Reading:  Babbie, Chapter Five, Conceptualization and Measurement

Class on Friday Feb 15 will be online.  Sign on to SAKAI and click on the Methods class and then click on "Chat Room".  If you are on campus, you can sign on at any campus computer lab.  You can also sign on from home or anyplace on the internet.  Have the Babbie book available and have the SDA software open in another window of your computer.  Go to the SDA Frequencies/Crosstabulation program.   If you miss this class, you can read the transcript of the chat at any time.


Class notes:  The Powerpoint shown in class this week is available in SAKAI.

Here are some key terms.  If you need definitions, check the book:
conceptualization - Thinking through what our conceptions mean, defining them.
dimensions of a concept
indicators of a concept
operationalization
operational definitions
interchangeability of indicators
reliability
  -  test-retest method
  -  split-half method
  -  internal consistency method (best for questionnaires with lots of items, Cronbach's alpha can be used or item-whole correlations
validity
 -  face validity
 -  convergent validity (very similar to internal-consistency tests of reliability)
 -  discriminant validity -  indicatoers of each dimension of the concept should correlate more than indicators of other dimensions
 -  criterion (or predictive) validity is best if you have a good criterion
 -  content validity
 -  construct validity, this is the most subtle and difficult to understand.  An example:  a study of UFO Abduction Status.


  Conceptualization means thinking through what we mean by the words we use (concepts are expressed with words).  Some are fairly obvious, e.g., suicide.  Others are not at all, e.g., race or poverty.  Is there a difference between "sex" and "gender"   Babbie distinguished between real definitions (which he does not believe in, but some philosophers do, e.g, Plato) , nominal definitions, operational definitions.  Babbie follows a nominalist philosophy as opposed to realist, concepts are things we make up, not things that really "exist".  This is a useful way to approach empirical research.

 An example:  the measurement of romantic love. defined three dimensions affiliative and dependent need, a predisposition to help, and an orientation of exclusiveness and absorption.

A particularly controversial topic has been the concept of "intelligence"  What does this mean?  Is it one thing or does it have multiple dimensions:  Example:     Multiple Intelligence

Operationalizing the Concepts.  A lot of effort goes into this.  Quantitative  research means you have to measure your variables and a lot depends on having good measurement.  Sometimes this is difficult, e.g., measuring "intelligence" or "liberalism-conservatism" or "mental illness" or "crime rates (various kinds)".  Often we use standard measures created by the government agencies that collect statistics.

The point of conceptualization and operationalization is to measure things.  This means putting things into categories that correspond to attributes of variables, e.g., men and women, upper class, middle class, lower class (or UU, LU, UM, LM, UL, LL),  annual income $37,541.23.   The critical thing in social research is how we do this and what our measures apply.  We can understand this in terms of

Levels of Measurement

The first and most important question is:   is the measure continuous or categorical?   This is important because continuous variables are required for the use of statistics such as the mean, standard deviation, correlation and regression.  With continuous measurement we have precise distances between the items measured, with categorical we just have them sorted into discrete categories.

If a variable is continuous, we can ask whether it is "interval" or "ratio".    Both of these have precise distance measurement between points.  In addition, ratio measures have a logically meaningful zero point.  With ratio measures, we can talk about ratios between variables, e.g., say that $50 is twice as much money as $25.   With interval variables, such as fahrenheit temperatures, we cannot make such statement.

If a variable is categorical, we can ask whether it is "dichotomous,"  "nominal" or "ordinal"

Dichotomous variables have only two categories.  These can be two natural categories such as "male' and "female"  or they can be artificial "dummy" variables, such as:   are you a Catholic or not;.  With dichotomies you can use regression and correlation.

Nominal variables have more than two categories, but not in any order or with a measured distance between them.  We can do percentages and chi-square significance tests with nominal variables.
Nominal Measurement.  Categories that could be put in any order.
      Catholic, Protestant, Jewish, Moslem, LDS, Buddhist, Episcopalian, Baptist
                       variable one, category of religion, variable two denomination.
            Mental illnesses (DSMIV) e.g.,  adjustment disorder, borderline personality disorder, paranoid schizophrenic
               Crimes:   burglary, assault, murder.  What do these terms mean?  Look at the US Criminal Code.

  Each individual should go into one and only one category on a variable, one value on a variable.   For example:  What is your favorite food, we have a long list, but each person is allowed only one.

 Sorting people into categories must be as reliable and accurate or valid as possible.  One of the things we do is evaluate how accurate our measurement is. 

Ordinal variables have the categories in a logical order  (from "lower" to "higher").  We can compute a median and a range.

Ordinal Measurement.   Here we have categories in a logical order.       Very short, short, medium, very tall, tall .  Often we take continuous variables and make them ordinal.    Income:   Under $20,000   $20 to 40,000  $40 to 60,000   $60000 plus.

Interval Measurement:   TEMPERATURE IN FAHRENHEIT OR CENTIGRADE, 0 degrees is not the absence of heat.  How about the day that the "temperature doubled" in New York City?

Ratio Measurement:    Income in dollars:  a continous numerical value PLUS a meaningful zero point.  Height in inches.

In answering questions about measurement, give the highest or best level of measurement that is justified.  Any variable that meets the criteria for a ratio variable also meets the criteria for an interval variable, but the criteria for a ratio variable are more stringent so we would say that it is ratio measurement.  Any ordinal variable also meets the criteria for a nominal variable, but if it meets the criteria for ordinal we say it is ordinal.

It is important to understand that many variables can be measured at different levels.  Thus I could take height and put it into categories such as short, medium, tall in which case I would be using ordinal measurement because they are in order.  I could also measure it in inches or centimeters, which would be ratio measurement.  It is also important to understand that each of the statistics is appropriate for variables measured in some ways but not others.  Doing percentages and cross-tabulations makes sense for nominal or ordinal data. Chisquare is for nominal or ordinal data. Doing correlation or regression or means and standard deviations requires interval or ratio data.  We can make a broad distinction between categorical (nominal or ordinal) or continuous (ratio or interval) data.  The dichotomy is a special case because we can use correlation and regression with dichotomies, but we can also do percentages, cross tabulations and chisquares.

Quality of Measurement   -   Reliability and Validity. 
 
Reliability -  you get the same thing over and over.  Consistency.
         inter-rater - two different raters get the same answer.
         test-retest, if you take it twice the answers are the same.
           internal consistency - are the items on a test consistent?  This can be calculated by looking at the inter-item correlations.  Chronbach's alpha is a statistic that measure inter-item reliability.  Example, correlate the ABORT variables in the GSS data file.  We see that all the correlations are positive and significant.  We can then make an index of them by adding up scores on the six variables.

Validity  is it "really" measuring what it is supposed to measure.
          Face Validity - does it look right?   This is often related to fairness, people will object to the use of measures that do not have face validity even though they may have predictive validity, e.g., using the frequency of moving as a criterion for loaning money.
          Predictive or criterion validity - does it predict what we want to predict, some "true" measure.  SAT test predicts college or law or medical school grades.
          Convergent validity -  do several measures give the same result.              
          Construct validity - does the measure perform as our theory says it should.  We use this when we have no criterion.   This is the most difficult, it is used when things are inherently difficult to measure.  Essentially, it asks whether the results are consistent with what we would expect based on theory and past experience.    Camden schools report.  Brim school report, see pdf page 14 for tables.  Story on Brim with graph.