Here are some points covered with multiple choice questions:

• The differences between survey research, field research, experimental research, focus groups and content analysis
• The field research, content analysis and other studies covered in the last two weeks of class.  If you weren't in class, you can check these out on the notes.
• History effects, maturation effects, testing effects, regression to the men and subject mortality in experimental research.
• Criteria for establishing causation.  Independent, dependent, antecedent and intervening variables.
• Levels of measurement:  dichotomous, nominal, ordinal, interval and ratio.  The levels of measurement required to use statistical techniques such as percentages, chi square, correlation, regression, means, standard deviation.
• Interpreting a time series graph, such as those we did with the Historical Trends module of WEBCT.  Fitting a linear regression equation to a time series graph.
• Tests of reliability:  inter-rater, test-retest, internal consistency.
• Tests of validity:  criterion, construct, convergent, face.
• Types of samples:  simple random, stratified, quota, systematic, cluster.
• Interpretation of scattergrams, e.g., height and weight.

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.

Experimental Research.   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

1. In laboratory settings with volunteers, e.g., student volunteers
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

• 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) 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? 

The Review Glossary is not adequate as a guide to this chapter.  Some points to be covered:

• Field research is needed to gather data on processes, especially those involving personal interaction
• It is good for observing behavior rather than getting reports of behavior or attitudes
• It takes a lot of time, and is less likely to be replicated, leading to problems of reliability.  Would another researcher observe the same thing?  Validity issues are difficult to assess, what abstract concepts are being used?
• By its nature field research is likely to be inductive, people go to see what's happening rather than to test a theory.  But observers may have a "hidden agenda" that leads them to see what they want to see.
• Selection of sites and topics is likely to reflect the special interest of the researcher.  There may be a bias toward the bizarre, or towards cases that will prove a particular point.
• Getting access is problematic, groups may not want to be observed.  Sometimes deception is used, which raises ethical issues.  The whole question of ethical review is difficult to apply to field research, e.g., "informed consent."
• There can be structured and unstructured observation.  You can count things.  Observation through a one-way mirror is sometimes possible, e.g., of day care centers.  This might be done with WEB-cams.
• Notes are important, need to be recorded quickly.  Data base programs can be used, here there might be validity issues, in the coding of observations, e.g., was a certain remark a case of "sexual harassment"?
• Accounts tend to be descriptive, there is a bias toward things that are interesting.  It is possible to combine field work with quantitative data collection.

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."  Here is the NY Times Review of Freeman's critique of Mead.

  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.

    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.

Research Design.  How research is organized or structured to accomplish different ends.

For the exam, you should know how to set up the regression equations to fit a path diagram.  The rules are the follows:

1. There should be a regression equation for each variable that has an arrow pointing towards it.
2. For each equation, the variable having arrows pointing into it is the dependent variable, and goes to the left of the euqals sign.
3. For each equation, the variables on the left of the dependent variable that have arrows pointing into it are the independent variables.  These are listed to the right of the equal sign and connected with + signs.
4. There is no need to include an intercept, because we are interested only in the standardized regression equations or beta weights.

    alienation from government = status deficiency

Today we will look at testing causal hypotheses.  On page 93 in the text, we have the example of the relationship between Height and Liking Basketball.  This is anIV and a DV.  An obvious TEST VARIABLE is Gender.  This would be Antecedent, Gender determines both your height and liking for basketball.  We could draw this as a path diagram (on board).

When we introduce the control, we split the table into two parts, e.g.,

                             Males                  Females                 Total
                        Tall     Short           Tall   Short         Tall    Short

Likes BB           85%    85%            25%   25%         65%    45%
Does Not           15%    15%            75%   75%         35%    55%

Total                 100%  100%          100%  100%      100%   100%

In the real world, things are never this sharp.

Let's look at some real data, using FEAR WALK, PLACE SIZE and R.INCOME from the GSS data set:

In the total sample, the low income respondents are more likely to feel there are areas near them where they should fear walking.  However, this effect disappears for some of the respondents when we control for the size of the town in which they live.

