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					Dr. Ji                                                                       Handout 1
Soc 332
Methods of Social Research

                        CHAPTER 1 CONCEPTS AND THEORIES
____________________________________________________________________________________

Social research method

       Social research method describes the way that sociologists do scientific research. It
       covers a variety areas, primarily consisting of concepts and theories, steps in research
       process, measurement, censuses and samples, causation and causal models, research
       designs, survey research, field research, experimental research, content analysis, and
       writing report.

Difficulties in Social Science

       Using scientific methods to study human beings can minimize the mistakes because
       common sense is not scientific and often leads to wrong conclusion.

       Unlike natural science, behaviors of human beings are in constant change and very
       variable.

       It is hard to have experiment on human beings.


Description and explanation

       Social sciences are guided by two goals:
       1) Description - to describe some aspects or characteristics of the world.
       2) Explanation - to explain why things are the way they are.

           Social scientists use concepts and theories to describe and explain parts of reality in
           society observed.

Concepts
      A building block of science. They are abstract terms that identify a class of things to be
      alike (such as “animals,” referring to a huge class of numerous and various animals.)
      Concept must be clear and apply to all possible members of that class.

       1       Parsimony (Abstract)
               The law of parsimony reads as follows:
               Theories always attempt to explain the most with the least.
               That is, to explain as much as possible with a theory that is as simple as possible.
               The more abstract the concepts, the better the concepts.
                                                                                                    2
       2       Utility
               The search for good concepts cannot be guided by truth because definitions are
               neither true nor false. Scientific definitions are regarded as names that are simply
               assigned to something. Therefore, the ultimate test of concepts lies in their utility,
               namely, their usefulness in constructing efficient theories.

       3       Clear boundaries
               Efficient concepts must have clear boundaries that eliminate ambiguity about
               what a concept includes and does not.

               School performance             Marital quality
               Religiosity                    Racial discrimination

               Are these concepts are clear or intertwined or overlapped?

       4       Naming is not explaining
               Concepts are useful for classification but do not explain anything. Simply to name
               things does not tell us why, what, when, or how.

               Powerless                      Normless
               Isolation                      Deviance
               Suicide

               Are these concepts of naming or explanation to some social phenomena?

Theories
       A theory refers to a particular kind of statement designed to explain something of general
       interest and application. These statements have two features: abstract and falsifiable.

       1       Abstract
               Theories are abstract statements that say why and how some sets of concepts are
               linked. Their purpose is to explain some portion of reality.

       2       Falsifiable - it is possible to say what evidence would show them to be false.
               That is, a real theory directs our attention to observations that would prove the
               theory to be false which implies empirical predictions and prohibitions.


Empirical research
       Empirical research links theory to observations. It means to “observe through the senses.”
       Although theories cannot be touched that only exist in our mind, they can guide and tell
       us that certain observable things would or would not happen. These certain things can be
       touched and observed by empirical studies, which in turn, verifies theory.
                                                                                                    3
Concepts and Indicators

Concepts
      Concepts are the abstract terms that identify a class of “things” to be regarded as being
      alike. Concepts cannot be observed. Only the specific instances of concepts can be
      observed.

       Social class, for example, is a concept that refers to a group of people who are alike in
       terms of prestige and wealth.

       Occupation
       Education
       Income

Indicator
       An indicator is an observable measure of a concept.

       Income, for example, is an indicator to measure social class.
       Number of prays a week, is an indicator of religiosity.
       GAP is an indicator of school performance.


Theories and hypotheses

A theory
       A theory is an abstract statement about reality.
       A logical deductive-inductive system of concepts, definitions, and propositions, which
       states a relationship between two or more selected aspects of phenomena and from which
       testable hypothesis can be derived. Theories in sociology are intended to be descriptive,
       explanatory, and predictive of phenomena of interest to discipline and to its individual
       practitioners.

       Sources of theories
              1       from our imaginations and observations.
              2       desire to explain something motivate theories
              3       research results formulate theories and modify theories


The wheel of science
      Dealing with the relationship between a theory and the empirical research.

       There are two different modes of reasoning, deduction and induction. The former is
       commonly utilized for description while the latter is often used for exploration research.
                                                                                               4

       1      Deduction logic (tui-lun)- from abstract to concrete
              From theory to hypothesis
              From hypothesis to observation

              From general to specific
              From known principle to an unknown but observable conclusion

       Major premise:        Conservatives oppose abortion.
       Minor premise:        Edward is a conservative.
       Conclusion:           Edward opposes abortion.

       2      Induction logic (gui-na)- from concrete to abstract
              From observation to empirical generalization
              From empirical generalization to a theory

              From the specific to the general
              From a set of observations to a general conclusion
              From a general conclusion to a principle

       Observation:          Bill, Mary, Tom, Robert, Peterson are from Wisconsin.
                             They all have Catholic as their religion.

       Conclusion:           People from Wisconsin are primarily Catholic.


Correlation
       Correlation means “to vary in unison” or “to go together.” That is, when one variable
       changes, the other one will change accordingly, or vise versa.

       Correlations can be positive or negative. A positive correlation exists when two variables
       move in the same direction. A negative correlation exists when one variable moves in one
       direction while the other variable goes in the opposite direction.

Hypothesis
      A hypothesis is an expected but unconfirmed relationship between two or more variables
      under study.
      While a theory specifies a relationship among concepts, a hypothesis specifies a
      relationship to be observed among indicators.

       Example
       A theory     Social class defines one’s attitudes and behavior – abstract and hard to
                    observe.
       A hypothesis People with higher education are more likely to have higher income –
                    observable and testable.
                                                                                                       5

The Null Hypothesis
      A null hypothesis states that a relationship between two variables is unrelated. Null
      hypotheses are always stated in the form denying the prediction derived from the theory.
      Because whether to reject or to accept the predicted relationship, one will never be sure to
      prove the theory. The finding of a test may support the theory while the finding of
      another test may deny the theory. Hence we commit either Type I or Type II error.

         In comparison, a research hypothesis states that a predicted relationship between two
         variables is related. A research hypothesis assumes the statement of a relationship that is
         in the direction derived from a theory.

         Type I error - when a null hypothesis is rejected, one commits Type I error.
         Type II error - when one fails to reject a null hypothesis, one commits Type II error.

Exploratory Research
       Engagement in speculative research that make systematic observations of uncharted or
       little known phenomena in order to get an initial sense of what is going on.


Pure, Applied, and Evaluation Research

Pure research
       The primary motive is directed by the desire to increase knowledge without regard for
       practical application. Such research usually involves theory testing, to refine or extend a
       theory.

Applied research
       The primary purpose is to serve practical needs. Applied research may or may not
       involve theory testing (most does not).

Evaluation research
       It is to assess the effectiveness of a program, policy, product, or procedure, and is usually
       commissioned by government agencies, business, or organizations such as schools,
       churches, or hospitals. This study may have an immediate practical payoff for a specific
       program but may add little or nothing to the stock of fundamental knowledge.


Dr. Ji                                                                        Handout 2
Soc 332
Spring 2002

                CHAPTER 2 STEPS IN THE SOCIAL SCIENTIFIC PROCESS
____________________________________________________________________________________

Step 1          Selecting a Topic
                                                                                                 6

What is a valid sociological topic?
       On the macro level, one may want to know such broad matters such the military, race
       relations, and multinational corporations. On the micro level, one studies such
       individualistic matters as how people interact on street corners, how people decorate their
       homes at Christmas, or college students’ dating patterns, etc. Any topic in social
       environment will be a valid topic.

