Dec 4  Smerconish on death penalty. - Capital Punishment and Homicide  -         Myths of Murder and Multiple Regression.  Police Crackdowns and Slowdowns. 

December 4  -  Field Research 

Margaret Mead, the only anthropologist (or sociologist) to get her own postage stamp, won fame through field work, primarily her book Coming of Age in Samoa.  Later, this book was denounced by anthropologist Derek Freeman in his book Margaret Mead and the Heretic : The Making and Unmaking of an Anthropological Myth.Anthropologists have come to Mead's defense, and have restudied the case, but I would have to agree with your text that "had Mead come back from Samoa with an accurate ethnographic report, it would not have made her famous."
     More recently, there has been a raging controversy about the book Darkness in El Dorado about research on the Yanomamo in Venezuela is the latest ethical controversy, which also raises important methodological questions.  Many of the book's allegations, however, have been contested by the National Academy of Sciences.
  The combining of fiction with factual research is increasingly common both in anthropology and in biographies.  Sometimes this is openly done as a literary form, in other cases such as that of Rigoberta Menchu, it is only admitted when critics discover it.   The Rigoberta Menchu Controversy by Arturo Arias. 
There are many problems with field research:  ethical issues, problems of reliability and validity when data are gathered by only one researcher, etc. A controversial book is Laud Humphrey's Tea Room Trade, which raises ethical issues. He studied gay sex in a men's room in a park in St. Louis, without informing the participants what he was doing.
    Field researchers sometimes seem to find examples that fit their preconceptions, and their work is often ignored by those who do not like the results, e.g., Leon Dash's book When Children Want Children and Rosa Lee which are just ignored by welfare advocates who prefer more sympathetic treatments.  One of the best field studies is Kathryn Edin's book Making Ends Meet. which is highly sympathetic to the mothers.  However, Edin collected statistical data as well her illustrative observations.  The statistics showed that almost none of the mothers actually lived off their grants alone.  Eli Anderson's book Streetwise on men in a Philadelphia ghetto has been well received, in large part because goes beyond one-sided advocacy.
   James Flatley, Etienne Jackson and Robert Wood's  Video version of Down Germantown Avenue. 

    A great strength of field work is observing behaviors that the people themselves don't understand or aren't even aware of., or at any event, are unable or unwilling to talk about.  Anthropologist Jules Henry spent a week living in each of the homes of several children who had grown up mentally ill, trying to discern patterns in the family interactions that contributed to the illness.   Myra Bluebond-Langner's book The Private Worlds of Dying Children has been very influential;  she has just published a sequel called In the Shadow of Illness : Parents and Siblings of the Chronically Ill Child    Field reserch offers a richness of description and possibility of new insights that is unparalled by any other method.  Unless it is supplemented with other methods, it does not provide statistical data, and it is hard to replicate.
    Myra Bluebond-Langner of our Anthropology Department wrote a classic, The Private Worlds of Dying Children, and more recently, In The Shadow of Illness.

Coming of Age in New Jersey. 

The Corner.     Memoirs:  Frey dispute with Oprah. 

Black American Students in an Affluent Suburb.  by John Ogbu. 

Elijah Anderson, The Code of the Street and others.

Commentary on Ogbu's research. 

Many scholars who have disputed those findings rely on a continuing survey of about 17,000 nationally representative students, which is conducted by the National Center for Education Statistics, an arm of the federal government. This self-reported survey shows that black students actually have more favorable attitudes than whites toward education, hard work and effort.

But that has by no means settled the debate. In the February issue of the American Sociological Review, for example, scholars who tackled the subject came to opposite conclusions. One article (by three scholars) said that the government data were not reliable because there was often a gap between what students say and what they do; another article by two others said they found that high-achieving black students were especially popular among their peers.

"It's difficult to determine what's going on," said Vincent J. Roscigno, a professor of sociology at Ohio State University who has studied racial differences in achievement. "'I'm sort of split on Ogbu. It's hard to compare a case analysis to a nationally representative statistical analysis. I do have a hunch that rural white poor kids are doing the same thing as poor black kids. I'm tentative about saying it's race-based."

Indeed, Professor Mickelson of the University of North Carolina found that working class whites as well as middle-class blacks were more apt to believe that doing well in school compromised their identity.

