independent variable) is under the control of the researcher.

• The researcher randomly assigns participants to the groups or conditions that constitute the independent variable of the study and then measures the effect this group membership has on another variable, i.e. the dependent variable of the study.

• There is a control and experimental group, some type of “treatment” and participants are randomly assigned to both: Control Group, manipulation, randomization).

True Experimental Design

Experimental DesignsIt is a controlled method of observation

in which the value of one or more independent variables is changed to

assess its causal effect on one or more dependent variables

(Monette et al., 1994).



Characteristics of a True Experimental Design

1.Time order of variable. 2.Manipulation of the INDEPENDENT

VARIABLE 3.Relationships between variable. 4.Control of rival (alternative) hypothesis. 5.Use of a control group. 6.Random sampling and random

assignment (Grinnell, 1997).



Concepts in Experimental Designs

1. Independent variable (treatment, stimulus, or manipulation)

2. Dependent variable (or outcomes) 3. Pre-testing and post-testing 4. Experimental and control group


Strengths of Experimental Designs1. Control over study

variables. 2. Can ordinarily use

random assignment manipulation of one or more IVs.

3. The isolation of the experimental variable and its impact over time.

4. Replication due to the fact that it requires little time and money.

Weaknesses of Experimental Designs

1. Very artificial 2. Lack external

validity


Quasi-Experimental Design• Quasi-experimental designs provide

alternate means for examining causality in situations which are not conducive to experimental control.

• The designs should control as many threats to validity as possible in situations where at least one of the three elements of true experimental research is lacking (i.e. manipulation, randomization, control group).

CORRELATIONAL RESEARCH

• Correlational research attempts to determine whether and to what degree, a relationship exists between two or more quantifiable (numerical) variables.

• It is important to remember that if there is a significant relationship between two variables it does not follow that one variable causes the other. CORRELATION DOES NOT MEAN CAUSATION.

• When two variables are correlated you can use the relationship to predict the value on one variable for a participant if you know that participant’s value on the other variable.


Diunduh dari: http://medical-dictionary.thefreedictionary.com/Experimental+research ….. 12/9/2012

C.R. = the systematic investigation of relationships among two or more variables,

without necessarily determining cause and effect.

Correlation implies prediction but not causation. The investigator frequently reports the

correlation coefficient, and the p-value to determine strength of the relationship.


The purpose of correlational research is to discover relationships between two or more variables.

Relationship means that an individuals status on one variable tends to reflect his or her status on the other.

Helps us understand related events, conditions, and behaviors.– Is there a relationship between educational levels of farmers

and crop yields?

• To make predictions of how one variable might predict another– Can high school grades be used to predict college grades?


Diunduh dari: https://docs.google.com/viewer?a=v&q=cache:_sYy8ZKbC6kJ:www.cals.ncsu.edu/agexed/aee578/correlations.ppt+CORRELATIONAL+research&hl=id&gl=id&pid=bl&srcid=ADGEEShcf1idh81Qn_UFfWbH6K7Tr4iSCX9IkCVbEqYL27maNShULYXNpyu0hJpRFPVtT6zbVrhrHNm_XP4gW008I3M9Fuyy3sOL7T_QDAS_ZV_Rjqgm2Kpw6sfLzlMPrCXY2kABxq8H&sig=AHIEtbSOr4WHUjdtaIBisvUJufOzs6y

0mg ….. 12/9/2012

Process

• Variables to be study are identified• Questions and/or hypotheses are stated• A sample is selected (a minimum of 30 is needed)• Data are collected• Correlations are calculated• Results are reported


source: http://hosting.soonet.ca/eliris/remotesensing/LectureImages/correlation.gif Diunduh dari: http://www2.cedarcrest.edu/academic/bio/hale/biostat/session24links/correlation.html….. 12/9/2012

Correlation analysis

measuresdirection and strength of a relationship

CORRELATION ANALYSIS

With correlation analysis, the relationship

may be a causal relationship

(independent and dependent variable) or a non-causal relationship (variable 1 and variable

2).

Classic Example of a Non-Causal Relationship


source: http://hosting.soonet.ca/eliris/remotesensing/LectureImages/correlation.gif Diunduh dari: http://www2.cedarcrest.edu/academic/bio/hale/biostat/session24links/correlation.html….. 12/9/2012

Diunduh dari: http://b.vimeocdn.com/ts/670/072/67007239_640.jpg ….. 12/9/2012


Cross-correlation analysis between serum biochemical indices and metabolic risk factors of T2DM. Only significant correlations are highlighted and numbered. For each significant correlation, Pearson's correlation

coefficients (r), p-values and sample sizes are shown in parentheses. Arora et al. BMC Medical Genetics 2011 12:95 doi:10.1186/1471-2350-12-95


http://www.biomedcentral.com/1471-2350/12/95/figure/F1

META-ANALYSIS

Meta-analysis is essentially a synthesis of available studies

about a topic to arrive at a single summary.

Meta-AnalysisFrom data that is after the fact that has occurred naturally (no interference from the researcher), a

hypothesis of possible future correlation is drawn. Correlation studies are not cause and effect, they simply prove a correlation or not

(Simon & Francis, 2001).

Meta-analysis combines the results of several studies that address a set of related research

hypotheses. "The first meta-analysis was performed by Karl Pearson in 1904, in an attempt

to overcome the problem of reduced statistical power in studies with small sample sizes;

analyzing the results from a group of studies can allow more accurate data analysis" (Wekipedia.,

2006.