What do you want to know more about?
      Following one’s curiosity
      Following one’s drive of interests
      Based on available funding
      Consider the availability of data source

Topic

Maternal Influence and Children’s Marital Quality
Parental Educational Attainment and Children’s Performance at School
Parents Have Influence on Children
Religiosity and Law Abiding


Step 2         Formulating A Research Question / Defining the Problem

Specify exactly what you want to learn about the topic.
       The goal is to develop research questions for empirical investigation
       A research question needs to be transformed into a statement
               - a preliminary to the formulation of a hypothesis

         Example:     (Do) Parents’ marital behaviors influence their adult children’s marital
                      quality (?)

                      (Do) Parents’ socioeconomic status influence their children’s performance
                      in school (?)

Research questions are not research hypotheses
       Research questions include some concepts
       Research hypotheses need to be operationalized indicators
       Concepts need to be clearly defined prior to formulation of hypotheses
       Appropriate indicators of the concepts must be selected and defined for hypotheses

         Example:     What are the parental marital behaviors?
                      What is adult children’s marital quality?
                      What is the parents’ socioeconomic status?
                      What do you mean about children’s school performance?
                                                                                                7

Step 3         Defining the Concepts

Many different concepts used by researchers
      Some are generally understood while others are not
      Socioeconomic status - education, income, occupation
      Religious beliefs - Protestant, Catholic, Jewish, Others, None
      Social class - based on income, or education, or prestige, or wealth, or power

Many competing definitions and one has to make the selections
      Marital quality - marital happiness, relationships, togetherness, satisfaction,

Develop new concepts
      Family violence - wife abuse, child abuse, elderly abuse, husband abuse?
             Maltreatment, malnutrition, throwing objects, battering, spanking, hitting, killing,
             murders, rapes, etc.

Step 4         Operationalizing the Concepts

Operationalizing the Concepts by appropriate indicators
        the process of developing effective and feasible indicators of the concepts
       “Marital quality,” “religiosity,” “powerless,” “isolation,” “school performance,”

Marital quality
       “Marital satisfaction between husbands and wives”

Social stratification
        Slavery system, Caste system, Estate system, Class system
        Class system-power, prestige, property
        Property - income, wealth

Religiosity
       Number of prays/day/week, frequency to church, regularity to church, perception of God
       as Mom, Dad, good deeds to people/ride/donation, etc.

School performance
       GAP, attendance, rewards, presentations, papers published, leadership,

Use standard indicators
       Concepts                    Indicators
       Education attainment - years of school, degrees earned, <HS, HS, Col, G, Phd,
       Elderly              - 65 -74, the young old; 75-84, the old; 85+, the oldest old
       Demographic characteristics - age, sex, race, marital status,

Develop new indicators
                                                                                                   8
         Social networks - relatives, neighborhood, community, friends, colleagues,
         Family ties - parents, siblings, grandchildren, relatives, neighborhood, etc.

Use the best possible indicators
       Most surveys provide sets of questionnaire items already defined.


Step 5          Formulating the Hypothesis

Transforming research questions into testable hypotheses

Hypothesis
      The expected but unconfirmed relationships between variables under study

         Hypothesis I           Higher the marital satisfaction for parents, higher the marital
                                satisfaction for children (MQ)

         Hypothesis II          Marital relationships between fathers and mothers will influence
                                children’s marital relationships (MQ)

         Hypothesis III         When fathers have higher educational attainment, children’s GPA
                                scores are also likely to be higher (Edc)


Step 6          Making the Observations

Hypotheses direct our attention to the specified behavior of each indicator

         By direct observation - collecting data, interviewing people, participation,

         By looking at data set, questionnaires, etc.


Step 7          Analyzing the Data

Make data analysis through appropriate statistical methods to check out the hypotheses
       Choosing a appropriate method
       Recoding variables
       Handling missing data
       Making statistical analysis
Step 8        Assessing the Results

Are hypotheses supported or rejected by the results?

         If supported, write report for publication
                                                                                               9

         If not supported or rejected, retrace all steps to find errors,
                 Is the hypothesis consistent with the theory?
                 Are concepts properly defined?
                 Do indicators measure the concepts?
                 Are these the correct units of analysis?
                 Were data properly collected?
                 Was the analysis done correctly?

                 Find problems by oneself
                 Publishing the paper to let others find problem for you


Step 9           Publishing the Results

All sciences are public activities and social activities

Written words are precise and have enduring record

Publications link past research to present and present to future

Publications stimulate effort for replication


Step 10          Replication Research

Replication Research
       By repeating previous research to check on the results
       Using different set of units of analysis, indicators, time, place – human beings are more
       variable/changeable.
               e.g. Religious commitment causes young people to be law abiding which is found
               in Richmond, and California; the finding is confirmed in Oregon; but findings in
               Atlanta and Utah denied; Eventually, findings in Arizona again verified the
               previous finding. Conclusion: Religions do reduce delinquency except under
               special circumstances.

         Replication reveals flukes (by luck) that may occur leading to false findings

         Replication helps to weed out research that is careless, incompetent, or dishonest.


Dr. Ji                                                                       Handout 3
Soc 332
Spring 2002

                                    CHAPTER 3 MEASUREMENT
                                                                                                  10
______________________________________________________________________________

Variables

Constants
      Characteristics or aspects of things being studied do not change. Constants take the same
      values constantly. Constants cannot explain variables. Constants that do not change
      cannot cause things to change. The speed of light, for example, is a constant.

Variables
       Characteristic or aspects that take different values among the things being studied and
       thus can vary.

       Dependent variable - presumed effect of an independent variable
       Independent variable- presumed cause of the dependent variable

       Other variables: control variable, intervening variable, suppressor variable, antecedent
       variable, etc.

Levels of Measurement
       Measurement is the assignment of numbers or labels to units of analysis to represent
       variable categories. The various meanings of these numbers are what is meant by the
       levels of measurement.

Nominal/Categorical
     Cases are sorted into two or more categories (gender, race, religion, place of residence,
     party preference).
     Lowest level of measurement
     Categories are mutually exclusive and exhaustive
     Numerals are merely labels or codes for convenience and quantitatively meaningless
     (1+2=Democrat plus Republican?! or 4+5 > 1+2?!)

       Example:       Party Preference*
                      1      Democrat
                      2      Republican
                      3      Independent
                      4      Other
                      5      No Preference

                      Religious Preference*         Religious Preference
                      1      Protestant             1      Protestant
                      2      Catholic               2      Catholic
                      3      Jewish
                      4      Other
                      5      None
                                                                                                    11
                       Place of Residence              Place of Residence*
                       1      Urban                    1      Urban
                       2      Suburban                 2      Suburban
                       3      Rural                    3      Rural, farm
                       4      Farm                     4      Rural, nonfarm

                       Marital Status                  Marital Status*
                       1      Married                  1      Married
                       2      Single                   2      Single
                       3      Divorced                 3      Divorced
                       4      Widowed                  4      Widowed
                                                       5      Others

               Exhaustive
               The overall categories must be exhaustive, meaning that there must be sufficient
               categories so that all persons, objects, or events being classified will fit into one of
               these categories.

               Mutual Exclusive
               Categories must be mutually exclusive, meaning that the persons or things being
               classified must not fit into more than one category.


Ordinal
       It is the process of ranking cases in terms of the degree to which they have any given
       characteristics. Numbers indicate the rank order of cases on some variable.
       Cases are sorted into categories.
       Categories can be rank ordered.

