Construct Validity

Construct validity is most directly concerned with the question of what the instrument is, in fact, measuring. What construct, concept, or trait underlies the performance or score achieved on the test ? Does the measure of attitude measure attitude or some other underlying characteristic of the individual that affects his on her score ? Construct validity lies at the very heart of scientific progress. Scientific need constructs with which to communicate. Thus, in marketing we speak of people’s sosio economic class, their personality, their attitudes, and so on. These are all constructs that we use as we try to explain marketing behavior. And although vital, They are also unobservable. We can observe behavior related to these constructs, but we cannot observe the construct themselves. Rather, we operationally define the constructs in terms of a set of observables when we agree on the operational definitions, precision in communication is advanced. Instead of saying that what is measured by these it items is the person’s brand loyalty, we can speak of the notion of brand loyalty.

Construct validity seeks agreement between a theoretical concept and a specific measuring device or procedure. For example, a researcher inventing a new IQ test might spend a great deal of time attempting to "define" intelligence in order to reach an acceptable level of construct validity.

In social science and psychometrics, construct validity refers to whether a scale measures or correlates with a theorized psychological construct (such as "fluid intelligence"). It is related to the theoretical ideas behind the personality trait under consideration; a non-existent concept in the physical sense may be suggested as a method of organising how personality can be viewed. The unobservable idea of a unidimensional easier-to-harder dimension must be "constructed" in the words of human language and graphics.

A construct is not restricted to one set of observable indicators or attributes. It is common to a number of sets of indicators. Thus, "construct validity" can be evaluated by statistical methods that show whether or not a common factor can be shown to exist underlying several measurements using different observable indicators. This view of a construct rejects the operationist past that a construct is neither more nor less than the operations used to measure it.

Construct validity is the approximate truth of the conclusion that your operationalization accurately reflects its construct. All of the other terms address this general issue in different ways. A distinction between two broad types: translation validity and criterion-related validity.

In translation validity, focus on whether the operationalization is a good reflection of the construct. This approach is definitional in nature -- it assumes you have a good detailed definition of the construct and that you can check the operationalization against it. In criterion-related validity, examine whether the operationalization behaves the way it should given your theory of the construct. This is a more relational approach to construct validity. it assumes that your operationalization should function in predictable ways in relation to other operationalizations based upon your theory of the construct.

We need to ensure, through the plans and procedures used in constructing the instrument, that we have adequately sampled the domain of the construct and that, there is internal consistency among the items of the domain. The assumption about the internal consistency of a set of items is that “if a set of items is really measuring some underlying trait or attitude, then the underlying trait cause the covariation among the items. The higher correlation, the better the items are measuring the same underlying construct”. We saw that internal consistency was also at issue in determining content validity, and as a matter of fact, negative evidence of content validity of a measure also provides negative evidence about its construct validity. A measure possessing construct validity must be internally consistent insofar as the construct is internally consistent. On the other hand, it is not true that a consistent measure is a construct validity measure. In other words, consistency is a necessary but not sufficient condition for construct validity.

Evaluation of construct validity requires examining the correlation of the measure being evaluated with variables that are known to be related to the construct purportedly measured by the instrument being evaluated or for which there are theoretical grounds for expecting it to be related. Such is consistent with the multitrait-multimethod matrix of examining construct validity described in Campbell and Fiske's landmark paper (1959). Correlations that fit the expected pattern contribute evidence of construct validity. Construct validity is a judgment based on the accumulation of correlations from numerous studies using the instrument being evaluated.



Source:
-. Marketing Research, Methodological Foundations, 5th edition, The Dryden Press International Edition, author Gilbert A. Churchill, Jr.
-. http://www.colostate.edu/
-. www.wikipedia.com
-. http://www.socialresearchmethods.net

Building a Career in Statistics

by David L. Banks, Department of Statistical Science, Duke University
publishing.yudu.com/Library/Auxjn/AmstatNews/resources/3.htm


This article is based on a talk I gave at the JSM 2007 meeting for the ASA Committee and Career Development. But, I should confess at the outset that I have no particular qualifications or any expertise on this topic, aside from having a lot of jobs (which ought to raise questions about my suitability on first place).

Years ago, I was involved in drafting the New Researchers Survival Guide (available at www.imstat.org/publications). Reading over it again, we were awfully earnest and a bit naïve, but I think it has a lot of value for people who are beginning in academic career. So, I refer new faculty members so that, and, in this article, shall focus on topics that apply to everyone, not just recent PhDs and not just academics.

Although statisticians are relatively homogenous in our training, we have the usual range of talents, personalities, and utility functions. This creates many career paths and many ways to be successful. It also means you can be miserable if you get caught on a path that doesn’t fit your personal strengths and values.

All careers have a stochastic component, so we should look to dynamic programming as a model for continual reappraisal of our situations and ways that may better them. This implies a portfolio analysis perspective: We each have different mix of strengths and weakness, and we should try to adaptively invest our energy in combinations that seem most likely to pay off. Some skills that apply to employment in all sectors are the following

1. Technical Strength.
This is the foundation when you are starting out, but it often becomes less important as you advance. Especially in business and government, one needs breadth more than depth at higher levels.

