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Measurement is an important part of social science 
research. If we take a look at this research  

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process diagram from single demonstrates we can 
see that the two most important decisions after  

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you have decided on your research question is what 
do you sample, what are the units of analysis and  

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what do you measure which means are the variables 
that you study from those units of analysis.

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After that you make your data collection and you 
do your data analysis and report the results.

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The important part is that the quality of 
your study is mostly determined by what do  

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you sample and what do you measure from 
your sample. So when you have your data  

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then the upper limit of the quality 
of the study is basically determined

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If your measurement doesn't work or 
if your sample is somehow flawed then  

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no matter how complicated or how 
sophisticated analysis you apply  

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to those poor data your research 
output will not be very high.

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The idea of measurement is that we want 
to assign numbers to some quantities that  

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we study. For example some things that 
we could study are heights of people,  

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temperature outside, intelligence of 
a person, innovativeness of company.

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The idea of all these quantities is 
that they are variables. The idea  

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that something is a variable means that 
it varies. Some people are taller than  

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others. Sometimes it's colder outside. 
Sometimes it's warmer outside. Sometimes  

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some people are smarter than others. Some 
companies are more innovative than others.

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So the idea is that there's some 
kind of variation in the objects  

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or the units that you study and 
the idea of measurement is that  

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you want to assign some numbers to that 
variation to quantify that variation.

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There are three key questions when you do 
measurement. The first question is where do  

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you get the numbers? So how do we assign the 
height of a person? We can't quantify it. So  

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for height that's obvious we use a measurement 
tape for example. For temperature you use a  

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thermometer but there are different kinds 
of thermometers that you can apply also.

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But how do you quantify things that are not 
physical quantities like innovativeness or  

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intelligence? That's a less straightforward to 
do and there are different ways of doing it.

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The next question is what does the number 
tell you? So if you say that a company's  

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innovativeness is 5. Is it a lot or 
a little? What does it actually mean?  

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So we're talking about the meaning of the 
number and the interpretation of the number.

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Finally how do we justify the way we 
assign the numbers? So we of course  

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are besides just getting the numbers we 
have to convince our readers and ourselves  

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that our numbers are actually valid for 
the purpose that we're using them for.

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There are a couple of different like 
higher-level ways of getting the numbers.  

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Let's look at the research designs by Singleton 
and Straits. They present four research designs.

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The first is a laboratory experiment. The idea of  

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laboratory experiment is that you don't 
actually measure the key variable that  

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you're studying instead you manipulate it. 
So laboratory experiments and experimental  

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studies are more about manipulation 
of things than measurement of things.

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The remaining three are about measurement and they  

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are different approaches of measurement 
and to some extent sampling as well.

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The idea of a survey is that you 
measure things by asking people.  

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So the subjects are provided the numbers. 
If we study people, their intelligence,  

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then we ask them whether they're smart 
or not. And if we study companies we  

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ask people in those companies whether 
the companies are innovative or not.

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We can do it also indirectly by asking whether 
the companies have been successful in producing  

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new products and new services and then we 
have the second category field research.

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The idea of field research is that we 
don't ask the subjects instead we rate  

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or our research assistant rates or 
evaluates the subjects and records  

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what happens based on observation 
and that gives us the numbers.

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Finally we can use numbers collected by 
others so that's the archival records.

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So that's basically how we get the numbers. 
The actual practicalities of how they do that  

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is something that I'll address in other 
video but those are the three main main  

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ways of getting the numbers. Ask the people rate 
yourself. Use data collected by somebody else.

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The next question is what the numbers tell 
us and how do we justify the numbers? To  

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answer those questions we need to understand 
a little bit about measurement theory which  

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relates to how the data and the 
thing being measured are related.

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To understand measurement theory we need 
to understand the concepts of a latent  

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variable. The idea of a latent variable 
is that an observed variable is that we  

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have two types of variables. The observed 
variables are variables for which we have  

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case values. So we have a specific number 
for each individual in our sample. We have  

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a specific number for innovativeness of the 
first company's the second company and so on.

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These are in moral path diagrams. These 
are presented by these squares. Sometimes  

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measurement measured variables are called 
indicators or manifest variables which  

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highlight that their purpose of these 
measured variables is oftentimes to  

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quantify some unmeasurable or unobservable thing.

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Latent variable is another kind of variable. 
The idea of latent variable is simply that it  

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is a variable for which we don't have the 
case value. So we know that there is some  

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variation between companies or between 
people but we cannot specifically assign  

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numbers to any companies. We just 
know that there's some variation on  

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some attribute or some variable but 
we cannot assign the exact numbers.

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We can estimate these numbers and we 
can estimate correlations within latent  

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variables so we can't say what the specific 
values are. So the difference between latent  

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variable and observed variable are important 
when we talk about measurement theory and when  

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we talk about models that allow us to test or 
use or operationalize our measurement theory.

