Operasionalisasi Konsep

Operasionalisasi Konsep

Generalized Wheel of Social Science

Empirical Generalizations

Hypotheses Observations

DEDUCTION

INDUCTION

Theories

Constructing a Deductive Theory 1. Specify the topic. 2. Specify the range of phenomena your theory addresses. 3. Identify and specify your major concerns and variables.

Constructing a Deductive Theory 1. Find out what is known about the relationships among the variables. 2. Reason from those propositions to the topic you are interested in.

Research design – operationalization of variables – Chapter 6 in Babbie & Mouton (2001)

• The construction of actual, concrete measurement techniques; the creation of “operations” that will result in the desired measurements. • The development or choice of specific research procedures (operations) that will result in representing the concepts of interest.

Operationalization • turning the research subject (term, issue, process, phenomenon) into variables that can be investigated by empirical data • makes a term concrete • from theory to empirical analysis • good operationalization corresponds to the meaning to the term (within the particular study); measures what one wanted to measure; is measurable

Operationalization • An operational definition is a procedure for classifying, ordering, or quantifying something – Classifying - crowded or not crowded – Ordering - uncrowded, mildly crowded, severely crowded – Quantifying - measure crowdedness in terms of the number of residents per square kilometre

• Focus on questionnaires – other operationalization techniques in section on types of research design

Choices to be made about operationalization • The range of variation – how large should your categories be? – Depends on the purpose of your study – pragmatic considerations (e.g. income)

• Variation between the extremes – how fine are the disctinctions you want to make in your study? • e.g. age – Again, depends on the purpose of your study – (Why research is such a challenging task – very few recipes)

• Single or multiple indicators of variables – Some straightforward, such as gender – But others benefit from multiple indicators

Conceptualization • The process through which we specify what we mean when use particular terms in research is called conceptualization. • The result is called a concept • Concepts have specific and agree-upon meanings.

Conceptualization – = the process of identifying and clarifying concepts; through which we specify what we mean by using certain terms – Indicators indicate the presence or absence of the concept we are studying. These often are multi-dimensional; they have more than one specifiable aspect of facet. E.g. happiness.

Conceptualization – We want to speak of abstract things – “intelligence”; “ability to cope with stress”; “life satisfaction”; “happiness”. – We cannot research these things until we know exactly what they are. – Everyday language often vague and unspecified meanings. Conceptualization is to specify exactly what we mean and don’t mean by the terms we use in our research. – No “true” (final) definitions of “the stuff of life”

Variable, Dimensions and Indicators of Concepts • Dimension : a specific aspect of a concept • Variable: variance of a concept • An Indicator : the presence or absence of the concept • During conceptualization and operationalization, we often specify different indicators to represent different dimensions of a concept.

Operationalization: developing specific research procedures to be used in empirical observations representing those concepts Consider: Range of variation Degree of precision

Operational definitions • Specifying exactly what we are going to observe, and how we will do it. Turn your variable into a directly measurable thing • It is a description of the “operations” that we will undertake to measure a concept

Measurement Process: Conceptualization & Operationalization • Conceptualization conceptual definition • Operationalization operational definition

Four Levels of Measurement • Nominal Measures : differences among categories – Ex: gender, religious affiliation, college major

• Ordinal Measures : categories can be ordered or ranked – Ex: social class, prejudice

• Interval Measures : can specify the distance between categories – Ex: IQ scores

• Ratio Measures : attributes are based on a true zero point – Ex: age, # of times married, length of residence in a given place

VARIABLES • Univariat: satu variabel Kebanyakan mahasiswa UI paling tidak mengunjungi bioskop sekali seminggu Variabel: Frekuensi mengunjungi bioskop • Bivariat: dua variabel Mahasiswa perempuan mengunjungi bioskop lebih sering dibandingkan laki-laki Variabel: 1) jenis kelamin, 2) frekuensi ke bioskop • Multivariat: banyak variabel Diantara mahasiswa yang mengalami depresi, laki-laki lebih sering mengunjungi bioskop dibandingkan perempuan. Tetapi, diantara mahasiswa yang tidak mengalami depresi, perempuan lebih sering mengunjungi bioskop. Variabel: 1)sex, 2) frekuensi ke bioskop, 3) kondisi depresi atau tidak