Meta-AnalysisPearson (1904) reviewed evidence on the effects of a

vaccine against typhoid. – Pearson gathered data from eleven relevant studies of

immunity and mortality among soldiers serving in various parts of the British Empire.

– Pearson calculated statistics showing the association between the frequency of vaccination and typhoid for each of the eleven studies, and then synthesized the statistics, thus producing statistical averages based on combining information from the separate studies.

– Begins with a systematic process of identifying similar studies. – After identifying the studies, define the ones you want to keep

for the meta-analysis. This will help another researcher faced with the same body of literature applying the same criteria to find and work with the same studies.

– Then structured formats are used to key in information taken from the selected studies.

– Finally, combine the data to arrive at a summary estimate of the effect, it’s 95% confidence interval, and a test of homogeneity of the studies.

Meta-Analysis• Begins with a systematic process of identifying

similar studies. • After identifying the studies, define the ones

you want to keep for the meta-analysis. This will help another researcher faced with the same body of literature applying the same criteria to find and work with the same studies.

• Then structured formats are used to key in information taken from the selected studies.

• Finally, combine the data to arrive at a summary estimate of the effect, it’s 95% confidence interval, and a test of homogeneity of the studies.

Meta-AnalysisIn statistics, a meta-analysis refers to methods focused on contrasting

and combining results from different studies, in the hope of identifying patterns among study results, sources of disagreement

among those results, or other interesting relationships that may come to light in the context of multiple studies.

In its simplest form, this is normally by identification of a common measure of effect size, of which a weighted average might be the

output of a meta-analysis.

The general aim of a meta-analysis is to more powerfully estimate the true effect size as opposed to a less precise effect size derived in a

single study under a given single set of assumptions and conditions.

Diunduh dari: http://en.wikipedia.org/wiki/Meta-analysis ….. 12/9/2012

Advantages of Meta-AnalysisThe advantages of meta-analysis (e.g. over classical literature reviews,

simple overall means of effect sizes etc.) are that it:1. Shows whether the results are more varied than what is expected from the

sample diversity,2. Allows derivation and statistical testing of overall factors and effect-size

parameters in related studies,3. Is a generalization to the population of studies,4. Is able to control for between-study variation,5. Includes moderators to explain variation,6. Has higher statistical power to detect an effect than in 'n=1 sized study

sample',7. Deals with information overload: the high number of articles published each

year,8. Combines several studies and will therefore be less influenced by local

findings than single studies will be, and9. Makes it possible to show whether a publication bias exists.

Diunduh dari: http://en.wikipedia.org/wiki/Meta-analysis ….. 12/9/2012

Meta-Analysis

Meta Analisis merupakan metode yang digunakan untuk menganalisis gagasan, ide, bahasa, asal usul,

asumsi, model, dan signifikansi dalam analisis kebijakan publik.

Proses meta-analisis kebijakan publik diawali dengan memahami makna dan gagasan tentang publik. Istilah

“publik” dimaknai sebagai aktivitas manusia yang dipandang perlu untuk diatur atau diintervensi oleh pemerintah atau aturan sosial, atau setidaknya oleh

tindakan bersama.Diunduh dari: ….. 12/9/2012

Meta-AnalysisMeta-analisis merupakan suatu teknik statistika yang

menggabungkan dua atau lebih penelitian sejenis sehingga diperoleh paduan data secara kuantitatif. Meta-analisis merupakan suatu studi observasional

retrospektif, dalam artian peneliti membuat rekapitulasi data tanpa melakukan manipulasi eksperimental.

Meta analysis tidak fokus pada kesimpulan yang didapat pada berbagai studi, melainkan fokus pada data; seperti

melakukan operasi pada variabel- variabel, besarnya ukuran efek, dan ukuran sampel.

Diunduh dari: http://catatananakkuliah.blogspot.com/2010/03/meta-analisis-dan-isu-kebijakan-publik.html ….. 12/9/2012

Should I do a Quantitative Study?

• Problem definition is the first step in any research study.

• Rather than fitting a technique to a problem, we allow the potential solutions to a problem determine the best methodology to use.

• Problem drives methodology…most of the time.


QUANTITATIVE STUDY

Diunduh dari: http://answers.ask.com/Science/Psychology/what_is_a_quantitative_study ….. 12/9/2012

Quantitative studies require the researcher to measure variables, such as time, treatment, and

weight; and analyse the relationships among variables using statistics.

The study can be either descriptive, which simple measures things as they are; or experimental,

where there is an attempt to change or otherwise affect the subjects of the study to observe the

outcome.

QUANTITATIVE STUDY

Diunduh dari: edweb.sdsu.edu/.../QUANT.d... - Amerika Serikat ….. 12/9/2012

Method1. Research design & procedures

What is the research design? Experimental? Quasi-experimental? Descriptive? Ex post facto? How will the study be conducted?

2. Sample, Population, or Subjects Describe the sample: Who are the subjects? How are they to be selected? What are important characteristics of the sample population?

3. Variables in the StudyDescribe both the dependent and independent variables in the study

4. Instrumentation and Materials How will each variable be measured? What measurement instruments will be used?What materials

5. Data Analysis What statistical treatments of the data will be carried out?

Variables• A variable, as opposed to a constant, is

anything that can vary, or be expressed as more than one value, or is in various values or categories (Simon, 2006).

• Quantitative designs have at least two types of variables: independent and dependent (Creswell, 2004).

• independent variable (x-value) can be manipulated, measured, or selected prior to measuring the outcome or dependent variable (y-value).


