Bahan kajian pada MK. Metode Penelitian Interdisiplin Kajian Lingkungan Diabstraksikan oleh: Smno.psl.ppsub.Agst2013

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.

Riaset ilmiah bertumpu pada aplikasi metode metode ilmiah yang didasarkan pada paradigma ilmiah. This research provides scientific information and theories for the explanation of the nature and properties of humans and the whole Universe. It makes practical applications possible.

RISET DASAR

Riset dasar (fundamental atau pure research) mempunyai tujuan utama pengembangan pengetahuan dan pemahaman teoritis mengenai huungan-hubungan di antara variabel. 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. Istilah “basic” atau “fundamental” menyatakan bahwa, melalui teori yang dihasilkannya, riset-dasar menyediakan landasan bagi riset selanjutnya, atau riset terapannya.

METODE RISET

Tujuan dari proses riset adalah menghasilkan pengetahuan baru, yang biasanya mempunyai tiga macam bentuk:

RISET EKSPLORATORI:

which structures and identifies new problems

RISET KOSNTRUKTIF:

which develops solutions to a problem

RISET EMPIRIK:

which tests the feasibility of a solution using empirical evidence

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 Simulation Statistical analysis Statistical surveys Content or Textual Analysis Ethnography

PROSES RISET METODE ILMIAH

Generally, 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 definitions Operational 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.

Hipotesis yang bagus memungkinkan untuk prediksi yang baik, dan di dalam kerangka waktu penelitiannya prediksi tersebut dapat diverifikasi. 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.

Meskipun item-itemnya mungkin saja beragam tergantung pada obyek dan penelitinya, namun konsep-konsep berikut ini merupakan bagian penting dari penelitian historis formal: 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

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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

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TEORI ILMIAH

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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.

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

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KRITERIA ESENSIAL DARI TEORI

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“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: http://en.wikipedia.org/wiki/Scientific_theories ….. 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 study Theori sebagai pengetahuan baru Diunduh dari: www2.le.ac.uk/...research...research...ppt/.../fil... ….. 12/9/2012

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TEORI SEBAGAI PARADIGMA .

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

) Diunduh dari: ….. 12/9/2012

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TEORi sebagai suatu LENS .

Existing theory(s) which seek to explain how aspects of (social) reality ‘work’ ( models ). E.g.

– Models of learning • Behaviourist (

( Anderson ) Skinner Social constructivist ( ); Constructivist ( Piaget ); Vygotsky ); Deep learning

– Models of professional/expertise

development

• Situated learning; Communities of practice (

Lave; Wenger )

– Models of second language acquisition •

Krashen ’s SLA theory; Oxford ’s S2R; Diunduh dari: ….. 12/9/2012

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TEORI SEBAGAI PENGETAHUAN BARU .

1. Adaptasi, revisi atau konfirmasi teori yang ada 2. Menghasilkan teori baru 3. Berhubungan dengan Kerangka Konsep Diunduh dari: ….. 12/9/2012

KERANGKA KONSEP

KK merupakan penyajian visual atau tertulis 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 • Typically developed after literature review • Provides the structure/content for the whole study

based on literature and personal experience

• Revisited at the conclusion of the study. • Riset Kualitatif • Initial framework after literature review • Further developed as participants’ views and issues

are gathered and analysed.

TUJUAN RISET Riset Eksploratori Eksploratori :

1. to find out what is happening, 2. to seek new insights, 3. to ask questions 4. to assess phenomena in a new light

Tiga prinsip untuk melakukan riset Eksploratori:

1. A search of the literature 2. Interviewing ‘experts’ in the subject 3. Conducting focus group interviews Diunduh dari: ….. 12/9/2012

RISET EKSPLANATORI EKSPLANATORI adalah:

1. to establish causal relationships between variables 2. to analyse the quantitative data to prove a relationship 3. to analyse the qualitative data to explain a reason of an issue.

Diunduh dari: ….. 12/9/2012

RISET DESKRIPTIF 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.

Diunduh dari: ….. 12/9/2012

.STRATEGI RISET.

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

RISET QUA

N

TITATIVE

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 Diunduh dari: ….. 12/9/2012

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NUMERICAL DATA

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Absolute Population: France, Germany, and the United Kingdom, 1950-2005 Diunduh dari http://www.dhr.history.vt.edu/modules/eu/intro/evidence_charts.html ….. 12/9/2012

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NUMERICAL DATA

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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.