All these years later, Professor Fordham said, she fears that the acting-white idea has been distorted into blaming the victim. She said she wanted to advance the debate by looking at how race itself was a social fiction, rooted not just in skin color but also in behaviors and social status.

"Black kids don't get validation and are seen as trespassing when they exceed academic expectations," Professor Fordham said, echoing her initial research. "The kids turn on it, they sacrifice their spots in gifted and talented classes to belong to a group where they feel good."

Frey Dispute with Oprah        Dutch:  Fictionalized Reagan Bio.  NY Times review. 

November 29  -  The powerpoint on Unobtrusive Research is in SAKAI.

Content Analysis - "unobtrusive data"  Data created by a bureaucratic system, e. g. police records, or often by the media.  Television or Newspapers either because that is our interest, the media, or as a way of getting information, e.g., on crime reported in the news.

Similar to survey research, except that you do coding instead of interviewing.  Coding means that you assign numbers to phenomena that you observe.  Counting things.  Each of your variables is coded from the published information.

Conceptualization.
Measurement.  Reliability and Validity.

Manifest Content - what's it's about on the surface
Latent Content - things that we infer about the content, e.g., does the writer sound angry?  Indignation, sexy?
A Content Analysis Study of Editorial Cartoons.
 A Content Analysis of Internet-Accessible Written Pornographic Depications.

Our interview data:    Coding scheme in Microcase.  Frequencies.  How to interpret?


November 27

Regression equation.  This equation is used with two variables measured on an interval scale, or at least an ordinal scale that approximates the interval.  It assumes a linear relationship between variables, i.e., that if you plot them on a scatter plot they will approximate a straight line.  If they do not, then the regression equation is not an appropriate tool, at least not unless the data can be modified in such a way that the relationship appears linear (e.g., by converting to logarithms).  This can best be illustrated with the scatterplot tool in Microcase.

The  equation is used to predict the Dependent variable (Y)  with the Independent variable  (X).   Y  =  a + b X  where:
X is the independent variable
Y is the dependent variable
b is the "unstandardized regression coefficient"
a is the "intercept"

In the exercise we will be doing, year will be the independent variable.  The advantage of this is it allows us to project into the future, assuming that trends are linear and will continue to be so.

November 13: Powerpoint on Survey Research is in SAKAI.  . Questionnaire design. Tips for Writing Questionnaire Items.   National Criminal Victimization survey as an example.  Interviewing Guidelines.   Survey Interviewing.

November 8.   Critical article from NY Times on ethics panels.  Carol Tavris, The High Cost of Skepticism.  The test we took in class today is in SAKAI in the resources folder with the filename IRBtest.doc. 

November 6.    Experimental Methods.  Two powerpoints are in SAKAI:  ExperimentalResearch.ppt and ExperimentalMacroSociology.ppt

  Experimental Designs.  See the graphs in the book or on Trochim's WEB site:  Types of Designs. 

Essential characteristics:

1. Two or more groups are matched, usually by random assignment, sometimes by a kind of stratified random selection, e.g., an equal number of men and women or black sand whites in each group.  But the key is random assignment so that the groups can be assumed to be the same on all variables.  "Quasi-experiments" are when we use groups that are pretty much the same but we didn't assign people at random
2. The Independent Variable is "manipulated," i.e., it is applied to one group and not to the other
3. Change in the Dependent Variable is measured

Experiments can be done:

1. In laboratory settings with volunteers, e.g., student volunteers
1. The Milgram Experiment on Obedience to Authority
   
2. In institutional settings such as prisons, hospitals, rehabilitation centers, etc., where people are assigned to treatment groups
1. New drugs and medical treatments generally must be shown to work in experiments before they are approved for use.  Often, treatment is compared to a placebo.  These experiments are usually "double-blind," to control for the psychological effects of knowing one is getting treatment.  This is a way of controlling subject bias and experimenter bias/
2. In criminal justice, one might do an experiment comparing a "half way house" to drug treatment program to a prison term for offenders.  To do this, you would have to get the judge to assign offenders to different programs at random.  Ethical issues are raised here and there are likely to be objections
3. Occasionally in natural settings, for example
1. welfare reform experiment, assign some recipients to the old program, some to the new.  This didn't work very well, there were errors in the group assignments and the women often forgot which group they were in anyway
2. vaccination experiments
3. guaranteed annual income experiments
4. Kansas City Patrol Experiment. 
   