Variables• Intervening or moderating variables

affect some variables and are affected by other variables.

• They influence the outcome or results and should be controlled as much as possible through statistical tests and included in the design (Sproull, 1995).

• (ANCOVA) may be used to statistically control for extraneous variables. This approach adjusts for group differences on the moderating variable (called a covariate) that existed before the start of the experiment. Diunduh dari: ….. 12/9/2012

Variables

Diunduh dari: https://onlinecourses.science.psu.edu/stat100/node/6 ….. 12/9/2012

Data is a collection of a number of pieces of

information. Each specific piece of information is called

an observation.

The observations are measurements of certain

characteristics which we call "variables".

The word “variable” is used because the pieces of

information, the observations, vary from one

person to the next.

Research Variables

Diunduh dari: http://www.experiment-resources.com/research-variables.html….. 12/9/2012

Any factor that can take on different values is a scientific variable and influences the outcome of experimental research.

Most scientific experiments measure quantifiable factors, such as time or weight, but this is not essential for a component to be classed as a variable.

Gender, color and country are all perfectly acceptable variables, because they are inherently changeable.

As an example, most of us have filled in surveys where a researcher asks questions and asks you to rate answers. These responses generally have a

numerical range, from ‘1 - Strongly Agree’ through to ‘5 - Strongly Disagree’. This type of measurement allows opinions to be statistically

analyzed and evaluated.

Variables

Diunduh dari: http://changingminds.org/explanations/research/measurement/variables.htm….. 12/9/2012

1. A variable is something that can change, such as 'gender' and are typically the focus of a study.

2. Attributes are sub-values of a variable, such as 'male' and 'female'. An exhaustive list contains all possible answers, for example gender could also include 'male transgender' and 'female transgender' (and both can be pre- or post-operative).

3. Mutually exclusive attributes are those that cannot occur at the same time. Thus in a survey a person may be requested to select one answer from a list of alternatives (as opposed to selecting as many that might apply).

4. Quantitative data is numeric. This is useful for mathematical and statistical analysis that leads to a predictive formula.

5. Qualitative data is based on human judgement. You can turn qualitative data into quantitative data, for example by counting the proportion of people who hold a particular qualitative viewpoint.

6. Units are the ways that variables are classified. These include: individuals, groups, social interactions and objects.

Variables


Types

1. Descriptive variables are those that which will be reported on, without relating them to anything in particular.

2. Categorical variables result from a selection from categories, such as 'agree' and 'disagree'. Nominal and ordinal variables are categorical.

3. Numeric variables give a number, such as age.4. Discrete variables are numeric variables that come from a limited set of

numbers. They may result from , answering questions such as 'how many', 'how often', etc.

5. Continuous variables are numeric variables that can take any value, such as weight.

Variable and Atribute

Diunduh dari: http://en.wikipedia.org/wiki/Variable_and_attribute_%28research%29 ….. 12/9/2012

In science and research, attribute is a characteristic of an object (person, thing, etc.).

While an attribute is often intuitive, the variable is the operationalized way in which the attribute is represented for further data processing.

In data processing data are often represented by a combination of items (objects organized in rows), and multiple variables (organized in columns).Values of each variable statistically "vary" (or are distributed) across the

variable's domain.Domain is a set of all possible values that a variable is allowed to have. The values are ordered in a logical way and must be defined for each variable.

Domains can be bigger or smaller. The smallest possible domains have those variables that can only have two values, also called binary (or

dichotomous) variables. Bigger domains have non-dichotomous variables and the ones with a higher level of measurement.

Variable and Atribute

Diunduh dari: http://en.wikipedia.org/wiki/Variable_and_attribute_%28research%29….. 12/9/2012

An exampleAge is an attribute that can be operationalized in many ways. It can be dichotomized so that only two values - "old" and "young" - are allowed

for further data processing.

In this case the attribute "age" is operationalized as a binary variable. If more than two values are possible and they can be ordered, the attribute is

represented by ordinal variable, such as "young", "middle age", and "old". Next it can be made of rational values, such as 1, 2, 3.... 99

The "social class" attribute can be operationalized in similar ways as age, including "lower", "middle" and "upper class" and each class could be differentiated between upper and lower, transforming thus changing the

three attributes into six or it could use different terminology.

Independent Variables

Diunduh dari: http://www.everythingbio.com/glos/definition.php?word=independent+variable ….. 12/9/2012

An independent variable is a factor that can be varied or manipulated in an experiment (e.g. time, temperature, concentration, etc). It is

usually what will affect the dependent variable.

There are two types of independent variable, which are often treated differently in statistical analyses:

1. Quantitative variables that differ in amounts or scale and can be ordered (e.g. weight, temperature, time).

2. Qualitative variables which differ in "types" and can not be ordered (e.g. gender, species, method).

By convention when graphing data, the independent variable is plotted along the horizontal X-axis with the dependent variable on

the vertical Y-axis

Research Questions and Hypotheses

The aim is :1. to determine what the relationship is

between one thing (an independent variable) and another (dependent variable);

2. the difference between groups with regard to a variable measure;

3. the degree to which a condition exists.



• Although a research question may contain more than one independent and dependent variable, each hypothesis can contain only one of each type of variable. There must be a way to measure each type of variable.

• A correctly formulated hypotheses, should answer the following questions:

1. What variables am I, the researcher, manipulating, or is responsible for a situation? How can this be measured?

2. What results do I expect? How can this be measured? 3. Why do I expect these results? The rationale for these

expectations should be made explicit in the light of the review of the literature and personal experience.