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

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NUMERICAL SCALE

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Pain Assessment By: Bram Riegel, M.D. Numeric Pain Intensity Scale Diunduh dari: http://www.burnsurvivorsttw.org/articles/painass1.html….. 12/9/2012

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NUMERICAL SCALE

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Long and short numeric scales

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

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NUMERICAL SCALE

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

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NUMERICAL SCALE

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LIKERT SCALE

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

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.

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HUBUNGAN ANTAR PEUBAH : PREDIKSI 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?articleid=882647&show=html ….. 12/9/2012

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

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HUBUNGAN ANTAR PEUBAH : KONFIRMASI 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.

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

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HUBUNGAN ANTAR PEUBAH : UJI HIPOTESIS Investigating latent trait and life course theories as predictors of recidivism among an offender sample

Daniel J O'Connell.

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

Diunduh dari: http://www.sciencedirect.com/science/article/pii/S0047235203000503 ….. 12/9/2012

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HUBUNGAN ANTAR PEUBAH : UJI HIPOTESIS 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.

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

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.

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DESCRIPTIVE RESEARCH

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Used to obtain information concerning the current status of a phenomena. Descriptive Research Steps Purpose of these methods is to describe “what exists” with respect to situational variables.

1. Status descriptive survey 2. Explanatory descriptive studies 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.

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

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Causal-comparative research

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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 retrospect 4. The basic causal-comparative approach involves starting with an effect and seeking possible causes 5. The basic approach starts with cause and investigates its effects on some variable 6. 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 research 9. Causal-comparative studies attempt to identify cause-effect relationships; correlational studies do not 10. Causal-comparative studies typically involve two (or more) groups and one independent variable, whereas correlational studies typically involve two or more variables and one group 11. Causal-comparative studies involve comparison, correlational studies involve relationship

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

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

in dependent variable is manipulated by the researcher.

• In causal comparative research the

in dependent 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 VARIABLES Primary 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

random MUST be

– 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 preexisting according to common 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 in dependent variables.

MANOVA , and multivariate regression , when there are multiple de pendent variables.

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

EXPERIMENTAL RESEARCH The objective, systematic, controlled investigation for the purpose of predicting and controlling phenomena and examining probability and causality among selected variables.

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

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RISET EKSPERIMENT

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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.html

Read more: http://wiki.answers.com/Q/What_is_the_definition_of_experimental_research#ixzz26Ua MgsHm

METODE EKSPERIMEN

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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 Research Experiments 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.

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

EXPERIMENTAL RESEARCH METHODS

Steven M. Ross (The University of Memphis) Gary R. Morrison (Wayne State University) The experimenter’s interest in the effect of environmental change, 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.

Diunduh dari: ….. 12/9/2012

Research Strategy: EXPERIMENT

Experiment: 1. Define a theoretical hypothesis 2. Selection of samples of individuals from the population 3. 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

Research Strategy: EXPERIMENT

Figure 5.2 A classic experiment strategy

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 Designs

It 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).

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

True Experimental Design 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).

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

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

Strengths of Experimental Designs

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

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.

CORRELATIONAL RESEARCH

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.

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

CORRELATIONAL RESEARCH

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: ….. 12/9/2012

CORRELATIONAL RESEARCH

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 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=AHIEtbSOr4WHUjdtaIBisvUJufOzs6y0mg ….. 12/9/2012

CORRELATION ANALYSIS

Correlation analysis measures direction and strength of a 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

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

CORRELATION ANALYSIS

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

CORRELATION ANALYSIS

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

META-ANALYSIS

Meta-analysis is essentially a synthesis of available studies about a topic to arrive at a single summary.

Meta-Analysis

From 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-Analysis

Pearson (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-Analysis

In 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-Analysis

The 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, and 9. Makes it possible to show whether a publication bias exists.

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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-Analysis

Meta-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.

Diunduh dari: ….. 12/9/2012

QUANTITATIVE STUDY 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.

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

QUANTITATIVE STUDY Method

1. 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 Study Describe 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?

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

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 (

manipulated, measured, or selected prior to measuring the outcome or dependent variable (y-value).

x -value) can be Diunduh dari: ….. 12/9/2012

Variables

•

Intervening or moderating variables some variables and are affected by other variables. affect

• 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

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.

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

Research Variables

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.

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

Variables

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

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. Diunduh dari: ….. 12/9/2012

Variable and Atribute

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.

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

Variable and Atribute An example

Age 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.