Although logically experiments are the most rigorous way to test causal hypotheses, there are practical problems:

• It may be hard to manipulate the independent variable effectively, it may not have enough importance to people that they notice it
• Experimental conditions may not be realistic enough, e.g., the Milgram experiments having people apply electric shock to people, experiments that simulate being in prison.  An experiment is not the real world and people know it.  This is called external validity, does the experiment match real world conditions
• There may be problems of internal validity, difficulties in carrying out the experiment:
1. "History" effects - the world changes during the experiment, people get older, more mature, they are effected by things in the real world
2. Maturation, people get older, learn more
3. Testing effects, taking the pretest measure effects people, causes them to change.  Sometimes we have a matched but untested control group that is measured only after the experiment.
4. Instrument effects, the testing instrument may change.  You can't use the same exact test sometimes because people will remember it, so items change
5. Regression to the mean, just by chance the people who got extremely high or low scores on a pretest are likely to get more average scores on the second test.
6. Subject "mortality" - we may lose people.  This is especially a problem in testing things like drug rehabilitation, it works for the people who stick with it, the failures drop out
• Ethical concerns:  people may not be willing to be experimented on, or it may be harmful to subject them to experimental conditions, e.g.,
1. Tuskeegee syphillis experiment denied some men penicillin.  You can only deny an experimental drug if you are not "certain" that it works or if the condition is not serious, e.g., common cold research
• A big strength of experiments is resolving questions that involve different recollections of events, e.g., children's reports of abuse.  You don't know what "really" happened and people disagree on how well they accept the recollections of different people.  In an experiment, you know what really happened, so you can check the accuracy of perception.  We find that children often remember things that didn't really happen.   "20/20 report on Child Abuse experiments (VIDEO shown in class from an ABC News 20/20 show aired October 22, 1993, hosted by Hugh Downs.   Transcript available at www.transcriptstv.com) demonstrates false memory because we know what really happened since it happened in a controlled experimental setting.  This is much more difficult to establish in real life case histories:  Loftus:  Who Abused Jane Doe?  There is other information online on the Kelly Michaels case and other cases.

Another example we can look at is an experimental study of internet downloads.  This was published in Science magazine because it demonstrates a sociological principle with rigorous experimental data.    Several documents from this study are in WEBCT, the most accessible summary is in a file called "Experimental Macrosociology". 


October 30 -  Sampling -  The powerpoint used in this class is in SAKAI with the title Sampling Powerpoint.ppt.  However, you are not responsible for everything in the powerpoint or in Babbie, just for the material included in these notes.  Use the books to help you understand anything that is not clear.

SAMPLING is used when we are interested in studying a population that is too large for us to study each individual.  The first step is to define the population we wish to make statements about, e.g. adults in New Jersey, probable voters, people convicted of felonies, graduates of our department.  We might want to study the entire population of the USA.  If we try to collect data from everyone, this is a census.  The Census Bureau does this once every decade, and misses a lot of people.  Everyone else does sampling, we select a cross-section to represent the population.  If you try to study the whole population, you often fail to do a good job.   Gallup:  How Polls are Conducted.

Terms we need to know:

Sample statistic - data from our sample, e.g., a percent or mean score
population parameter - the true value for the whole population for the same percent or mean score

Margin of Error:  How much a sample statistic is likely to vary from the population parameter.  We say that we are 95% sure that the sample is not off by more than the margin of error.  How this is presented in NY Times.  "19 out of 20" is another way of saying 95%.    95% is our confidence level. - we could use another one, but the convention is to use a 95% confidence level.
Standard Error -  this is actually half of what is usually called the "margin of error" - it is a 66% confidence level, we can be 66% sure that the statistic is within one standard error of the paramater value.  Many times statistical reports do report a standard error instead of a margin of error.  In that case, look to see if the result is more than two standard errors from the null hypothesis (often zero). 
 .
Confidence interval:  the range within which we think a sample statistic might differ from the population parameter,.  We get this by taking the margin of error and substracting it from the sample statistic, then adding it to the sample statistic. e.g., if the margin of error is 3% and the sample statistic is 67%, the confidence interval is from 64% to 70%.  We are 95% sure that the true figure  or population parameter is within this limit.