This helps form the conceptual or theoretical framework for the study.


• A hypothesis is a logical supposition, a reasonable guess, or an educated conjecture. It provides a tentative explanation for a phenomenon under investigation.

• Research hypothesis are never proved or disproved. They are supported or not supported by the data.

• If the data run contrary to a particular hypothesis, the researcher rejects that hypothesis and turns to an alternative as being a more likely explanations of the phenomenon in question, (Leedy & Ormrod, 2001).


SCIENTIFIC HYPOTHESIS

According to Schick and Vaughn (2002), researchers weighing up alternative hypotheses may take into consideration:

1. Test-ability (compare falsifiability)2. Parsimony (as in the application of "Occam's razor",

discouraging the postulation of excessive numbers of entities)3. Scope – the apparent application of the hypothesis to multiple cases

of phenomena4. Fruitfulness – the prospect that a hypothesis may explain further

phenomena in the future5. Conservatism – the degree of "fit" with existing recognized

knowledge-systems.

What is a research question?

Diunduh dari: http://writingcenter.gmu.edu/?p=307 ….. 12/9/2012

A research question is a clear, focused, concise, complex and arguable question around which you center your research.

You should ask a question about an issue that you are genuinely curious about.

Research questions help writers focus their research by providing a path through the research and writing process.

The specificity of a well-developed research question helps writers avoid the “all-about” paper and work toward

supporting a specific, arguable thesis.

Steps to developing a research question

Diunduh dari: http://writingcenter.gmu.edu/?p=307….. 12/9/2012

1. Choose an interesting general topic. Even directed academic research should focus on a topic in which the writer is at least somewhat personally invested. Writers should choose a broad topic about which they genuinely would like to know more.

2. Do some preliminary research on your general topic. Do a few quick searches in current periodicals and journals on your topic to see what’s already been done and to help you narrow your focus. What questions does this early research raise?

3. Consider your audience. For most college papers, your audience will be academic, but always keep your audience in mind when narrowing your topic and developing your question. Would that particular audience be interested in this question?

4. Start asking questions. Taking into consideration all of the above, start asking yourself open-ended “how” and “why” questions about your general topic. For example, “How did the slave trade evolve in the 1850s in the American South?” or “Why were slave narratives effective tools in working toward the abolishment of slavery?”

5. Evaluate your question.Is your research question clear? Is your research question focused? Is your research question complex?

Theoretical Framework

Diunduh dari: http://www.analytictech.com/mb313/elements.htm….. 12/9/2012

A theoretical framework is a collection of interrelated concepts, like a theory but not necessarily so well worked-out.

A theoretical framework guides your research, determining what things you will measure, and what statistical relationships you will

look for.

Theoretical frameworks are obviously critical in deductive, theory-testing sorts of studies.

In those kinds of studies, the theoretical framework must be very specific and well-thought out.

Diunduh dari: http://www.analytictech.com/mb313/elements.htm ….. 16/9/2012

Theoretical FrameworkThe independent variables, also

known as the predictor or explanatory variables, are the factors

that you think explain variation in the dependent variable.

In other words, these are the causes.

For example, you may think that people are more satisfied with their

jobs if they are given a lot of freedom to do what they want, and if they are

well-paid. So 'job freedom' and 'salary' are the

independent variables, and 'job satisfaction' is the dependent

variable.


Theoretical FrameworkBarry P. Haynes, (2007) "Office

productivity: a theoretical framework", Journal of Corporate Real Estate, Vol. 9

Iss: 2, pp.97 – 110.

Validated theoretical framework of office productivity.

A model can be developed to represent the concept of office productivity with

the dimensions of physical environment and behavioural environment.

It can therefore be concluded that a validated model has been developed, and in light of this study's research

findings, the theoretical framework for office productivity can be redefined.

Diunduh dari: http://libguides.usc.edu/content.php?pid=83009&sid=618409….. 16/9/2012


A theoretical framework consists of concepts, together with their definitions, and existing theory/theories that are used for your particular study.

The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that will relate it

to the broader fields of knowledge in the class you are taking.

The theoretical framework is not something that is found readily available in the literature.

You must review course readings and pertinent research literature for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and

explanatory power.

Diunduh dari: http://www.analytictech.com/mb313/elements.htm ….. 16/9/2012

Theoretical FrameworkThe theoretical framework strengthens the study in the following

ways.

1. An explicit statement of theoretical assumptions permits the reader to evaluate them critically.

2. The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.

3. Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to move from simply describing a phenomenon observed to generalizing about various aspects of that phenomenon.

4. Having a theory helps you to identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest. It alerts you to examine how those key variables might differ and under what circumstances.

5. By virtue of its application nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges of a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.


Relationship between green management and

environmental training in companies located in

Brazil: A theoreticalframework

and case studiesAdriano Alves Teixeira , Charbel José Chiappetta Jabbour , Ana Beatriz Lopes de Sousa Jabbour.

International Journal of Production Economics. Volume 140, Issue 1, November 2012, Pages 318–329.

Theoreticalframework relating green

management and environmental training.

Theoritical Framework

http://www.sciencedirect.com/science/article/pii/S0925527312000102

Diunduh dari: http://www.sciencedirect.com/science/article/pii/S0261517712000076….. 16/9/2012


The effect of environmentally

friendly perceptions on festival visitors’ decision-making process using an

extended model of goal-directed behavior

Hak Jun Song, Choong-Ki Lee, Soo K. Kang, Sug-jin Boo.