To make it a finished Table:
                Small Town or rural         Small City        City/Surb          Total
                  Low  Med  Hi             Low Med Hi        Low Med Hi      Low Med Hi

Fear Walk         30%  27%  24%            48  42%  20%      56  41  43     51%  39%  41%
No Fear           70%  73%  76%            52% 58%  80%      44% 59% 57%    49%  61%  59%
                     p = .710                p = .043           p = .000       p=.000
                     N = 251                 N = 133            N = 1253      N = 1637
 

To to a more complete causal model of Fear of Walking at Night, we should introduce more variables.  Some of them may be in our data set, others now.  

What variables should we look at?

Variables      Hypotheses
Gender           Females more fearful than males.
Age              Elderly more fearful, also Children.  Might be curvilinear.
Crime Rate       People in high crime communities
Street Lighting
Freq of Patrols
Graffiti, Broken Windows, Trash, other indicators of an "out of control" neighborhood
Bicycles
Number of Pedestrians
Physical Shape
Training in Self Defense

We can examine some of these variables with our data.  We may find it useful to use regression rather than cross-tabulation.
We can also use pages

Today we will look at testing causal hypotheses.  On page 93 in the text, we have the example of the relationship between Height and Liking Basketball.  This is anIV and a DV.  An obvious TEST VARIABLE is Gender.  This would be Antecedent, Gender determines both your height and liking for basketball.  We could draw this as a path diagram (on board).

When we introduce the control, we split the table into two parts, e.g.,

                             Males                  Females                 Total
                        Tall     Short           Tall   Short         Tall    Short

Likes BB           85%    85%            25%   25%         65%    45%
Does Not           15%    15%            75%   75%         35%    55%

Total                 100%  100%          100%  100%      100%   100%

In the real world, things are never this sharp.

Let's look at some real data, using FEAR WALK, PLACE SIZE and R.INCOME from the GSS data set:

In the total sample, the low income respondents are more likely to feel there are areas near them where they should fear walking.  However, this effect disappears for some of the respondents when we control for the size of the town in which they live.

To make it a finished Table:
                Small Town or rural         Small City        City/Surb          Total
                  Low  Med  Hi             Low Med Hi        Low Med Hi      Low Med Hi

Fear Walk         30%  27%  24%            48  42%  20%      56  41  43     51%  39%  41%
No Fear           70%  73%  76%            52% 58%  80%      44% 59% 57%    49%  61%  59%
                     p = .710                p = .043           p = .000       p=.000
                     N = 251                 N = 133            N = 1253      N = 1637
 

To to a more complete causal model of Fear of Walking at Night, we should introduce more variables.  Some of them may be in our data set, others now.  

What variables should we look at?

Variables      Hypotheses
Gender           Females more fearful than males.
Age              Elderly more fearful, also Children.  Might be curvilinear.
Crime Rate       People in high crime communities
Street Lighting
Freq of Patrols
Graffiti, Broken Windows, Trash, other indicators of an "out of control" neighborhood
Bicycles
Number of Pedestrians
Physical Shape
Training in Self Defense

We can examine some of these variables with our data.  We may find it useful to use regression rather than cross-tabulation.

We can also use pages 114-122 in the workbook as examples..

The Art and Science of Cause and Effect. (powerpoint)

Probabilistic cause, not an absolute cause, not a cause that is sufficient or necessary.   "Cigarette smoking causes
cancer."  WHat we mean is, smoking cigarettes increases the likelihood of getting cancer.  How much?

There are multiple causes for everything.  What we want to find out is how much each thing contributes.  There are also
causal linkages, or indirect causes.  A causes B and then B causes C.

Diagraming causal models.  We put the dependent variable at the right.  We draw arrows going into it for each causal
variable that effects it directly.  Then we can have arrows that go into the arrows, steps into the causal analysis, as in
this sample file:
http://crab.rutgers.edu/~goertzel/homomale.htm

Criteria of Causation - how do we know that something is a cause of something else.