       Example
       An attitude toward abortion can be measured as the following ordinal variable:

       1)      Strongly disagree
       2)      Disagree
       3)      No opinion
       4)      Agree
       5       Strongly agree

       Marital satisfaction variable is measured at another ordinal variable:

       1)      Very unsatisfied
       2)      Unsatisfied
       3)      Average
       4)      Satisfied
       5)      Very satisfied
                                                                                                 12

       Higher scores represent more satisfaction with marital lives.

Interval
        It is the process of assigning a score to cases so that the magnitude of differences between
        them is known and meaningful. Interval variable is measured using some fixed unit of
        measurement such as the dollar, the year, the pound, or the inch. Numerals represent
        mathematical equivalences on the variables being measured. Temperature: 0,10, 20,
        …90, 100, is another example.
        A more precise form of ordinal
        Categories can be rank ordered
        Fixed unit of measurement
        Interval between categories is of equal quantity

       Example

   Mothers’ educational attainment in years is indicated by a set of scales:

   (0) no education;
   (1) one grade;
   (2) two;
   (3) three;
   (4) four;
   (5) five;
   (6) six;
   (7) seven;
   (8) eight;
   (9) nine;
   (10)       ten;
   (11)       eleven;
   (12)       twelve grades, completed high school;
   (13)       one year of college;
   (14)       two years of college;
   (15)       three years of college;
   (16)       four years of college;
   (17)       17+, 5 plus years of college.

   Higher scores represent higher level of education.
                                                                                                13


   Mothers’ income in dollars

   It is indicated by the actual and the around number of dollars ranging from the lowest level to
   the highest level:
        $800
        $801
        $802
        …
        …
        $90,000
        …
        $99,997

        Higher scores represent higher level of income.


Ratio
        Like an interval variable, it has a standard unit of measurement.
        Unlike an interval variable, a ratio variable has a non-arbitrary zero point.
        The zero point represents the absence of the characteristic being measured.
        Having equal intervals between categories
        Having meaningful zero point
        Ratio variables convey the most information and thus have the highest level of
        measurement.
        The interval and ratio variables are often used interchangeably because there are not
        many truly interval variables in social sciences.


        Example

        World population birth rate   = 2.2%         (2001 world popu data sheet)
        World death rate              = .9%
        World growth rate             = 1.3%
        World percent urban           = 46%
        United States birth rate      = 1.5%
        United States death rate      = .9%
        United States growth rate     = .6%
        United States                 = 75%
                                                                                                 14
        Levels of Measurement of Variables
       __________________________________________________________________
       Level               Rank order to values? Fixed unit of measurement
       __________________________________________________________________
       Nominal                     No            No
       Ordinal                     Yes           No
       Interval                    Yes           Yes
       Ratio                       Yes           Yes



       Information Provided by the Four Levels of Measurement
       ________________________________________________________________________

       Information
       Provided            Nominal      Ordinal      Interval     Ratio
       ________________________________________________________________________

       Classification      X            X            X            X
       Rank Order          -            X            X            X
       Equal Interval      -            -            X            X
       Absolute Zero       -            -            -            X
       ________________________________________________________________________


Units of Analysis
       The entities to which our theory and research applies
       Things a hypothesis directs us to observe. They can be human beings or non-human
       beings; can be individuals or groups or aggregates.

       Examples of units of analysis:
             Individuals, students, married couples,
             Basketball teams, court cases, stage plays,
             Towns/cities, states or provinces, nations/countries, etc.


Aggregate Data
      Individual scores or individual respondents are combined into larger groupings.
      Data of this kind in which cases are larger units of analysis are called aggregate data.

       Since we cannot question our cases such as a city, towns, schools, states, or nations, we
       create measures by summing or aggregating data on individuals within the larger unit.
       The population in Eau Claire, for example, is simply the sum of the individuals within its
       boundaries. Usually the raw numbers are turned into rates. Population density in Eau
       Claire, for example, is the ratio between population and land areas per square mile.
                                                                                                       15
        Examples for more aggregate data are birth rate, death rate, migration rate, murder
        rate, employment rate, dependency ratio, infant mortality rate, divorce rate, marriage rate,
        urbanization rate, etc.

        Sources of medical care of the aged by gender and region in Hanan
        _______________________________________________________
                      Male                   /      Female
        ______________________________/_________________________
                      Urban           Rural /       Urban          Rural                        Self
        593           8273 /          3140          9658
                      9.0             92.7 /        42.5           96.5

        Half-self       785             270     /       2299            318
                        11.9            3.0     /       31.1            3.2

        Public          5200            384     /       1946            33
                        79.1            4.3     /       26.4            .3

        Total       6578        8927 /      7385        10009
              42.4        57.6 /      42.5        57.5
        ________________________________________________________


Ecological Fallacy

        Either inferring individual characteristics from analysis of aggregate data, or inferring
        aggregate characteristics from data for individuals, entails a logical error, called
        Ecological Fallacy.

        Ecological Variables - If the larger units are spatial or geographic areas like states,
        provinces, or countries, the aggregate data are ecological data and their variables are
        ecological variables. Examples are murder rate, percent urban, average income, and
        percent Hispanic in the 50 states.


The quality of Measures

Reliability
       A variable is reliable if it is consistent - if repeated observations give similar results.
       Reliability deals with consistency.

        The extent to which the measurement of instrument procedures produces consistent and
        stable results on repeated trials.
                                                                                                                                             16
    Test-retest reliability
            The same cases are measured at two different times, and correlation between the
            two scores is the estimate of the reliability of the measure

    Alternate forms
           Design two tests with same level of difficulties but administer each test to the
           same individuals at different times. The correlation between two scores reflect the
           reliability of the variable

    Split halves
Indicators are divided by two equivalent half and both forms are administered in one test.

The correlation between the scores on the two halves measures the reliability.



    Internal consistency
a) Internal consistency seeks to determine if all of the items in a test are measuring the same

thing. b) Each indicator is assumed to be a good measure of the same target concept. c)

Cronbach’s alpha (α) is the mostly used approach. d) The value of alpha ranges from 0 - 1.

“0” means not reliable; “1” means perfectly reliable; e) α = .7 is accepted as sufficiently

reliable for use.




            Example:               Children’s Gender Role Belief (CGRB)

            12013 Most of the important decisions in the life of the family should be made by the
                  man of the house.
            12014 When there children I the family, parents should stay together even if they don’t
                  get along.
12015       It is perfectly all right for women to be very active in clubs, politics, and other outside activities before the children are
grown up.
12016       There is some work that is men’s and some that is women’s and they should not be doing each other’s.
12017       A wife should not expect her husband to help around the house after he comes home from a hard days work.

            12018 A working mother can establish as warm and secure a relationship with her
                  children as a mother who does not work.
            12019 It is much better for everyone if the man earns the main living and the woman
                  takes care of the home and family.
            12020 Women are much happier if they stay at home and take care of their children.
                                                                                                    17
              12021 It is more important for a wife to help her husband’s career than to have one
                    herself.
              12023 A man’s family should always come before his career. Would you say____

                              1)      Strongly agree
                              2)      Agree
                              3)      Undecided
                              4)      Disagree
                              5)      Strongly disagree



              α = .7357 (12013, 12019, 12020, 12021)


       Inter-rater reliability/Inter-coder reliability
               Using the same measuring instrument, two or more independent raters/coders
               place observations into the same category. If the measuring instrument is reliable,
               different observers should produce consistent results.