Tourism Management. Volume 33, Issue 6, December 2012,

Pages 1417–1428.

A proposed research model.

http://www.sciencedirect.com/science/article/pii/S0261517712000076


Poh Lean Chuah, Wai Peng Wong, T.

Ramayah, M. Jantan, (2010).

Organizational context, supplier

management practices and

supplier performance: A case

study of a multinational company in Malaysia.

Journal of Enterprise Information Management, Vol.

23 Iss: 6, pp.724 – 758.

Diunduh dari: http://www.icarus.upc.edu/research/air-transportation ….. 16/9/2012

Trajectory management for

aircraft noise annoyance

minimisation

We have developed an optimisation tool to compute

noise annoyance optimal trajectories for specific

scenarios. The involved airport, with

its surrounding cartography, geography and

meteorological data, define an scenario.

In this scenario, a given trajectory produces a given amount of noise annoyance,

in function of the emitted aircraft noise.


Diunduh dari: http://www.sciencedirect.com/science/article/pii/S004896970600636X ….. 16/9/2012

Modelling instantaneous traffic emission and the influence of traffic speed

limitsLuc Int Panis, Steven Broekx,

Ronghui Liu.Science of The Total

Environment. Volume 371, Issues 1–3, 1 December 2006,

Pages 270–285.

The proposed model framework to analyze the

relationship between transport policy, traffic

network conditions, vehicle emissions and urban air pollution.



Minga Negash, (2012) IFRS AND ENVIRONMENTAL ACCOUNTING.

Management Research Review. Vol. 35 Iss: 7, pp.577 – 601.

Diunduh dari: http://www.emeraldinsight.com/case_studies.htm/case_studies.htm?articleid=1885824&show=html ….. 16/9/2012

Aldónio Ferreira, Carly Moulang, Bayu Hendro.Environmental management accounting and innovation: an exploratory

analysis. Emerald 23, (2010)

Diunduh dari: http://www.emeraldinsight.com/journals.htm?articleid=1959356&show=html….. 16/9/2012

Guy Assaker, Vincenzo Esposito Vinzi, Peter O'Connor, (2011).Modeling a causality network for tourism development: an empirical

analysis.Journal of Modelling in Management, Vol. 6 Iss: 3, pp.258 - 278

SAMPLING DESIGN

Diunduh dari: http://www.fao.org/docrep/004/Y1457E/Y1457e10.htm ….. 16/9/2012

Typologi of sampling design

There are many different sampling designs, each with

advantages and disadvantages for

assessment of different products.

Figure provides a typology of sampling

designs.

SAMPLING DESIGN

Diunduh dari: http://www.idf-finance.in/Sangapor_profile.html ….. 16/9/2012

Analysis of the Performance of MFIs: A

Case Study of the Initiatives for

Development Foundation - Financial Service

Private Limited (IDF- FSPL),

Bangaluru/Dharwad.

Using a random sampling technique, he focused on

cases in the rural and urban parts of the Dharwad district of Karnataka State in India.

SAMPLING DESIGN

Diunduh dari: http://www.hsrmethods.org/Glossary/Terms/C/Cluster%20Sampling.aspx….. 16/9/2012

Cluster Sampling

A sampling strategy where the population of interest is divided into representative "clusters" of individuals, among whom a random selection of subjects is

drawn. Cluster sampling is often

conducted when it is impossible or impractical to

draw a simple random sample or stratified sample

because the researcher cannot get a complete list of members of the population.

SAMPLING PROCESS

Diunduh dari: http://www.surveyonics.com/SurveyCourseware/Survey-Design-Methodology.aspx ….. 16/9/2012

Choosing a representative sample from a population is a multistep process that ensures the information received is useful. In the sampling process, the

following steps must be conducted:

Defining the population

In this step, a population is defined for surveying. If an organization is interested in the purchasing behaviors of college students in a particular city,

then all students in that city are considered a population. For some survey studies, the population is simply defined as the consumers (e.g.

Internet users or mall shoppers). However, marketing strategies focus on specific demographics to survey in a population. If the manufacturer of

specialty rugs is interested only in the buying preferences of upper middle class residents, people who make a certain amount of money and above (e.g. $250K per year) would be the population. A clear definition of a population

is important for the accuracy of the remainder of the steps.

SAMPLING PROCESSChoosing a representative sample from a population is a multistep process that

ensures the information received is useful. In the sampling process, the following steps must be conducted:

Developing a sampling frame A sampling frame provides a source or a listing of all elements

or individuals within a population. In the example of the specialty rugs manufacturer, a sampling frame of upper

middle class individuals could be public records that show tax and income figures. Since those records reflect all high income earners in one city, they

are considered the sampling frame for the survey study. In sales and marketing, a sampling frame is not as easy to obtain as customer

lists may not be available. For many organizations, sampling frames are usually previous customers’ lists

or those purchased from other companies.


SAMPLING PROCESS

Choosing a representative sample from a population is a multistep process that ensures the information received is useful. In the sampling process, the

following steps must be conducted:

Determining sample size

Once a sampling frame is identified, a sample size is determined.

The size of a chosen sample depends on a number of factors: the number of questions in the survey, the type of questions, and the

purpose of the survey.

Sample sizes can range from 30 to several hundred depending on the availability of time and cost.


SAMPLING PROCESS

Specifying sample method

This final step in the sampling process determines the sampling methodology.

For instance, a survey may require only answers from

experts in a field. Another survey that is informal

may be given to any customers that frequent a

business without regard for the population from which it

is drawn.