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Independent Variables

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

Research Questions and Hypotheses The aim is :

1. to determine what the variable); relationship between one thing (an independent variable) and another (dependent is 2. the difference between groups with regard to a variable measure; 3. the degree to which a condition exists.

Diunduh dari: ….. 12/9/2012

Research Questions and Hypotheses

• 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.

Research Questions and Hypotheses

• 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). Diunduh dari: ….. 12/9/2012

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 phenomena 4. Fruitfulness – the prospect that a hypothesis may explain further phenomena in the future 5. Conservatism – the degree of "fit" with existing recognized knowledge-systems.

What is a research question?

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.

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

Steps to developing a research question

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.

5.

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?” Evaluate your question.

Is your research question clear? Is your research question focused?

Is your research question complex?

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

Theoretical Framework

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….. 12/9/2012

Theoretical Framework

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

Theoretical Framework

Barry 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://www.emeraldinsight.com/journals.htm?articleid=1626702&show=html ….. 16/9/2012

Theoretical Framework

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

Theoretical Framework

The theoretical framework strengthens the study in the following ways. 1.

2.

3.

4.

5.

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

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.

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.

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.

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.

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

Theoritical Framework Relationship between green management and environmental training in companies located in Brazil: A theoreticalframework and case studies Adriano 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.

Diunduh dari: http://www.sciencedirect.com/science/article/pii/S0925527312000102 ….. 16/9/2012

Theoretical Framework

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

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.emeraldinsight.com/journals.htm?articleid=1891211&show=html ….. 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. Theoritical Framework In this scenario, a given trajectory produces a given amount of noise annoyance, in function of the emitted aircraft noise. Diunduh dari: http://www.icarus.upc.edu/research/air-transportation ….. 16/9/2012

Modelling instantaneous traffic emission and the influence of traffic speed limits Luc 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.

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

Minga Negash, (2012) IFRS AND ENVIRONMENTAL ACCOUNTING.

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

Diunduh dari: http://www.emeraldinsight.com/journals.htm?articleid=17038597&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/case_studies.htm/case_studies.htm?articleid=1885824&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

Diunduh dari: http://www.emeraldinsight.com/journals.htm?articleid=1959356&show=html….. 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.fao.org/docrep/004/Y1457E/Y1457e10.htm ….. 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.

SAMPLING DESIGN Using a random sampling technique, he focused on cases in the rural and urban parts of the Dharwad district of Karnataka State in India.

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

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

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: 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.

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

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: 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.

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

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.

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

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.

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

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

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 n 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 we would need to sample 129 babies: = [2.575 (1.1)/0.25]2 = 128.3689 or 129.

s

= 1.1, 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 (

population. be sampled

– At approximately

N N < 100), there is little point in sampling and surveys should be sent to the entire

– For population size ≈ 500 50% of the population should – For population size ≈ 1,500, 20% should be sampled

= 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

• S

3 d 2 CAN DO ALL

• Sample Size (n), Statistic (descriptive),

substantive hypothesis

• Data Type (NOIR), Distribution

Determines the type of Test: T-test, chi-square, ANOVA, Pearson, Spearman,

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

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.

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

ANALISIS DATA Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modeling.

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

ANALISIS DATA Data cleaning Data cleaning is an important procedure during which the data are inspected, and erroneous data are—if necessary, preferable, and possible—corrected. Data cleaning can be done during the stage of data entry. If this is done, it is important that no subjective decisions are made. 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.

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

ANALISIS DATA Quality of data 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).

Other initial data quality checks are: 1. Checks on data cleaning: 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. Analysis of missing observations: 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. Analysis of extreme observations: 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. Test for common-method variance.

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ANALISIS DATA Quality of measurements 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.

There are two ways to assess measurement quality: 1. Confirmatory factor analysis 2. Analysis of homogeneity (internal consistency), which gives an indication of the reliability of a measurement instrument. During this analysis, one inspects the variances of the items and the scales.

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ANALISIS DATA Initial transformations 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.

Possible transformations of variables are: 1. Square root transformation (if the distribution differs moderately from normal) 2. Log-transformation (if the distribution differs substantially from normal) 3. Inverse transformation (if the distribution differs severely from normal) 4. Make categorical (ordinal / dichotomous) (if the distribution differs severely from normal, and no transformations help) Diunduh dari: http://en.wikipedia.org/wiki/Data_analysis ….. 16/9/2012

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 variables 2. Scatter plots 3. Correlations 4. Cross-tabulations Diunduh dari: http://en.wikipedia.org/wiki/Data_analysis ….. 16/9/2012

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.