Note that all of this assumes random sampling, it does not compensate for errors due to non-response or deficiencies in the sampling frame, the actual list from which we select respondents. 

The mathematics is simpler if we use a  simple random sample, which means that each person (or other sampling unit) in the population has the same chance of appearing in the sample.  In practice, however, we often do not use simple random samples, for several reasons:  (see page 208 in Ayers for a condensed summary of these techniques)

1. we may not have a list of the population.  If we do not, we first divide the sample into sub-groups of some kind (census tracts, blocks, classrooms, organizations, depending on the nature of the study).  We then sample the subgroups and list the populations in them .  This is called multistage cluster sampling.  It is often used for interviewing people at home instead of over the telephone. 
   
2. We may be interested in differences between sub-groups of the sample and need to make sure we have enough of them.  In this case we select random samples of each of the relevant sub-groups, and weight the results appropriately.   This is called stratified sampling, e.g., the NY Times  explains that its surveys oversample black respondents.  Most surveys today are on the telephone.  This is still random sampling, but it is random within the strate.  The results have to be weighted to get accurate popular figures. 
   
3. Sometimes we just go down a list, which is called systematic sampling.  This gives the same results as simple random sampling, unless there is some systematic ordering to the list that causes a distortion
4. Sometimes we use non-random or "quota" sampling.  This is done for convenience, or because we just want to know what the range of differences is without putting numbers on them.  Internet surveys tend to be of this sort, although there are some randomly selected Internet panels.

These things are all explained in Babbie.  The main terms are also discussed in the powerpoint that has been posted.  Babbie also discusses some other terms, but I am going to limit the test to terms covered in these notes.
.
Babbie does not do sampling statistics, but we will do some in this course.  You need to consult these notes for the statistics (you could find them in any statistics book, of course, along with much more).  I am focusing on some practical questions that come up.  The first is:

  How big of a sample do I need? Size of the sample does not depend on the size of the population.   It depends on two things:
1.  How much error you can tolerate.
2.  Whether you need to calculate statistics for sum-groups of the population (in that case, each one becomes a separate sample for statistical purposes.

To get the sample size needed.

1.  Decide how many groups you need to calculate statistics about.  If you need statistics for only the whole population, the number is one.  If you want statistics for five counties in South Jersey, it is five.  If you want statistics for freshmen, sophmores, juniors and seniors, it is four, etc.
2.  Decide on the margin of error you can tolerate, e.g., 5%, 3%, 10%, for statements about each subgroup
3.  convert the margin of error to a proportion, e.g, 5% becomes .05
4.  The sample size, n, is computed with the following formula where m is the margin of error expressed as a proportion  n = 1/ m²
     For example, for a 5% margin of error   n =  1/.05²  which is 1/ .0025 or 400.
5.  Multiply this result by the number of groups.  Thus if you needed results for black, white and Hispanic respondents separately, you need a sample of 1200 to get a 5% margin of error for each group. 
6.   Ignore any information about the size of the population or the size of the groups

If you already have a sample and need to compute the margin of error, just reverse the formula    m = 1/sqrt(n).  or  in words
Margin of error is equal to one divided by the square root of the sample size.  Sample of 400, the square root is 20.  1/20 = .05 or 5%.  If you interviewed 400, 300 were white, 50 were black and 50 were others. If you have subgroups, you need to use only the number of respondents in each group to compute the margin of error for that group For the blacks, with a sample of 50, we would have a 14% margin of error.  For the whites, with a sample of 300, we would have a 5.8% margin or error.  

  An example:  Take 300, the square root of 300 is = 17.32     1 /17.32 = .0577  * 100 = 5.8%

This formula above is is for  proportions or percents (if you move the decimal over two).  We use this if we are not told otherwise.  However, sometimes we need margins of error for mean scores instead of percentages. If you need a margin of error for a mean score (an average such as income in dollars or scores on a test), you need to know the standard deviation (sd) and the sample size (N). Ignore any other information you are given, including the size of the population.

Use the following formula: M = 2 * sd / SQRT(N)

Suppose I sample 457 Camden residents and the mean income is $27,541  and the standard deviation is $3452

M = (2 * 3452 )/sqrt(457).  This result will be in dollars, not percentages. 

M =      6904        /21.378  =  $322.95.  