SAMPLING METHODOLOGY


SAMPLING METHODOLOGY

In the final step of the sampling process, a

particular methodology is chosen and applied.

This methodology depends on the type of

sample that is surveyed. Samples are divided in probability and non-probability samples

Sample Size• Note: We can use the following formula to determine the

sample size necessary to discover the “true” mean value from a population.

• where zа/2 corresponds to a confidence level (found on a table or computer program). Some common values are 1.645 or 1.96, which might reflect a 95% confidence level (depending on the statistical hypothesis under investigation), and 2.33, which could reflect a 99% confidence level in a one-tailed test and 2.575 for a two-tailed test s is the standard deviation, and E is the margin of error.

• Example: If we need to be 99% confident that we are within 0.25 lbs of a true mean weight of babies in an infant care facility, and s = 1.1, we would need to sample 129 babies:

• n = [2.575 (1.1)/0.25]2 = 128.3689 or 129.Diunduh dari: ….. 12/9/2012

Sample Size –sigma unknown

In most studies, 5% sampling error is

acceptable.

Sample Size• Gay (1996, p. 125) suggested general

rules similar to Suskie’s for determining the sample size. – For small populations (N < 100), there is little point

in sampling and surveys should be sent to the entire population.

– For population size ≈ 500 50% of the population should be sampled

– For population size ≈ 1,500, 20% should be sampled

– At approximately N = 5,000 and beyond, the population size is almost irrelevant and a sample size of 400 is adequate. Thus, the larger the population, the smaller the percentage needed to get a representative sample.

Other Considerations in Selecting a sample

• Characteristics of the sample. Larger samples are needed for heterogeneous populations; smaller samples are needed for homogeneous populations (Leedy & Ormrod, 2001).

• Cost of the study. A minimum number of participants is needed to produce valid results.

• Statistical power needed. Larger samples yield greater the statistical power. In experimental research, power analysis is used to determine sample size (requires calculations involving statistical significance, desired power, and the effect size).

Other Considerations in Selecting a sample

• Confidence level desired (reflects accuracy of sample; Babbie, 2001)

• Purpose of the study. Merriam (1998) stated, "Selecting the sample is dependent upon the research problem“ .

• Availability of the sample. Convenience samples are used when only the individuals that are convenient to pick are chosen for the sample. It is sometimes known as a location sample as individuals might be chosen from just one area.

Analisis Data• S3d2 CAN DO ALL • Sample Size (n), Statistic

(descriptive), substantive hypothesis

• Data Type (NOIR), DistributionDetermines the type of Test:T-test, chi-square, ANOVA, Pearson,

Spearman,

ANALISIS DATA

Diunduh dari: http://en.wikipedia.org/wiki/Data_analysis ….. 16/9/2012

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting

useful information, suggesting conclusions, and supporting decision making.

Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in

different business, science, and social science domains.Data mining is a particular data analysis technique that

focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes.

Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information.

ANALISIS DATA


In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA),

and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA

on confirming or falsifying existing hypotheses.

Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification,

while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from

textual sources, a species of unstructured data.

ANALISIS DATA


Data integration is a precursor to data analysis, and data analysis is closely linked to data

visualization and data dissemination.

Istilah “data analysis” kadangkala juga dimaknai atau diartikan sebagai “pemodelan

data”.

ANALISIS DATA: Membersihkan Data

Diunduh dari: http://en.wikipedia.org/wiki/Data_analysis….. 16/9/2012

“Membersihkan data” merupakan prosedur memeriksa (menelaah) data, mengoreksi data (kalau diperlukan, dianggap lebih baik dan memungkinkan). Kegiatan ini dapat dilakukan selama fase “Entry

Data”. Kalau hal ini dilakukan diharapkan tidak akan terjadi kesimpulan subyektif.

The guiding principle is: during subsequent manipulations of the data, information should always be cumulatively retrievable.

In other words, it should always be possible to undo any data set alterations. Therefore, it is important not to throw information away at

any stage in the data cleaning phase. All information should be saved (i.e., when altering variables, both the original values and the new values should be kept, either in a duplicate

data set or under a different variable name), and all alterations to the data set should carefully and clearly documented, for instance in a syntax or a

log.

ANALISIS DATA: KUALITAS DATA

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The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analyses: frequency counts, descriptive statistics (mean,

standard deviation, median), normality (skewness, kurtosis, frequency histograms, normal probability plots), associations (correlations, scatter plots).

Kontrol Kualitas Data:1. Pembersihan data: have decisions influenced the distribution of the variables? The

distribution of the variables before data cleaning is compared to the distribution of the variables after data cleaning to see whether data cleaning has had unwanted effects on the data.

2. ANALISIS OBSERVASI YANG HILANG: are there many missing values, and are the values missing at random? The missing observations in the data are analyzed to see whether more than 25% of the values are missing, whether they are missing at random (MAR), and whether some form of imputation is needed.

3. ANALISIS OBSERVASI EKSTRIM: outlying observations in the data are analyzed to see if they seem to disturb the distribution.

4. Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.

5. UJI RAGAM UMUM.

ANALISIS DATA: KUALITAS PENGUKURAN


The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or

research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the

literature.

Ada dua cara untuk menilai kualitas pengukuran :1. Analisis Faktor Konfirmatori2. Analisis Homogenitas (Konsistensi Internal), yang memberikan

indikasi keterandalan (reliabilitas) instrumen pengukuran. Selama analisis ini, peneliti memeriksa ragam dan skala datanya.