1.  Time Order.  The cause comes before the effect.  Sometimes we sort out the time order theoretically, we assume that
education preceeds employment.  Or we can use a research design that involves gathering data at two points in time.  If
you don't have measurements at two points in time, this is shaky.

2.  Correlation.  The two variables vary together.  When one is high, the other is high OR when one is low the other is
high.  This gets at the degree of causation, the higher the correlation the strong the causal relationship.

3.  non-spuriousness,  we want to know that the correlation is not cause by something else.  This can be tested rigorously with experimental designs, when feasible.  But with most sociological or criminal justice problems experimental rigor is not possible, so we may use statistical controls as an alternative.  This is much less rigorous, but often all we can do is see whether the relationship holds up when we control for other variables that might account for it.

Dependent Variable - that is what we want to explain.  Often these are opinions or behaviors

Independent Variable - what we use to explain it.  Often there are traits or physical characteristics, e.g., sex or race,
almost always independent.

If you study the relationship of race on voting, for example, race would be independent and voting dependent.

Antecedent variables, things come before the independent variable.  This helps us to deal with a causal chain.
Antecedent variable cause IV which causes the DV.
If the antecedent variable "explains" the relationship, we have an "explanation", we say it is "spurious".

Intervening Variables, this that are intervening, e.g.   Race determines ideology which determines the vote.
This is an "interpretation" it tells WHY the causal relationship exists.
Path Models:  a way of graphically expressing complex causal models.

Example:  Determinants of Adult Homosexuality in White Males.

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

   m = 1/sqrt(n)    Solve for N:      m² =  1/n      n * m² = 1     n = 1/ m²    If we need a margin of error of 3%, or .03.   n = 1/ .03²

  If you have a sample size and need to know the margin of error, use    m = 1/sqrt(n)

   If you are given a margin of error and asked how large a sample you need, use  n = 1/ m²

          In these formulas n = the size of the sample (not the population).    m = the margin of error expressed as a proportion, not as a percent.  Thus, if the questions says "we need a margin of error of 5%, then m = .05.   

If our sample is stratified, this means we really have several sub-samples and we need the same size sample for each of them, regardless of the size.  For example, if we want sample white, black and Hispanic respondents and make statements about each group, we need the same size sample of both regardless of their size in the population.  Thus, if we need a margin of error of 5% for each of the three groups, then the answer is  3 * ( n = 1/ m² ).

Terms:

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%. 

 Confidence level:  we always use a 95% confidence level.

1. Selecting a topic.  Typical motives include:
1. Finding out something we don't know.  This may include something local, e.g., what do people in Camden think about the new Governor's actions, something that has been unresolved in earlier research, something that hasn't been studied because it is new, etc.  This is what the authors of your book mean when they say "research always starts with wondering."
2. Another purpose that motivates research is proving to other people that what we "know" is true really is true.  This is "advocacy" research, and it can be very one-sided and lead to sloppy work.  Often this involves causal arguments, proving "why" something happens.  This kind of research may not start with "wondering" but with "arguing."
3. Answering a question posed to us by our employer or by a client, applied research.  Here someone else really chooses the topic.
2. Formulating a Research Question.  This means formulating a "statement" which will involve variables.  We have an argument or story in mind at this point.
3. Defining the Concepts.  Usually not a lot of time goes into this stage of empirical research, but some people do write articles focusing on this, e.g., what does "race" or "poverty" mean, what is the difference between "sex" and "gender"  An example:  the measurement of romantic love.
4. 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.
5. Formulating Hypotheses.  This is usually pretty easy.  There is a distinction between "null hypotheses" and regular hypotheses, which is explained on page 13.  It means testing the hypothesis that your hypothesis is not true.  Thus, you hope to "reject the null hypothesis" rather than "accept the (regular, not-null) hypothesis".  So far as I know, there is no word for the opposite of Null, it might be Substantive?  Type One Error:  accepting that a relationship exists when it doesn't.  Type two:  rejecting a relationship when it really does exist.
6. Making observations.  This is a major step unless we just get the observations from someone who already did the work.
7. Analyzing the Data.  This is "number crunching"  running data through the computer.  Of course, one can also analyze qualitative data from interviews or observations, but today even that tends to get quantified (content analysis).
8. Assessing the results.  This is really part of the analysis.  If the hypothesis doesn't work out, often researchers go back and change the hypotheses and pretend they knew all along what was going to happen
10. Publishing the findings. This assumes that you are doing "scientific" or "pure" research, much applied research is actually distributed only within the organization that paid for it.  This may be done in person, with a "power point" presentation.  Refereed publications:  you paper is sent to other specialists for review to decide if it should be published.  "Refereed journal."  Press release.   Publication can be online as well as on paper.  You publish the research so you can get credit, see your name in print, get promoted, and also so that you can inform others, and perhaps most important, so that other people can criticize or attempt to replicate it.    Usually people replicate research in the hope of overthrowing it, if you just find the same thing as before, there is less interest.  This cancels out a lot of the bias in social research, since there is usually someone with the opposite bias to correct it.