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

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).

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

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).

DRAWING CONCLUSIONS

• 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. confident we are in rejecting a null hypothesis. P -values measure how

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

<

0.05

p

<

0.10 p > 0.10 P-value Moderate evidence against H0 Suggestive evidence against H0 Little or no real evidence against H0

.VALIDITY.

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.

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

INTERNAL VALIDITY

1.

2.

3.

4.

5.

6.

7.

8.

Internal validity is an inductive estimate of the degree to which conclusions about causal relationships can be made (e.g. cause and effect), based on the measures used, the research setting, and the whole research design. Eight kinds of confounding variable can interfere with internal validity (i.e. with the attempt to isolate causal relationships): History, the specific events occurring between the first and second measurements in addition to the experimental variables 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.

Testing, the effects of taking a test upon the scores of a second testing.

Instrumentation, changes in calibration of a measurement tool or changes in the observers or scorers may produce changes in the obtained measurements.

Statistical regression, operating where groups have been selected on the basis of their extreme scores.

Selection, biases resulting from differential selection of respondents for the comparison groups.

Experimental mortality, or differential loss of respondents from the comparison groups.

Selection-maturation interaction, etc. e.g., in multiple-group quasi-experimental designs Diunduh dari: http://en.wikipedia.org/wiki/Validity_%28statistics%29 ….. 12/9/2012

.

EXTERNAL VALIDITY

.

External validity concerns the extent to which the (internally valid) results of a study can be held to be true for other cases, for example to different people, places or times. If the same research study was conducted in those other cases, would it get the same results?

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 posttest 2. 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.

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

WHY SCIENTIFIC VALIDITY?

1. To be ethical, scientific research must be conducted in a methodologically rigorous manner 2. Scientifically significant, good question + bad method and/or conduct = invalid results 3. Invalid research is a waste of resources 4. Exploits people.

Diunduh dari: ….. 12/9/2012

. TO BE ETHICAL

.

1. Method must be valid 2. Practically feasible 3. A clear scientific objective 4. Well designed, accepted principles 5. Sufficiently powered – adequate numbers 6. A plausible data analysis plan 7. Must be executable

Diunduh dari: ….. 12/9/2012

Threats to Internal validity

1. History 2. Maturation 3. Testing 4. Instrumentation 5. Statistical Regression 6. Selection bias 7. Experimental Mortality (Attrition) 8. Diffusion or imitation of treatments 9. Demoralization Diunduh dari: …. http://www.uic.edu/classes/socw/socw560/EXPERMT/tsld011.htm … 13/9/2012

Threats to External validity

External Validity Threats to External Validity 1. This refers to the extent to which we can generalize the findings of a study to settings and populations beyond the study conditions. 2. It deals with the representativeness of the study sample, setting, and procedures.

1. Reactive or interactive effect of testings. 2. Interaction effects of selection biases and any research stimulus. 3. Reaction effects of arrangements. 4. Multiple-treatment interaction (Grinnell, 1997).

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

Threats to validity

John 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

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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). Cormack, D. (1991). Pyranee Books. Creswell, J. W. (2004). Emerson, R. W. (1983).

Leedy, P., & Ormrod, J. E. (2001). Merriam, S. B. (1997). Pearson, K (1904). Simon, M. K. (2006). Business research methods (8th ed.). Boston: Irwin. Team spirit motivation and commitment team leadership and membership, team evaluation . Grand Rapids, MI: Educational research: Planning, conducting, and evaluating quantitative and qualitative research ed.). Columbus, Ohio: Merrill Prentice Hall.

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. Practical research planning and design (8th ed.). New York: Macmillan.

Qualitative research and case study applications in education . San Francisco: Jossey-Bass. Report on certain enteric fever inoculation statistic research project . Dubuque, Iowa: Kendall/Hunt.

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(2nd Dissertation and scholarly research: A practical guide to start and complete your dissertation, thesis, or formal 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). The Scarecrow Press.

Handbook of research methods: A guide for practitioners and Students in the social sciences (2nd. Ed.). New Jersey: Sproull, N. D. (2004). The Scarecrow Press.

Handbook of research methods: A guide for practitioners and Students in the social sciences (3rd Ed.). New Jersey: 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 Dissertation. University of Phoenix.

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