Confidence Interval:   I am 95% sure that the population figure is between:  $27,218.05 and $27,863.95

Some margin of error problems:

Suppose I did a sample of 400,selected from the 7,357,218 people living in New Jersey.  What is the margin of error? M  = 1 /SQRT(N).   N is the sample size, not the population size. N = 400.   Sqrt of N =  20.   1/20  =  .05  or 5%.  If I find that 42% agree, that is my population "statistic."    The population paramater is the true value, and I would say that I am 95% sure (my confidence level) that the paramater is between 42% - 5% and 42% + 5%.   The true value should be between 37% and 47%.  Suppose I go to 1000, what is my margin of error?  M = 1/SQRT(1000).  =   1/ 31.62  =  .0316 or  3.2%.  The confidence interval is between 38.8% and 45.2%.  This applies to statements made about the whole sample.  42% of the respondents said yes, the margin of error is 3.2%.  For statements about a subgroup, the N is the number of people in that sub group (genders, races, sports fans).  We have a sample of 1200, of whom 800 are white, 300 are black and 100 are Hispanic.  57% of the Hispanics said yes to the item.  What is the margin of error for this percent?  Since it says "of the Hispanics" our N is the number of Hispanics, or 100.  M = 1/SQRT(100)  = .10 or 10%. For the black respondents, our margin of error is M=1/SQRT(300).  = 1 / 17.32  =  .0577 =  5.8% For the white respondents  M =  1/SQRT(800)  =  .03535 or 3.5%.  How large a sample do I need to get a 5% margin of error, with a population of 485,321?  N = 1/M2     M must be expressed as a proportion, not a percent.  M = .05.    .05 * .05   = .0025. Sample size = 1/.0025  =  400 Suppose I wish to study the black, white and Hispanic populati0n and I need a margn of error of 5% for each group.  How large a sample do I need? The other thing we need to deal with is margins of error for mean scores.  Thein a survey of 300 county residents, the mean income  is $45,321.  We need to have the standard deviation.  The Standard Deviation is a measure of variation.  The standard deviation is $3521.  M = 2 * sd/sqrt(n).  N = 300.   2 * 3521/17.31  = $203.29.

October 25 -  Note:  The Powerpoint used for this class is in SAKAI with the title IndexandScaleConstruction.ppt.  Scaling and index construction are techniques for measuring abstract concepts with a number of items.  This can be an attitude, e.g, "conservatism" or "authoritarianism."  Or it can be a category of behavior, e.g, "violent crime" which is measured by averaging together the frequency of a number of crimes.  Magazines like to do this to come up with ranking, e.g, of the best community to live in or the worst one, the best colleges, etc.  Usually this is done just by adding up a number of characteristics (in which case your texts would call it an "index", although many people still use the term scale).  This gives is a rank order of sorts, and some idea of how high or low cases are, but we really don't know how big the differences are or what the scores really mean.  What does it mean that Camden is the "most dangerous city"?  Is it really that much more dangerous than the next dangerous?  Or than an average city? 

Most of the measures we actually use are what your textbooks call indexes.  Our midterm exam is an example.  We just add up the point to measure the general variable "knowledge of research methods as covered in the first part of the course."  This is an index.  To make a true scale we would have to rank the items from easy to hard.  This is tricky, because items that are hard for some people are easy for others.  When we make an index or scale, we get measures that can be treated as interval, even if they are not strictly interval.  Scaling methods can be more precise, but these are not used as often in sociology or CJ because they are more difficult and the added information is not always needed or useful.

Attitude Scaling methods include Thurstone and Guttman Scaling. and true  Likert scaling.   Usually Likert-type items are just used to make an index.  Thurstone scaling tries to get true interval measurement.  Guttman scaling gets ordinal measurement.  Index construction approximates interval scaling in some ways, but it is hard to know how well. 

  For an example of true scaling in criminal justice, we could scale the seriousness of crimes.  There are various methods of measuring this. - paired comparisons means asking a sample of people to rate crimes based on their perceived seriousness.
New Zealand Study on Attitudes to Crime.    Crime Victims United (Oregon). 

Indexes work well IF the items actually measure a common trait in the minds of the respondents (not just in the mind of the researcher).  We determine this by measuring the correlations between answers to the specific items.  If the items intercorrelate well, an index will work well.  The exercises in Ayers illustrate this process.