ANALISIS DATA: TRANSFORMASI AWAL


After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial

transformations of one or more variables, although this can also be done during the main analysis phase.

Transformasi Peubah :1. Transformasi akar kuadrat (Kalau distribusinya agak menyimpang

dari distribusi normal)2. Transformasi Log (Kalau distribusinya jauh menyimpang dari

Normal)3. Transformasi Inverse (Kalau distribusinya sama-sekali berbeda dengan

Normal)4. Make categorical (ordinal / dichotomous) (if the distribution differs

severely from normal, and no transformations help)

ANALISIS DATA


Characteristics of data sample

In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine

the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis

phase.

The characteristics of the data sample can be assessed by looking at:1. Basic statistics of important variables2. Scatter plots3. Correlations4. Cross-tabulations

ANALISIS DATA


Exploratory and confirmatory approaches

In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In

a confirmatory analysis clear hypotheses about the data are tested.Exploratory data analysis should be interpreted carefully.

An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then

following up that analysis with a comfirmatory analysis in the same dataset could simply mean that the results of the comfirmatory analysis

are due to the same type 1 error that resulted in the exploratory model in the first place.

The comfirmatory analysis therefore will not be more informative than the original exploratory analysis.

ANALISIS DATA


Many statistical methods have been used for statistical analyses.

A very brief list of four of the more popular methods is:1. General linear model: A widely used model on which various

statistical methods are based (e.g. t test, ANOVA, ANCOVA, MANOVA). Usable for assessing the effect of several predictors on one or more continuous dependent variables.

2. Generalized linear model: An extension of the general linear model for discrete dependent variables.

3. Structural equation modelling: Usable for assessing latent structures from measured manifest variables.

4. Item response theory: Models for (mostly) assessing one latent variable from several binary measured variables (e.g. an exam).

http://en.wikipedia.org/wiki/T_test

http://en.wikipedia.org/wiki/ANOVA

http://en.wikipedia.org/wiki/ANCOVA

CAN-DO-ALL

• Hypothesis testing is a method of testing claims made about populations by using a sample (subset) from that population.– Like checking out a carefully selected hand

full of M&Ms to determine the makeup of a Jumbo Size bag.

• After data are collected, they are used to produce various statistical numbers such as means, standard deviations, and percentages.

CAN-DO-ALL• These descriptive numbers summarize or

describe the important characteristics of a known set of data.

• In hypothesis testing, descriptive numbers are standardized (Test Values) so that they can be compared to fixed values (found in tables or in computer programs) (Critical Values) that indicate how unusual it is to obtain the data collected.

• Once data are standardized and significance determined, we can make inferences about an entire population (universe).

PEMBUATAN KESIMPULAN

• A p-value (or probability value) is the probability of getting a value of the sample test statistic that is at least as extreme as the one found from the sample data, assuming the null hypothesis is true.

• Traditionally, statisticians used alpha (а) values that set up a dichotomy: reject/fail to reject null hypothesis. P-values measure how confident we are in rejecting a null hypothesis.

IMPORTANT NOTE• Note: If the null hypothesis is not rejected, this

does not lead to the conclusion that no association or differences exist, but instead that the analysis did not detect any association or difference between the variables or groups.

• Failing to reject the null hypothesis is comparable to a finding of not guilty in a trial. The defendant is not declared innocent. Instead, there is not enough evidence to be convincing beyond a reasonable doubt. In the judicial system, a decision is made and the defendant is set free.

Interpretation

p < 0.01 P-value

p < 0.05 Moderate evidence against H0

p < 0.10 Suggestive evidence against H0

p > 0.10 Little or no real evidence against H0

.VALIDITY.

Diunduh dari: http://en.wikipedia.org/wiki/Validity_%28statistics%29 ….. 12/9/2012

In statistics, validity has no single agreed definition but generally refers to the extent to which a concept, conclusion or measurement is well-

founded and corresponds accurately to the real world.

The word "valid" is derived from the Latin “validus, meaning strong”.

The validity of a measurement tool (for example, a test in education) is considered to be the degree to which the tool measures what it claims to

measure.

In the area of scientific research design and experimentation, validity refers to whether a study is able to scientifically answer the questions it

is intended to answer.

VALIDITAS INTERNAL

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Internal Validity merupakan estimasi induktif tentang derajat sampai dimana kesimpulan hubungan kausalita dapat dibuat (misalnya sebab dan akibat), berdasarkan atas sarana yang digunakan, prosedurnya, dan disain risetnya.

Ada delapan macam peubah yang dapat mengganggu validitas internal:1. History, the specific events occurring between the first and second measurements in

addition to the experimental variables2. Maturation, processes within the participants as a function of the passage of time (not

specific to particular events), e.g., growing older, hungrier, more tired, and so on.3. Testing, the effects of taking a test upon the scores of a second testing.4. Instrumentation, changes in calibration of a measurement tool or changes in the

observers or scorers may produce changes in the obtained measurements.5. Statistical regression, operating where groups have been selected on the basis of their

extreme scores.6. Selection, biases resulting from differential selection of respondents for the comparison

groups.7. Experimental mortality, or differential loss of respondents from the comparison groups.8. Selection-maturation interaction, etc. e.g., in multiple-group quasi-experimental designs.

http://en.wikipedia.org/wiki/Internal_validity

http://en.wikipedia.org/wiki/Internal_validity

. VALIDITAS EKSTERNAL.