                  An example:  a study of UFO Abduction Status.            

Levels of Measurement.  What is our measurement really saying about the relationship between the values?

Dichotomous Measurement -   Two and only two categories.  Can be a natural dichotomy or a  "dummy variables" - we take a complex variable and divide it into a series of dichotomous 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.
            Illnesses:    adjustment disorder, borderline personality disorder, paranoid schizophrenic
               Crimes:   burglary, assault,  

  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 reliable and accurate or valid.

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. 
 
Scaling is when we use a number of measures, such as test scores or questionnaire items, to measure a more general concept.  We can do this by adding them up (in which case your text would call it an "index", although many people still use the form scale) , or they may be ordered from lowest to highest (in which case it is a true scale as the term is used in your book).  Your test is an example.  I just add up the points, to measure the general variable "knowledge of research methods as covered in the first part of the course."  Another approach would be to rank the items from easy to hard and see which you could do.  This is tricky, because some people can do the hard ones and not the easy ones.  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 much in sociology or CJ.  For example, 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.

February 11: 

Today we will begin with Amar Patel's Chi-Square lesson.   This covers the concept of expected frequencies and observed frequencies, and introduces the concept of "fairness", the difference statistic and the chisquare statistic.  These are applied to problems where the expected frequencies are given by a null hypothesis of "fairness".

We can apply this to any distribution where we have a theoretical reason to expect a certain result.  E.g., with two dice, each with six sides.  What results are possible and what likelihood do we have?

1.  
   
2.   *               Snake-eyes!
   
3.   **             (1 and 2; 2 and 1)
   
4.   ***           (1 and 3; 3 and 1; 2 and 2)
   
5.   ****         (1 and 4; 4 and 1; 3 and 2; 2 and 3)
   
6.   *****       (1 and 5;  5 and 1; 4 and 2;  2 and 4; 3 and 3)
   
7.   ******      (4 and 3;  3 and 4;  5 and 2;  2 and 5; 6 and 1; 1 and 6)
   
8.   *****       (4 and 4; 5 and 3; 3 and 5; 6 and 2; 2 and 6)
   
9.   ****         (5 and 4;  4 and 5; 6 and 3;  3 and 6)
   
10.   ***            (5 and 5; 6 and 4; 4 and 6)
    
11.   **              (6 and 5; 5 and 6)
    
12.  *                  Boxcars!

We will then apply the same statistic to crosstabulations where the expected frequencies are determined by the marginal frequencies.  Last class we worked with observed frequencies, row percent, column percent, and total percent.  Today we will compute expected frequencies for each cell in a cross-tabulation table, and show how the difference statistic and chisquare statistic are computed. 