              Example
              “How do you code the following into two categories?”
              Marital Quality (MQ):

              Marital problems
              Marital satisfaction
              Marital disagreement
              Marital happiness
              Marital conflicts
              Marital enjoyment
              Marital abuse
              Marital togetherness
              Marital employment*
              Marital division of labor*
              Marital relations*
              Marital finance*


Validity
       The degree to which the empirical indicators measure what it purports to measure.
       The extent to which the indicators measure what you intend to measure.

       Face validity
              The variable obviously measures the concept judging based on the face of it.

              “How old are you?”
                                                                                           18
       “ When were you born?”
       Are questions obviously a valid measure for measuring the age of the respondent.




Convergent validity
      Indicators of the same concept should be highly and positively correlated with one
      another. Indicators that represent the same concept should converge on a single,
      underlying, empirical base.

       What are your test scores in math, physics, chemistry, biology, statistics, and
       computer science?

       Are these scores are correlated to each other?
       Are they valid measures to measure your GPA and/or IQ?

Criterion validity
        Validity is established by determining how strongly the measure is correlated with
        a criterion variable that is external to the measurement instrument. It is to
        compare an indicator with a criterion one that is known as valid.

       Education, Income, and Occupation are widely accepted criteria variables to
       measure one’s socioeconomic status (SES).

       Can I use Education as a variable to measure class status?
       Can I use Income as a variable to measure prestige?
       Can I use Occupation as a variable to measure power?

Construct validity
       This validity is rooted in its ability of the measure to work in theory. Validity of a
       measure is established by seeing if it meets the underlying assumptions. In other
       words, the validity of a measure is assessed through its relationship to other
       measures consistent with theoretical derived hypotheses.

       Research Questions:
       Do fathers impact their children’s temperament?
       Do children’s occupation resemble their parents?

       The validity of the measures is established by whether or not these measures are
       consistent with the theories: The Resource Theory, Socialization Theory, and The
       Theory of Intergenerational Effect.

Cross-case comparability
                                                                                                 19
                Valid measures must be comparable across cases. That is, questions must be
                understood the same way by all respondents. If the same question is understood
                differently and responded differently, meaningful comparisons are hard to make.
                (This is especially difficult when being used across nations).

                “Please tell me in round numbers about the amount of income you received last
                year including your family incomes.”

                $0, 1, 100, 1000, 10, 000, 20,000, … to $ 99,000.

                Does the income include spouse’s income, children’s, other income like property,
                stocks, etc?

                If understood differently, then how could one make comparisons meaningfully by
                gathering this kind of data?



A Variety of Measures

Survey questions and indexes

         Survey questions
                Surveys are the most common method for obtaining information in social science.
                Survey questions not only ask characteristics of the respondents but also questions
                about opinions and attitudes and self-reports of behavior such as drinking,
                shopping, reading, voting, attending church, etc. They meet the specific purpose
                of researchers.

         Indexes
                A number of similar questions that are designed to measure the same concept can
                be combined together known as an index. This index is based the assumption that
                more measures of the same thing will yield more sensitive measurements.

Scales
         Scales and indexes are often used interchangeably.
         Both provide a rank ordering of respondents along a continuum representing the traits of
         the target of measurement; both rely on multiple indicators of the traits to form composite
         measures. True scales weight the importance and intensity of the indicators used in the
         construction of the scales (Guttman).

         An index and a 6-point scale to measure Religious Fundamentalism:
         An index of Religious Fundamentalism:
         a)     Religion is a very important part of life
         b)     After I do something wrong, I fear God’s punishment
                                                                                                    20
       c)      People who are evil in this world will suffer in Hell
       d)      Gods knows everything a person does wrong
       e)      In the end, God punishes those who have sinned

       Each item was scored on a 6-point scale:
       1= strongly agree
       2= agree
       3=somewhat agree
       4=somewhat disagree
       5=disagree
       6=strongly disagree

Measures based on observations (Detailed in Chapter 10)
      Measures based on actual recorded observation of the phenomenon of interests.
      Observation provides both verbal and nonverbal behavior.
      Observation can yield many measurements in other modes of research.

Rates and other aggregate measures
       When units of analysis are aggregates such as towns, schools, states, or nations, we apply
       measures by summing or aggregating data on individuals within the larger units.

       Rates
               A rate is a proportion or ratio and is created by dividing one variable by another
               variable.
               Rates create a common basis for comparison across aggregate units.
               Rates are standardized measures because they express and compare differences
               between scores out of 100. They relate number of cases occurring to a 100.

Coding content to get measures
      When units of analysis are not humans or aggregates of humans such as newspapers,
      movies, songs, or stories, they require a coder to make judgments about how cases should
      be categorized. To get measures, most researchers use content analysis.

       Content analysis
             A technique of transforming non-quantitative verbal, visual, or textual material
             into quantitative data to which standard statistical analysis technique can be
             applied.
             UWEC Campus             001
             Chippewa Valley         002
             Wisconsin Daily         003
             US Today                004
             Washington Post         005
             …                       …
                                     200
                                                                                                  21

Dr. Ji                                                                           Handout 4
Soc 332
Spring 2002

                         CHAPTER 4 CENSUSES AND SAMPLES
______________________________________________________________________________

     To learn basic concepts about census and samples
     To explain how sampling works
     To know the bases of random sampling


                 Census

     The total process of collecting, compiling, and publishing demographic, economic, and
       social data pertaining, at a specified time or times, to all persons in a country or delimited
       territory (UN).

     A census is an official count of the population and recording of certain information about
       each person in a country.

     Data are collected from all cases or units in the relevant set.


Early censuses

     Egypt, Babylonia, China, India, and Rome
     Large-scale counts were registered as early as 5 B.C but not real-sense census.
     Caesar Augustus, the Emperor of Rome, ordered that a census be taken of his empire.
       Everyone was required to return to his ancestral home (based on Bible).
     To establish how many people and households there were within the territory under the
       ruler’s control and how much tax would be expected to pay.
     To report the number of persons in every household and to list their significant
       possessions

Modern Census

       Starting from late18th and early 19th century

       Sweden is the first European nation to have census                1749
       Denmark and several Italian states (before uniting of Italy)      1700s
       The United States                                                 1790
       England                                                           1801
       Lebanon                                                           1932
                                                                                                   22
       India                                                          1881
       China                                                          1953

       78% of world’s population was enumerated by censuses           1953-1964
       81% of countries have or are planning to take census           1995-2004


Census of the United States

       1       First census was conducted in 1790
       2       A census is taken since 1790 for every 10 years (22 times)
       3       The first 100 year-censuses were taken by US marshals
       4       The Census Bureau took the job since 1902
       5       The information asked is from simple to complex- from age, free or slave, name,
               aged 16 year older, white male or females, and other persons, to basic
               demographic and housing characteristics, to all other information such as sex,
               racial, ethnic, year of birth, marital status, nationality origin, literacy, death,
               suicide and murder, income, net worth, occupation, and place of birth, etc.
       6       Who is included in the census?
               Two ways:
               de facto population – which counts people wherever they are found on the
               census day;
               de jure population – which counts people where they legally belong to
               regardless whether they were there on the day of the census.
       7       The United States adopts the method in between de facto and de jure, and
               includes people in the census on the basis of Usual Residence, which is roughly
               defined as the place where a person usually sleeps. College students may be
               counted at the college rather than counted at their parents’ houses.


Purpose of census

       1       The government wants to know the accounts of taxpayers, laborers, and solders.
       2       To study population process, change, and to make planning.