2. Computational Ability.
Anyone who can do solid statistical programming will never miss a meal. It is a blue chip skill and a way of thinking that has a unique value. But, it is hard to become rich or famous on this alone.

3. Public Speaking.
Every member of the ASA has survived at least 1,5 decades of dull lecturer in school, which is why it amazes me that so many of us have not learned enough from that experience to avoid giving bad talks. Good presentation are key of component of almost any success story, and whatever you can do to build strength in this area will repay of your efforts.

4. Writing.
It is crucial to be able to write clearly, correctly, and briefly. This is a lifelong learning process – anyone who writes well is constantly studying how to write and attending to their process.

5. Social Networking.
This is crucially important, and it sometimes statisticians study it more, while learning less, than those in other field. You need diverse networking; having a lot of friends who work on local asymptotic minimaxity is not as helpful as having friends with complementary strengths.

6. Organization.
This sounds mundane, but it is very hard for a manager to promote you if you are sloppy or slow about paperwork. And the discipline of quick turnaround on such items (phone cells, email, appointments, referee report) helps in other aspects one’s career.

7. Time Management.
Don’t waste time feeling guilty about wasting time, just be efficient when you actually get down to work.

Someone else would probably generate a slightly different list, but these are all key areas to cultivate.

For those who need to stick in their job, there are still ways to advance. Personality counts for a lot. Try to pretend to be happy and productive. Read the newspaper so you have a wealth of conversation topics and aren’t stereotypically dull or narrow. You should avoid doomed projects, those that do not build new professional assets and those for which you are not central. I’d recommend looking for projects that cross division boundaries – it helps to have a broad base of good opinion, and you can build unique collaborations the organization needs. Try to differentiate yourself. Think of at least one of idea a week, but be properly skeptical of its value.

Content Validity

Content Validity is based on the extent to which a measurement reflects the specific intended domain of content (Carmines & Zeller, 1991, p.20). In psychometrics, content validity (also known as logical validity) refers to the extent to which a measure represents all facets of a given social construct.

Content validity is illustrated using the following examples: Researchers aim to study mathematical learning and create a survey to test for mathematical skill. If these researchers only tested for multiplication and then drew conclusions from that survey, their study would not show content validity because it excludes other mathematical functions. Although the establishment of content validity for placement-type exams seems relatively straight-forward, the process becomes more complex as it moves into the more abstract domain of socio-cultural studies. For example, a researcher needing to measure an attitude like self-esteem must decide what constitutes a relevant domain of content for that attitude. For socio-cultural studies, content validity forces the researchers to define the very domains they are attempting to study.

Content validity focuses on the adequacy with which the domain of the characteristic is captured by the measure. Content validity is sometimes known as “face validity”assessed by examining the measure with an eye toward ascertaining the domain being sampled. If the included domain is decidedly different from the domain of the variable as conceive, the measure is said to lack content validity.

How can we ensure that our measure will process content validity ?
We can never guarantee it because it is partly a matter of judgment. We may feel quite comfortable with the items included in a measure, for example, while a critic may argue that we have failed to sample from some relevant domain of the characteristic. Although we can never guarantee the content validity of a measure, we can severely diminish the objections of critics. The key to content validity lies in the procedures that are used to developed the instrument.

One widely used method of measuring content validity was developed by C. H. Lawshe. It is essentially a method for gauging agreement among raters or judges regarding how essential a particular item is. Lawshe (1975) proposed that each of the subject matter expert raters (SMEs) on the judging panel respond to the following question for each item: "Is the skill or knowledge measured by this item 'essential,' 'useful, but not essential,' or 'not necessary' to the performance of the construct?" According to Lawshe, if more than half the panelists indicate that an item is essential, that item has at least some content validity. Greater levels of content validity exist as larger numbers of panelists agree that a particular item is essential. Using these assumptions, Lawshe developed a formula termed the content validity ratio:

CVR = (ne - N/2)/(N/2)

CVR= content validity ratio,
ne = number of SME panelists indicating "essential",
N = total number of SME panelists.

This formula yields values which range from +1 to -1; positive values indicate that at least half the SMEs rated the item as essential. The mean CVR across items may be used as an indicator of overall test content validity.

One of the most critical elements in generating a content valid instrument is conceptually defining the domain of the characteristic. The researcher has to specify what the variable is and what it is not. The task of definition is expedited by examining the literature to determine now the variable has been defined and used. Because it is unlikely that all the definitions will agree, the researcher must specify which elements in the definitions underlie his or her use of the term. The researcher needs to be quite careful to include items from all the relevant dimensions of the variable. Again, a literature search may be productive in indicating the various dimensions or strata of a variable. At this stage, the researcher may wish to include items with slightly different shades of meaning, since the original list of items will be refined to produce the final measure.

The collection of items must be large so that after refinement the measure still contains enough items to adequately sample each of the variable’s domain. In the example cited previously, a measure of a sales representative’s job satisfaction would need so include items about each of the components of the job if it is to be content valid. The process of refinement, the essence of which is the internal consistency exhibited by the items within the test, is statistical in nature.


Source:
-. Marketing Research, Methodological Foundations, 5th edition, The Dryden Press International Edition, author Gilbert A. Churchill, Jr.
-. http://www.colostate.edu/
-. Wikipedia.com