We will use a simple 2 by 2 distribution as follows.  The variables are gender and opinion on an issue, each of which has two values:

25 men agreed
17 men disagreed
65 women agreed
30 women disagreed
 
 

Observed Frequencies or Obtained Frequencies	Men	Women	total
Agree	25	65	90
disagree	17	30	47
total	42	95	137

    We can compute expected frequencies, based on the null hypothesis that men and women do not differ intheir opinions.  We can compute these knowing only the marginal or total frequencies.  The easy way to compute them is to multiple the row total for each cell by the column total for that cell, then divide by the grand total.  Another way would be to convert the row totals to proportions, then multiply then by the column totals.  Expected Frequencies - rt *ct /gt

Expected Frequencies	men	women	total
agree	90*42/137=27.59	90*95/137=62.41	90
disagree	47*42/137=14.41	47*95/137=32.59	47
total	42	95	137

  What would we get if we used the expected frequencies to make acolumn percentage table?  The percentages would be the same in each column (except for rounding error).  That is the point of expected frequencies, they are frequencies  we would get if all the columns were the same on percentage term.

We can use the expected frequencies to compute the "difference statistic" as described by Patel.  This tells us how much each cell is off from what was expected.  As you can see, each cell is off by 2.59, in either the positive or the negative direction.   This is a rough measure of how much our observations differ from the expected, plus or minus 2.59, but it is not widely used.  The sum of the differences is zero because the negatives cancel out the positives.

The statistic that is used is the chi-square statistic.  This is designed to give more weight to bigger differences and to make all differences positive so they can be added up to a number that can be used for probability testing.  We have probability distributions for chi-square, which enables us to tell the likelihood that the difference could have appeared by chance.   Chisquare is computed by squaring the differences between the observed (Fo) and expected (Fe) for each cell, then dividing them by the expected for that cell, then adding them up.

To get the chi square, we add up the computations for each cell  = .2431+.1075+.4655+.2058 = 1.0229.   Programs such as Microcase compute this for us.  We can also get  the chi square typing the observed frequencies and into the WEB chisquare calculator (using the version without the "Yates correction").  The result is 1.023.  The computer this tells us that the result is not "statistically significant" by chi-square test.  In the days before computers, we looked these up in a table in the back of a statistics book.

To see these tables, open the EXCEL 2 by 2 chi-square calculator I have prepared. It has all the tables:  observed frequencies, row percents, column percent, total percent, difference statistic, chi square.  In this spreadsheet, if we change the numbers in observed frequencies table, the other numbers will change accordingly. 

In computing percents, take our observed frequencies and put them in a contingency or cross-tabulation table.  For example, if we ask men and women an agree/disagree survey question, we might get the following results:

       55  men agreed
       33 women agreee
       27    men disagreed
       42 women disagreed

  The first  thing we do is put these into a contingency or cross-tabulation table.  We usually put the Independent or (causal) variable in the column and the dependent variable in the row .  It is best not to have too many categories on either variable, unless you have a very large number of cases.  This is the smallest possible table, a 2 by 2 table.

Observed Frequencies	men	women	Total
Agree	55	33	88
Disagree	27	42	69
Total	82	75	157

There are three ways to do the percents.

1. What percent of the men agreed?  
2. What percent of the women disagreed? 
3. What percent of those who agreed were men?  
4. What percent of those who disagreed were women? 
5. What percent of the respondents agreed? 
6. What percent of the respondents were women?

Here is the kind of table we would put in a report.  It gives the column percents because the column variable is the Independent Variable.  For most purposes, the percents are based on the Independent Variable:
 

Column Percents	Men	Women	Total
Agree	67.1%	39.1%	52.2%
disagree	37.5%	60.9%	47.8%
Total	100%	100%	100%

Other concepts we can consider are: poverty, power, crime, murder, race, IQ, liberalism/conservatism, homelessness. Or we could look at Personality Types as defined by Carl Jung and Measured by Isabel Meyers-Briggs.

September 10:  No regular class was held.  Humansubjects movie was offered and a laboratory session.

September 8:  Ethics of research with human subjects.  We went through the material in the course on WEBCT.