Population

     Refers to all members of a nation.
     It is not limited to human beings. In statistics, a population refers to all units constituting
       a set which is delimited (ding-jie-xian).
     A population also refers to as the universe of units where the word of universe means “all
       things.” “All persons in India” defines a universe as does “all students in UWEC” or “ all
       women in WI.”
                                                                                                  23


Samples
      A subset of population
      A selected section of a population

Random Selection
      It is a process in which every case has an equal chance of being selected.
      Random sample and probability sample are used interchangeably because they based on
      random sampling principle that all cases have a known probability of being selected.

Statistics and parameters
        A statistics is a characteristic of a sample while a parameter is a characteristic of a
        population. A statistic is a value to describe a sample while a parameter is to describe the
        population. A statistic is the estimate of the parameter. An average income for Water ST,
        for example, is a statistic to describe residents in Eau Claire sample while the average
        income for all residents of America is a parameter.

Confidence interval and levels

       We use a sample statistic to estimate a population parameter. Population mean may be
       greater or less than the sample mean. To estimate a population mean, we must establish a
       range around the sample mean within which we think the population mean lies. This
       range is called confidence interval.

       It is a range around the sample mean within which population mean lies.
       It is a range around the sample mean to predict the population mean.
       It is a range within which we use a statistic to predict a parameter.

       Two levels of confidence interval are commonly used: 95 and 99 percent.

       95 percent confidence interval =  ± 1.96
       The chances are 95 out of 100 that we are confident that population mean () lies within
       this range-the confidence interval.

       99 percent confidence interval =  ± 2.58
       The chances are 99 out of 100 that we are confident that the population mean () lies
       within this range-the confidence interval.

       95 percent of the scores lie within 1.96 standard deviations of the mean. Thus we use
       this knowledge to find a confidence interval (Transparency).

       99 percent of the scores lie within 2.58 standard deviations of the mean. Thus we use
       this knowledge to find a confidence interval.
                                                                                                    24
       = standard errors



       Example

       The mean for watching TV daily is 2.90 with a standard deviation of  = 2.14, a sample
       size of N=1940, and a standard error of  = .049. Therefore,

       Standard Error formula=  = / N = 2.14 /1940 = 2.14/44.045 = .049

       = 2.90
       = 0.049
       95% standard deviation = 1.96
       99% standard deviation = 2.58

       95 percent confidence interval =  ± 1.96
              = 2.90 ± 1.96 (.049)
              = 2.90 ± .096
              = 2.80 to 3.00

       Thus the 95 percent confidence interval is between 2.80 and 3.00. The chances are 95 out
       of 100 that population mean () lies within this range.


       99 percent confidence interval =  ± 2.58
              = 2.90 ± 2.58 (.049)
              = 2.90 ± .126
              = 2.77 to 3.03

       Thus the 99 percent confidence interval is between 2.77 and 3.03. The chances are 99 out
       of 100 that population mean () lies within this range.


Statistically significance and significant levels

       Statistically significance is to test the probability that a null hypothesis being true is very
       small (p< .05). Therefore, we reject the null hypothesis and conclude that there is a
       relationship between the variables under study. And the relationship is existent in the
       population from which the sample was drawn.

       Statistically significance implies that the relationship found in the sample is likely to
       occur in the population from which the sample was drawn.

       Three significant levels:
                                                                                                  25

      P< .05          the probability that a null hypothesis being true is small (p< .05).

                      It implies that the probability that the research hypothesis being true is big
                      (p  .95).

      p< .01          the probability that a null hypothesis being true is small (p< .01).

      p<.001          the probability that a null hypothesis being true is small (p< .001).


Samples versus census

      A CENSUS                                       SAMPLE

      Yield parameters                       Yield statistics
      Full coverage                          Selective coverage
      Time consuming                         Time economic
      More expensive                         Less costly
      More person involved                   Limited personnel
             Long period collection                  Short period result
      Wider scope                            Specific scope
      More governmental                      More individual objective
      More comprehensive information         Partial and particular information
      More standardized methodology          More diversity methodology


Random Samples

      Every member of the population to be sampled has an equal chance of being included.
      That is, everyone has a known probability of being included.


Simple Random Sampling

      Based on the principle that all members of the population have an equal chance of being
      selected.

      1        like a lottery technique-all names included in the population-units drawn one by
               one
      2        published books of tables of random numbers
      3        using computer to generate lists of random numbers


Systematic Random Sampling
                                                                                        26

Selecting the first number randomly and then taking the Nth case until the end of
the list is reached. The steps are:

1       having a list of the entire population
2       dividing the total number by the sampling number
3       selecting the first number randomly
4       taking the Nth case until the end of the list

Example

population            = 20,000
sample                = 1,000
sampling fraction     = 1000/20000 = 1/20

Suppose the first case is randomly selected as 11, then the second would be 31, the third
is 51, the fourth is 71, that is,

11, 31, 51, 71, 91, 111, 131, …

Be sure the first number must be randomly selected in order to meet the principle of
every case having an equal chance of being selected.


        Telephone Polls

1       less costly
2       faster
3       efficient
4       wide coverage
5       97% of all US homes have phones
6       phone umbers are unique with area code, prefixes representing specific
        geographic areas
7       can be selected randomly
8       omit those having no phones or inaccessible to those with phones

Steps
1       identify the prefixes to locate geographic areas targeted
2       generate a list of all phones
3       eliminating non-residential or non-working numbers
4       use a random number generator to produce a list of phone numbers that have these
        prefixes
5       call one by one for phone interview with recorders
                                                                                                                        27
Stratified Random Samples

       A targeted population is divided into several strata or sub-populations and then samples
       are selected independently from each stratum.



       Example based on Individual data

       Strata based on characteristics of students in UWEC. The target population is UWEC
       students.

       1        Know all names and addresses of the students
                the population is stratified prior to drawing samples
       3        all students are divided into four subpopulations as freshman, sophomore, juniors,
                and seniors, and each subpopulation are further divided into two groups: male
                students and female students
       2        know the actual proportion of the each stratum in the population so that it is
                possible to draw separate samples from each stratum
       3        draw from each stratum with appropriate proportion/separate samples


       Example based on the aggregate data (omitted)
       Stratified samples are also used in aggregate data
       Studying race segregation in cities, for example, may sample only one stratum.
       1         first stratifying cities on the bases of the size of African-American population, usually all cities
                 having more than 100,000 African-Americans.
       3         then select samples of blocks from each city in the 100,000 stratum
       4         next calculate an index of dissimilarity for each block
       5         finally sum the indexes to yield a score for each city




Disproportionate stratification

       If the population involving characteristics are unequally distributed across cases making
       up the population, more cases from the smaller strata will be selected disproportionately
       in order to represent that stratum. This practice is known as disproportionate
       stratification.

       Example

       Canada has 10 provinces.
       Each province is a separate stratum.
       The small provinces are over sampled in order to represent the minority.

       Studying student performance between Whites and Blacks in UWEC.
                                                                                                28
       There are 9,000 white students but 1,000 blacks
       If the sample fraction is 10%, then white students will be 900 and blacks 100.
       To ensure more representatives for blacks, the researcher may increase the fraction to
       90% of the blacks thus making about 900 blacks in the sample.



Cluster Sampling

       It is a two-step-process in which aggregates or groups of individuals (clusters) are
       sampled and then samples of individuals are selected from within each cluster.

       Cluster sampling is often used to sample institutional clusters such as schools, churches,
       corporations, and service of residential clusters.

       Simply put, it is the Group selection first and individual second.