Diunduh dari: http://en.wikipedia.org/wiki/Validity_%28statistics%29….. 12/9/2012

External validity berkenaan dnegan sampai-dimana hasil-hasil penelitian (yang secara internal valid) dapat berlaku pada kondisi / kasus yang

berbeda, misalnya orang yang berbeda, tempat yang berbeda atau waktu yang berbeda.

Apakah kalau riset yang sama dilakukan pada kasus-kasus yang lain , akan menghasilkan hasil-hasil yang sama?

A major factor in this is whether the study sample (e.g. the research participants) are representative of the general population along relevant

dimensions. Other factors jeopardizing external validity are:1. Reactive or interaction effect of testing, a pretest might increase the scores

on a posttest2. Interaction effects of selection biases and the experimental variable.3. Reactive effects of experimental arrangements, which would preclude

generalization about the effect of the experimental variable upon persons being exposed to it in non-experimental settings

4. Multiple-treatment interference, where effects of earlier treatments are not erasable.

http://en.wikipedia.org/wiki/External_validity

MENGAPA VALIDITAS ILMIAH ?


1. Agar supaya etis, riset ilmiah harus dilaksanakan dnegan metode yang valid dan bagus

2. Secara ilmiah signifikan (penting), permasalahan yang bagus + metode yang jelek = hasil tidak valid

3. Riset yang tidak valid merupakan “sampah”4. Eksploitasi manusia.

AGAR SUPAYA ETIS .


1. Metode harus valid2. Layak secara praktikal3. Tujuan ilmiahnya jelas4. Disain penelitian bagus dan benar5. Sufficiently powered – adequate

numbers6. Analisis datanya bagus7. Dapat dilaksanakan

Threats to Internal validity1.History 2.Maturation 3.Testing 4.Instrumentation 5.Statistical Regression6.Selection bias 7.Experimental Mortality

(Attrition) 8.Diffusion or imitation of

treatments 9.Demoralization


ANCAMAN VALIDITAS EKSTERNAL

Validitas Eksternal

1. Sampai dimana kita dapat men-generalisir hasil-hasil penelitian pada kondisi yang berbeda dnegan kondisi penelitian.

2. VE ini berkaitan dengan kualitas sampel, disain dan prosedur penelitian.

Ancaman Validitas Eksternal

1. Efek reaktif atau interaktif dai uji-uji.

2. Interaction effects of selection biases and any research stimulus.

3. Efek-efek reaksi dari disain penelitian.

4. Interaksi ganda antar perlakuan (Grinnell, 1997).


ANCAMAN VALIDITASJohn Henry Effect:

A tendency of people in a control group to take the experimental situation as a challenge and exert more effort than they otherwise would;

they try to beat the experimental group. This negates the whole purpose of a control group. So called because this was discovered

at the John Henry Company where a new power tool was being tested to see if it could

improve productivity. The workers using the old tool took it as a challenge to work harder to show they were just as good and should get the new tool.

References• Best, J. W. & Kahn, J. V. (1993). Research in education (7th ed.). Boston: Allyn and Bacon.• • Babbi, E. (2001). The practice of social research. Australia: Wadsworth Thomson Learning. • Burns, n., & Grove, K. (1993). The practice of nursing research: Conduct, critique and utilization (2nd ed.). Philadelphia:

Saunders. • • Cooper, D. R., & Schindler, P.S. (2002). Business research methods (8th ed.). Boston: Irwin. • Cormack, D. (1991). Team spirit motivation and commitment team leadership and membership, team evaluation. Grand

Rapids, MI: Pyranee Books. • Creswell, J. W. (2004). Educational research: Planning, conducting, and evaluating quantitative and qualitative research

(2nd ed.). Columbus, Ohio: Merrill Prentice Hall.• Emerson, R. W. (1983).New England Reformers', lecture to the Society, 3 March 1844• Gay. L. R. (1996). Educational research: Competencies for analysis and application (4th ed.). Beverly Hills, CA: Sage. • Leedy, P., & Ormrod, J. E. (2001). Practical research planning and design (8th ed.). New York: Macmillan.• Merriam, S. B. (1997). Qualitative research and case study applications in education. San Francisco: Jossey-Bass. • Pearson, K (1904). Report on certain enteric fever inoculation statistics. BMJ 3:1243-1246.• Simon, M. K. (2006). Dissertation and scholarly research: A practical guide to start and complete your dissertation, thesis,

or formal research project. Dubuque, Iowa: Kendall/Hunt.•

Simon, M. K., & Francis, B. J. (2001). The dissertation cookbook: From soup to nuts a practical guide to start and complete your dissertation (3rd. Ed.). Dubuque, Iowa: Kendall/Hunt.

•Sproull, N. D. (1995). Handbook of research methods: A guide for practitioners and Students in the social sciences (2nd. Ed.). New Jersey: The Scarecrow Press.

•Sproull, N. D. (2004). Handbook of research methods: A guide for practitioners and Students in the social sciences (3rd Ed.). New Jersey: The Scarecrow Press.

•Suskie, L. (1996). Survey Research: What works. Washington D.C.: International Research.

• Wekipedia. (2005). Meta-analysis. Retreived January 5, 2006, from, http://www.answers.com/topic/meta-analysis• Yakoobian, V. (2005). Successful leadership styles of elementary school principals and parent-teach organization leaders.

Doctoral Dissertation. University of Phoenix.• Shared with full permission from IDTL Journal – copyright IDTL, University of Phoenix, Dr. Marilyn Simon, and Dr. Kimberly

Blum

Foto: smno.kampus.ub.agst2012

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