A very popular test is the Myers-Briggs Type Indicator, based on Jungian personality theory.  You can take several free versions of this and related tests online (Wikipedia article).  Several are available from similarminds.  A problem with this is that it sorts people into categories although the measure is really continuous.  This makes it understandable and it is very widely used. 


October 23.   The midterm exams will be returned together with the answer keys.  We can discuss any questions about them on Thursday.  The deadline to withdraw with a W is November 12. 

For our next assignment, we will be using the General Social Survey 1972-2006 data file.  This very large data set is described in an article from NY Times.  To access the data we can use the Survey Data and Analysis program at the University of California, Berkeley.  Just click on SDA.  to go to the SDA Frequencies/Crosstabulation program. 

Your task is to produce a multiple bivariate table with one column variable and at least three (or at most six) row variables. (NOTE:  you will get a little EXTRA CREDIT for doing more than three variables).  The percentages should be row percentages.  You should write a paragraph summarizing the results in which you correctly describe some of the percentages.  The finished product should look like this Sample Assignment.   You should prepare the table in a word processor, print it out and bring it to class on October 30.

You may use any variables you wish for this paper - you can search for them with the SEARCH facility.  If you do not have any idea, I suggest you use one of the ones selected by the New York Times for the column variable (your dependent variable).  These include premarital sex, trust in others, frequency of prayer, marijuana legalization, exciting life, fear at night, the afterlife, spanking children, confidence in institutions (just select one institution), happiness, abortion (pick one item), gun permits, happiness of marriage, newspaper readership, x-rated movies, homosexual teachers, social class memberhsip.  You should NOT use women & politics because we used that for the sample assignment.

Then you should pick three to six independent variables which you will use for row variables.  These can include such things as age, sex, religion, political party, year of interview, region of residence, education, marital status, etc.  HOWEVER, this data set has not been prepared for this purpose, so many variables need to be RECODED first.   To do this, click on "create variables" and "recoding rules".  It shows how to recode AGE for this purpose.   You need to check each of your row variables to see if it needs recoding.   This gives you the flexibility to recode them as YOU wish rather than relying on someone else's choices.

Then you need to do a cross tabulation for EACH of your row variables.  You will need three for three variables, six for six.  You may wish to print these out for your convenience or you can work on the screen.  You take the results for all the variables and type them into one big table for your assignment.

If you choose "year" as a variable, you should get the same results the New York Times got.  You could just select key years rather than doing them all.

A number of students have asked if they need to include the results for the total sample in their tables for the multiple bivariate assignment.  The answer is YES. 

Where do you get these?  If you look at the bottom of any of your cross-tabulation tables you will see them.  These may vary slightly from table to table, however, because of missing data.  Another way to get them, for the whole sample, is to type the name of the column variable in your table in the ROW box on SDA with nothing in the column box.  This will get you the frequencies.

October 16  Review. 

1.  These notes should be helpful, but they do not offer full explanations.  For that, you need to refer to the books. 

2.  The Ayers book has good brief summaries at the beginning of each chapter before it goes into the exercises.   The pages you should review carefully are:
1-3 on the probabilistic nature of social science and the nature of variables and attributes (chapter one in Babbie)
29 -31 on theories and concepts (but not paradigms)  (the second part of chapter two in Babbie)
38
 to 39 on deduction and induction, observations, generalizations and abstract theoretical propositions (also in chapter two of Babbie)
59 on necessary, sufficient and probabilistic causes.   (the rest of the topics are also in Chapters Four and Five of Babbie).
60 on independent and dependent variables
67 and 68 on spurious relationships, antecedent variables and multivariate analysis
93 on units of analysis
96 on the ecological fallacy
102-105 on longitudinal studies:  trend, cohort and panel
121-122 on conceptualization and measurement, definitions, indicators
123-128 on reliability and validity.
145-150 on levels of measurement and statistics.

3.  Chapters Four and Five in Babbie are very important.  Read them.   Also check the "main points" and "key terms" at the end.

4.  The Statistics Overview chart is important.  We have covered everything up to the Multiple R.

5.  The statistics questions will be very similar to those on Exercises Five and Six:  You should be able to compute row, column, and total percents and use them in a sentence.  You should understand and be able to compute expected frequencies.  You should be able to compute means, medians, ranges and standard deviations as explained on the Descriptive Statistics page.