       Example

       1      To sample Milwaukee residents, first, samples would be drawn from the clusters -
              the blocks of the city/clusters of residential districts.
       2      Next to locate all residence and list all persons residing in the selected
              blocks/districts.
       4      Then to conduct a stratified random sample (individuals) from that block


Probability Proportional to Size (PPS)

       As clusters may differ in size, random sampling for the different sized clusters may
       under-represent cases for the small clusters but over represent the larger ones.
       One solution to problem is to use probability proportional to size (PPS).
       This method emphasizes equal chance of selection and representation.

       1      to draw from each cluster a sample proportional to the size of the cluster

       2      each case of the cluster selected, big or small, now has the equal/same probability
              of being included


       Example

       1      to draw 10% residents from a cluster of 1,000 residents, we have 100 cases
              selected
                                                                                                    29
       2       to draw 10% residents from a cluster of 1,000,000 residents, we have 100,000
               cases selected
       3       in either big or small clusters, PPS guarantees sample size represents
               proportionately to size of that cluster
       4       Read example of “NORC National Samples”- the National Opinion Research
               Center used by University of Chicago (page 71)

Sampling Elusive Subgroups (Duo-Bi-De)

       A certain subgroups of people whose scarcity and location and information are difficult
       to obtain. A list of blue-eyed persons, for example, is hard to obtain. The homeless in
       Chicago, for another example, is done with special effort (Jews in America is omitted).

       Scarcity is the major problem
       Subgroups are simply difficult to locate.

       Example       Homeless in Chicago by Peter Rossi (1989)
       Consult people who are involved to help the homeless and learn more information about
       the homeless.

       1       Homeless are seasonal, more in summer but less in winter
       2       Rossi paid people for interviews
       3       The homeless are very mobile during the day and settle down at nights
       4       The homeless have several places for shelters:
               parks, sidewalks, doorways, alleys, vacant lots, abandoned buildings, etc.
       5       Assemble a list of shelters and average number of occupants
       6       Count all the homeless who are present in the selected shelters
       7       Make the randomly selection
       8       Interview are conducted with pay (80% completed the interviews)


Bias
       There are a number of sources of biases that may distort the random samples in addition
       to random fluctuations.

Non-response bias
      Not every person selected in the sample agrees to respond. They may either refuse to fill
      the questionnaires or deny the interview.

       A substantial non-response rate will bias the sample because the sample will under-
       represent certain kinds of respondents in the target population, such as people’s ages,
       gender, regions (urban or rural), race (Whites or Black), the poor or the wealthy, liberal
       or conservative, etc.

Selective availability
                                                                                                  30
       There is a certain group of people whose availability is problematic.
       1      Men are less likely to have permanent addresses and rarely at homes
       2      More women tend to be at home at time of interviews
       3      Residents of jails, prisons, and military posts are likely to be excluded
       4      students at schools are less likely to be selected because students’ dormitories are
              often excluded in the samples
       5      as a result, a certain group of people is likely to be less represented in the
              population

Areal bias
       In areas or states where the population is widely dispersed and ethnic and religious
       groups are highly concentrated, areal biases are likely to occur in the cluster samples.

       Example

       The homeless are likely to be found in big cities
       Blacks are more likely to be found in southern states.
       Asians are more likely to be found in Hawaii, San Francisco, Chicago, New York, Los
       Angeles, Washington DC, Houston, etc.
       Latinos are more likely to be found in southern western states.
       Catholics are more likely to be found in northern states.
       Baptists are more likely to be found in southern states, etc.


Weighting
      Assigning different values (weights) to each case in order to obtain proper proportionality
      to the sample or to the population depending on researchers’ objectives.

       1       Weights are assigned so that the size of the weighted samples is the size as the un-
               weighted sample. It is called weighting to the sample size.
       2       Weights are assigned so that the size of the weighted samples is proportional to
               population size. It is called weighting to the population size.
       3       Confidence intervals and significance tests are not used for weights because the
               number of cases is enormously inflated.


Examples

               An example: Assignment weights:

       Midterm test    25%
       Final           25%
       Assignment      20%
       Term Paper      20%
       Attendance      10%
                                                                                                 31
                        ------
                        100


         1      We have a sample size of 2000 randomly selected cases of nurses in which 1000
                are males and 1000 are females. It is obviously male nurse are over-represented.
                We have few females but too many males. Male and female nurses take equal
                percentages (50%) of the sample.
         2      Assume we only expect 112 males and 1888 females for research purpose. Then
                we weight each male by .112 and each female by 1.888.
         3      That is, each male in the sample is counted as only .112 of a case and each female
                counted as 1.888 of a case. As a result, male takes about 112/2000 = 5.6% and
                female take 94.4% of the sample. But all together the sample size remains
                unchanged 112+1888 = 2000.
         4      Consequently, the variable distribution in the weighted sample such as education
                and income for males and females will make their correct and proportional
                contributions to the totals.
         5      Suppose the population is N=10,000 while the sample size is n=2000.The sample
                takes 20% of the population. Assume that we want to reduce the sample
                percentage to 10%. Then we can weight each element of the sample by .5. That is,
                each element is only counted as .5 of a case and all together the sample is counted
                as n=2000 x .5 = 1000. As a result, we achieved to the weighted sample
                proportional to population size by 10%.


Dr. Ji                                                                       Handout 5
Soc 332


                    CHAPTER 5 CAUSATION AND CAUSAL MODELS
____________________________________________________________________________________

        Causal Relationships In Social Research
       Causal Criteria
       Causal Models

Causes
         A cause is anything that produces a result or an effect.
         Causation is one variable that produces results in variation in another variable.
         In terms of social science, independent variables are seen as causes and dependent
         variables are as effects.

         One of the important tasks for social scientists is to seek causes of social phenomena and
         then to sort out each as to whether it is an actual cause and how important it is when
         being compared with other causes.
                                                                                                   32

Criteria of Causation
       Four criteria must be met before one can claim that the relationships between variables
       under study are causal.

       1      Time order/Temporal order
              Refers to the sequence of the variables.
              A cause must occur before its effect.
              If the food caused your illness, you must eat food first and then you are sick.

              If X is the cause of Y, X must occur prior to Y.

       2      Correlation
              If X is the cause of Y, X and Y must co-vary.
              The cause and the effect vary in unison.
              Changes in X must cause changes in Y.
              When variables vary in unison, they are correlated.
              Correlation can be either positive or negative.
              The hypothesis must specify the sign of the expected correlation.

       3      Non-spurious relationship
              Two variables appearing to be a cause-and-effect relationship may be in fact
              that their correlations are due to a third variable, an unobserved or unidentified
              variable. In other words, if a causal relationship is explained away by a third
              variable, then that presents a spurious relationship.

              If X is the cause of Y, but Z is both a cause of X and Y, then the relatio0nship
              between X and Y is a spurious relationship.


       4      Theoretical explanation
              A causal relationship must be backed up based on a theoretical ground. That is, an
              explanation is needed from a theory to support the causal relationship.


Causal Aspects of Variables
      Hypotheses connect variables and often postulate a cause-and-effect relationship among
      them.
      The position of a variable in a hypothesis can clarify variables into four types:
      independent, dependent, intervening, and antecedent.

Independent variable
       An independent variable is hypothesized to be the cause of the dependent variable.
       Women’s education levels, for example, are hypothesized to be the cause of variations in
       their fertility (number of children born to a woman).
                                                                                                    33

Dependent variable
      A dependent variable is hypothesized to be the effect being caused. That is, number of
      children is caused by the variations in women’s educational levels.