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Then we need to understand a couple 
of other terms as well. We have to  

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understand the difference between 
concept construct and measure.

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The idea of a concept is that it's an abstract 
label for things that we study. And concepts have  

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a reference and often the meaning as well. The 
idea of a reference is that for example if we have  

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a concept of rock that refers to certain objects 
that we call rocks. The idea of meaning is that  

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the concept has some kind of meaning as well. So 
if if we say that we have a rock then people know  

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that well we have something that is a naturally 
occurring. It is a hard object. It's probably size  

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of a couple of fists or so. That's it instead 
of a boulder which is a larger one and so on.

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So the term has some kind of 
attributes or the concept has  

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some kind of attributes that are attached 
to it that give it meaning. Then a concept  

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can also have a definition and the idea 
of a definition is that we have agreed  

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on a specific written definition 
of what exactly the concept means.

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When you read papers that develop theory 
or introduce new constructs then they quite  

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often define the construct explicitly. 
I'll get your constructs in a moment.

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Examples of concepts are persons for 
example and rocks and many other things.  

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So concepts are like abstractions of 
things that we can observe and study.

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Construct is a special kind of a concept. It 
is a conceptual variable. So the idea is that  

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it can vary so people or organizations can have 
different decrease of the cone structure. You  

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can have a different decrease of innovativeness 
different amounts of intelligence and so on.

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So whereas in these concepts they can 
refer generally to just about anything,  

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construct is something that is typically 
quantifiable. And the reason that these are  

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because these are quantifiable we can study 
constructs using quantitative techniques.

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Constructs are also latent in the meaning that 
we cannot assign explicit correct values. We  

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can only observe constructs indirectly. 
Constructs also can have dimensions for  

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example we could have a construct of a person's 
size with the two main dimensions of height and  

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weight of the person. Then some examples are 
intelligence. It could have some dimensions.  

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Innovativeness it could have dimensions. So 
for example how well you are doing in product  

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innovation and how well you are doing a 
service or process innovation and so on.

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Then measure is the third kind of on variable or 
a thing that you need to understand. Measure is an  

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observable variable that quantifies one dimension 
of construct. If you have multiple dimensions in a  

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construct then you need one at least one measure 
for each. So it doesn't make any sense to try to  

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quantify person size using one number. You need 
at least two numbers: the height and weight.

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Examples include IQ test scores so 
that a measure for intelligence and  

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reading on mercury column thermometer 
which is a measure of our temperature.

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How do these construction measures then 
relate? There are two main approaches.

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One is a nominalism. The idea is that 
in nominalism you basically reject the  

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existence of constructs independently 
of measurement. An extreme version of  

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nominalism is operationalism which says 
that the construct is simply whatever the  

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measurement process produces. So the 
construct is defined by the measure.

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And then realism assumes that the constructs exist 
independently of measurement and the purpose of  

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measurement is to discover the true values of the 
construct. Most social science research follows  

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the realist approach. So the idea is that 
there exists something called innovativeness  

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independently of our measurement. Some we can 
say that some companies are more innovative  

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than others without measuring those. So that 
kind of statements make sense if we assume  

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that innovativeness or intelligence exist 
independently of our measurement attempts.

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Then how we actually apply these concepts 
in practices is that we use the measures as  

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proxies for the constructs. We cannot really 
observe the constructs directly so the next  

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best best thing is that we build some kind of 
statistical presentation based on our data.  

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And for example or we can just use a number as 
such, we can take a sum of multiple numbers or  

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we can build a latent variable model and then use 
the latent variable as a proxy for the construct.

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So we use these empirical representations 
constructed based on our data as proxies  

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for the constructs assuming that the 
implicit representation is a perfect  

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representation of the construct. That is 
of course something that is a hardly ever  

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exactly correct but we have to justify 
that it is a good enough approximation.

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So that's the idea of a proxy. Instead 
of using the construct when we study  

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something we use the measure as 
a stand-in for the construct.

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Summary of these key concepts. We have the 
constructs. Construct is a concept so it's  

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a variable that exists in principle. 
It can have some definition. Almost  

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always has a definition. We can say that 
some companies or some individuals are  

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higher on the construct than others 
and we cannot observe it directly.

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Then observable variables are specific numbers 
for each subject or a case that we have collected  

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somehow. The idea is that these measures are 
- if we take the realist perspective - the  

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idea is that the variation in these measures 
is caused by the variation in the construct.  

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For example our people's IQ score differs 
because their intelligence differs. So the  

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reason why there's variation in the data is that 
there's variation in the construct. Thermometer  

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changes its value because the temperature 
outside is different from one day to another.

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So that's the idea of realist perspective to 
measurement which I will be using in these videos.