You may find the Companion Site to the Babbie book helpful, especially for Chapters Four and Five.  You can find it at www.thomsonedu.com/sociology, by clicking on research methods and then on our book.  Or you can try this deep link which may go directly to it. 


October 11 -  Graphs and Charts to Communicate Statistical Data.  Exercise Six is available for download, but the answers should be put in SAKAI for immediate feedback.   Look in the Tests and Quizzes module on SAKAI for MethodsExercise_Six.

October9   We will focus on Exercise Six in the Ayers book.  We will also begin reviewing for the midterm.  Assignment Six, which is in SAKAI, is designed to prepare you for the midterm.  The reading in Babbie covers the same material and may be easier to read.  Chapters four and five in Babbie are key.  We will also be covering some statistics items.  TheStatistics Overview summarizes these.  We have covered the mean, median, mode, range, standard deviation, interquartile range, observed and expected frequencies, row, column and total percents, chi square, ANOVA, correlation coefficient.  The midterm will not cover regression or sampling statistics.  Note that ANOVA was not on the Statistics Overview handed out in class, we will discuss it today.  The Descriptive Statistics page explains how to compute a standard deviation.  You should be able to do a mean, median and standard deviation and row, column and total percents and expected frequencies.  You should understand the difference between descriptive and inferential statistics.

The example worked in class today:

45 men like spinach
85 women like spinach
65 men do not like spinach
80 women do not like spinach

                        Men     women

Like                   45        85            130

Don't                  65       80             145

                         110      165            275

130 respondents like spinach

expected frequencies answers the question:  if men and women did not differ in their liking for spinach, how many men would have liked spinach.

Easiest way to get it is to multiply the row total by the column total and divide by the grand total

How many men would we expect to like Spinach?    row total for the like spinach row 130   column total for the men column 110  grand total 275

                  139*110/275  =  52.0  This is not a percent, it is an expected frequency. 

What percent of the men like spinach?   the number of m en who like spinach/the number of men  45/110= 40.9%  of the men like spinach

What percent of the people who like spinach are men?  the number of men who like spinach/the number of people who like spinach  45/130 = 34.6% of the people who like spinach are men.

Midterm coverage.
    The syllabus has been structured around the Ayers book, we have covered the first six chapters.  It has good concise summaries at the beginning of each chapter. 

Oct 4  We will go through pages 121-129 in the Ayers manual which illustrates the following concepts (among others?):
conceptualization
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.


Oct 2   Note:  Chapter 5 in Babbie covers chapters 5 and 6 in Ayers but in reverse order.  It may be best to read the Babbie book first.  I will go through the topics in Babbie's order first, then go back to Ayers to review and to do the exercises.

Conceptualization.  Thinking through what our conceptions mean, defining them.

Defining the Concepts, or Conceptualization.  This 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 one 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.

Correlation Coefficients
N: 1603     Missing: 1229
Cronbach's alpha: 0.874
LISTWISE deletion (1-tailed test)     Significance Levels: ** =.01, * =.05
    ABORT DEF    ABORT WANT    ABORT POOR    ABORT RAPE    ABORT SING    ABORT HLTH
ABORT DEF     1.000       0.447 **    0.439 **    0.618 **    0.443 **    0.641 **
ABORT WANT    0.447 **    1.000       0.816 **    0.435 **    0.840 **    0.332 **
ABORT POOR    0.439 **    0.816 **    1.000       0.442 **    0.827 **    0.337 **
ABORT RAPE    0.618 **    0.435 **    0.442 **    1.000       0.437 **    0.636 **
ABORT SING    0.443 **    0.840 **    0.827 **    0.437 **    1.000       0.330 **
ABORT HLTH    0.641 **    0.332 **    0.337 **    0.636 **    0.330 **    1.000  

Correlation Coefficients
N: 1603     Missing: 1229
Cronbach's alpha: 0.796
LISTWISE deletion (1-tailed test)     Significance Levels: ** =.01, * =.05
            ABORT INDX
ABORT DEF     0.734 **
ABORT WANT    0.858 **
ABORT POOR    0.855 **
ABORT RAPE    0.728 **
ABORT SING    0.859 **
ABORT HLTH    0.645 **
 
    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. 

                          An example:  the measurement of romantic love.