       Independent                           Dependent

       Education Levels                      Number of children

Antecedent variable
      Antecedent is defined as “going before,” “prior to,” or “preceding.” An antecedent
      variable is the cause of spurious correlations between other variables. It is the source of
      spurious relationships because it comes before their consequences.

       Example 1

       HR1:    People’s height is likely to affect their reading scores.
       HR2:    People’s age is likely to affect both their height and reading scores.

       Antecedent Variable            Spurious Relationship


                                     Height
                       Age              
                                     Reading Score

       Example 2

       HR1:    Tall people are more apt to like basketball than are short people.

                              Height
                              
                              Liking Basketball


       Table 5.1 Liking Basketball by Height (%)
       _______________________________________
                                    Height
                            ________________
                            Tall           Short
       _______________________________________
       Liking               65             45
       Don’t liking         35             55
       _______________________________________
       Total                100            100
                                                                                                   34
       _______________________________________

       Results:         This table appears to support the hypothesis.

       Is this relationship spurious? Other variables such as gender may be an antecedent in time
       to both height and reactions to basketball. To verify this suspect, we introduce gender as
       a likely antecedent variable.


       HR2:    Gender is more likely to impact both height and liking basketball.

                                      Height
               Gender                  
                                      Liking Basketball

       Table 5.2    Liking Basketball by Height Controlling for Gender (%)
       ______________________________________________________________
                                  Males                         Females
                           ________________             _________________
                           Tall           Short         Tall           Short
       ______________________________________________________________
       Liking              85             85            25             25
       Don’t liking        15             15            75             75
       ______________________________________________________________
       Total               100            100           100            100
       ______________________________________________________________

       Results:         Table 5.2 shows that by treating gender as an antecedent, the table does
                        not support the hypothesis. The relationship between height and liking
                        basketball has disappeared. Then we conclude that the original
                        relationship is spurious (p93).


Intervening variable
       An intervening variable acts as a link between an independent and a dependent variable.
       Compared with the antecedent that occurs prior to both of the variables in a relationship,
       an intervening variable occurs after the independent but before the dependent variable.

       Example

       HR1:    Family structure affects children’s delinquency behavior.

               Independent                           Dependent

               Family Structure                      Delinquency
                                                                                                   35
             (1~2 parents)                          (scores)


      HR2:   Supervision is the link between family structure and delinquency.

             *Family structure affects amount of supervision for children and the
             amount of supervision affects children’s delinquency behavior.

             If the hypothesis is true, then it is the supervision that affects then delinquency
             while family structure makes no difference about delinquency scores.


             Independent           Intervening            Dependent

             Family Structure      Supervision            Delinquency 
             (1/2 parents)          (amount of time)        (scores)

             Family Structure                              Delinquency      
             (1/2 parents)                                  (scores)




      Table 5.3   Delinquency by Family Structure Controlling for supervision (%)
      __________________________________________________________________
                         Well Supervision           Poorly Supervision
      Delinquency        __________________         ___________________
      Score              1 parent      2parent      1 parent        2 parent
      __________________________________________________________________
      High               10            10           45              45
      Low                90            90           55              55
      __________________________________________________________________
      Total              100           100          100             100
      __________________________________________________________________

      Result:       Table 5.3 confirms the hypothesis that supervision is the link between
                    family structure and delinquency. When intervening variable is
                    introduced, the effect of family structure on delinquency is gone. Whether
                    “well supervision or not” makes a difference in delinquency.


Causal Models
                                                                                                 36
       The statistical descriptions of a set of empirical data that attempt to identify and to
       measure all of the relationships among some set of independent variables and the
       dependent variable. A causal model is designed to gauge dynamic relationships among
       the variables based on deductions from a theory.

       Causal models are constructed by examining a number of variables simultaneously.

       Causal models can be examined by cross-tabulations and regressions. As cross-tabulation
       analysis is limited by the umber of variables (3 to 4 variables at a time in a table),
       regression models are preferred and more advantageous over cross-tabs.


Interpreting Regression Models

Correlation coefficients
       r, Pearson’s correlation coefficient, only reports the relationship between two variables. It
       tells the strength of the relationship between two variables. Coefficients vary from 0.00
       (no relationship) to 1.0 (a perfect relationship). It may be either positive or negative. The
       limitation is that they examine only two variables at a time.

Regression coefficients
       Regression coefficients can use the correlations between each pair of variables in a set of
       variables to calculate relationships among the entire set of variables.
       Regression coefficients estimate the independent effect of each independent variable on
       the dependent variable.
       Ordinal or higher measures are applicable in regression analysis.

Net and Joint Effects
       BETA stands for the standardized beta or standardized coefficient, which is the NET
       effect of each independent variable on the dependent variable.

       The R2 (r square) stands for a measure of the combined, or joint effects of the all the
       independent variables on the dependent variable.
       R Square can be converted into percentages and is expressed as the percentage of the
       variance on the dependent variable explained by all the independent variables included in
       the model (see example p.99).


Spuriousness
       Regression can test the spuriousness.

       H1:     Higher Playboy circulation increases liquor consumption.
       H2:     States with more male homes increase liquor consumption.
                                                                                                37
       When putting both variables into the regression model, the relationship between
       playboy circulation and liquor consumption is insignificant, whereas states with higher
       male homes do significantly relate to liquor consumption. Therefore, when a variable has
       no net effect, the original relationship is spurious.

Suppressor Variables
      When two independent variables are highly correlated with one another and each is
      strongly correlated with the dependent variable but in the opposite direction.
      In this case, each of the independent variables may suppress the effect of the other. As a
      result, two independent variables may appear NOT to be correlated with the dependent
      variable when in fact they are. This is called a suppressor variable.

       Suppressor variables can be identified through “+” or “-“ Standardized Coefficients,
       BETA.

       Standardized beta has the advantages of comparability since the raw values of beta have
       been “standardized” - converted to a common unit of measurement (regardless the raw
       units such as years of education, dollars of income, membership rates, etc.).


Comparing Models
     When comparing models, reports vary from one researcher to another. In general,
     however, people will report significance for each variable (t and p), report the variance on
     the dependent variable explained by the independent variables in terms of R2; some also
     report the un-standardized coefficients (b); still some report the standardized beta (B).

       When making comparisons between models, standardized beta is used; while report
       analysis from one’s own research work, one can either use un-standardized coefficients
       (b), or use standardized beta (B), or both.

       Examples from SPSS.


Using Categorical Variables

       1       Regression analysis requires both dependent and independent variables to be
               interval, ratio, or ordinal measures.
       2       When the independent variable is categorical or nominal, the independent variable
               must be converted into dummy variable.

Dummy Variable
    A dummy variable is a categorical variable that must be recoded to assign the values 0
    and 1 for each category so that 1 indicates the presence of the category, and 0 represents
    the absence of the category.
                                                                                             38
      When interpreting the regression coefficients for dummy variables, the coefficients
      represent the means score of the category (recoded 1). The comparisons of means are
      made within that category between the category recoded 1 and the category recoded 0.

      The number of dummy variables = number of categories of that variable - 1
      Gender (male and female) has one dummy; Religion (Protestant, Catholic, Jewish, others)
      has three dummy; Marital status (married, single, divorced, widowed, others) has four,
      etc. (See example paged 105).


      Example

      Gender:       recoded 1= Male and 0 = Female;

      Race:         recoded 1= whites and 0= Blacks

      Religion:     Protestants recoded 1 and others = 0;
                    Catholic recoded 1and others = 0;
                    Jewish recoded 1 and others = 0;
                    Others recoded as 0, the reference group.

      All categories recoded as “0” categories are called “Reference Group.”




Dichotomous Dependent Variables

      When the dependent variable is a dichotomous variable that consists of two values, one
      has to apply an advanced technique known as “Logistic Regression.” This method will be
      dealt with in graduate studies and beyond the scope of this text.


See Examples on next page (Transparency)
                                                                                  39




             Examples for correlation and regression for Chapter 5 SOC332
Correlation Coefficients
N: 2313     Missing: 519
Cronbach's alpha: 0.274
LISTWISE deletion (1-tailed test)
Significance Levels: ** =.01, * =.05

       HEALTH SEX FREQ
HEALTH 1.000         0.175 **
SEX FREQ      0.175 **      1.000


Correlation Coefficients
N: 2097     Missing: 735
Cronbach's alpha: Not calculated--negative correlations
LISTWISE deletion (1-tailed test)
Significance Levels: ** =.01, * =.05

             SEX FREQ      HEALTH        AGE!          EDUCATION!     INCOME!
SEX FREQ      1.000         0.169   **   -0.380 **      0.132 **       0.238 **
HEALTH       0.169 **       1.000        -0.192 **      0.252 **       0.261 **
AGE!         -0.380 **     -0.192   **    1.000        -0.144 **      -0.031
EDUCATION!    0.132 **      0.252   **   -0.144 **      1.000          0.391 **
INCOME!       0.238 **      0.261   **   -0.031         0.391 **       1.000
                                                                                    40
Analysis of Variance
Dependent Variable: SEX FREQ
N: 2313          Missing: 519
Multiple R-Square = 0.031     Y-Intercept = 1.467
Standard error of the estimate = 0.451
LISTWISE deletion (1-tailed test)
Significance Levels: **=.01, *=.05

         Source       Sum of Squares       DF      Mean Square   F      Prob.
         REGRESSION   14.854               1       14.854        73.081 0.000
         RESIDUAL     469.713              2311    0.203
         TOTAL        484.566              2312

               Unstand.b     Stand.Beta    Std.Err.b     t
HEALTH         0.111         0.175         0.013         8.549 **

Analysis of Variance
Dependent Variable: SEX FREQ
N: 2097          Missing: 735
Multiple R-Square = 0.197     Y-Intercept = 1.849
Standard error of the estimate = 0.405
LISTWISE deletion (1-tailed test)
Significance Levels: **=.01, *=.05

         Source       Sum of Squares       DF      Mean Square   F      Prob.
         REGRESSION   84.227               4       21.057        128.66 0.000
         RESIDUAL     342.380              2092    0.164
         TOTAL        426.608              2096

               Unstand.b     Stand.Beta    Std.Err.b     t
HEALTH         0.029         0.046         0.013         2.194 *
AGE!           -0.010        -0.367        0.001         -18.264 **
EDUCATION!     -0.003        -0.019        0.003         -0.894
INCOME!        0.019         0.222         0.002         10.235 **
Regression can identify spurious relationships
Example
         H1:   Church attendance affects sex frequency
Analysis of Variance
Dependent Variable: SEX FREQ!
N: 2288          Missing: 544
Multiple R-Square = 0.006     Y-Intercept = 2.992
Standard error of the estimate = 1.953
LISTWISE deletion (1-tailed test)
Significance Levels: **=.01, *=.05

         Source Sum of Squares      DF     Mean Square   F      Prob.
         REGRESSION    50.304       1      50.304        13.195 0.000
         RESIDUAL      8715.213     2286   3.812
         TOTAL         8765.517     2287

               Unstand.b     Stand.Beta    Std.Err.b     t

ATTEND!        -0.054               -0.076         0     .015           -3.632 **

Is that relationship spurious?
                                                                                    41
       H2:    Age affects both church attendance and sex frequency

Analysis of Variance
Dependent Variable: SEX FREQ!
N: 2287          Missing: 545
Multiple R-Square = 0.191     Y-Intercept = 5.073
Standard error of the estimate = 1.762
LISTWISE deletion (1-tailed test)
Significance Levels: **=.01, *=.05

       Source Sum of Squares         DF     Mean Square   F            Prob.
       REGRESSION    1676.151        2      838.076       270.060      0.000
       RESIDUAL      7087.923        2284   3.103
       TOTAL         8764.074        2286

              Unstand.b     Stand.Beta      Std.Err.b     t

ATTEND!       -0.002                 -0.002               0.014        -0.113
AGE!          -0.051        -0.437          0.002         -22.892 **



Conclusion:

Regression can test the spuriousness.

When putting two or more independent variables into a regression model, if the
relationship between one of them and the dependent variable is gone, that implies that
the original relationship between that independent and the dependent variable might be
spurious.




Suppressor variables can be identified through “+” or “-”standardized
coefficients BETA
***Correlation Analysis

H1: Health status and age are likely to be related.


Correlation Coefficients
N: 2312     Missing: 520
Cronbach's alpha: Not calculated--negative correlations
LISTWISE deletion (1-tailed test)
Significance Levels: ** =.01, * =.05

              HEALTH!       AGE!            SEX FREQ!
HEALTH!        1.000        -0.235 **        0.202 **
AGE!          -0.235 **      1.000          -0.440 **
                                                                                              42
SEX FREQ!      0.202 **      -0.440 **       1.000

Findings:

Health and age are negatively related.
It is likely that health and age suppress each other and may affect their impact on
the dependent variable



***Regression Analysis

H2:    Both health status and age affect sex frequency.

Analysis of Variance
Dependent Variable: SEX FREQ!
N: 2312          Missing: 520
Multiple R-Square = 0.204     Y-Intercept = 4.196
Standard error of the estimate = 1.751
LISTWISE deletion (1-tailed test)
Significance Levels: **=.01, *=.05

       Source         Sum of Squares        DF       Mean Square     F         Prob.
       REGRESSION     1810.677              2        905.339         295.269   0.000
       RESIDUAL       7079.736              2309     3.066
       TOTAL          8890.413              2311

              Unstand.b      Stand.Beta     Std.Err.b      t
HEALTH!       0.251          0.104          0.046          5.466 **
AGE!          -0.049         -0.415         0.002          -21.725 **


Findings:
Health and age influence sex frequency but in opposite direction.
The results may be distorted due to the suppressor effect between health and age.



***For report on regression analysis and model comparisons, refer to Soc331 Chapter 12
transparency.



Dummy Variable Examples

Explore the relationship between marital status and family income.

H1 :   Family income varies as a function of marital status.

Marital status is a categorical independent variable and can be coded as “dummy variables:”
Dmar1 = married
Dmar2 = widowed
Dmar3 = divorced or separated
Dmar4 = never married coded as “0,” the Reference Group.
                                                                                                  43
The Regression Analysis Results

Predictor         R2        b              B*         t         Sig.       F
              strength                           statistics            Model Test Sig.
Married       .191       3.712           .363     11.093      .000     98.105       .000
Widowed                   - 2.326       -.134    -4.584       .000
Divorced                  -.284         -.020    -.652        .515


Marital Status: The data are consistent with the hypothesis that total family income varies as a
function of marital status (F = 98.105, p = .000). The knowledge of marital status explains
approximately (R2 = .191) 19.1% of the variance in total family income.

As marital status here is treated as a dummy variable which is coded Dmar1=married,
Dmar2=widowed, Dmar3=divorced or separated, and Dmar4=never married coded as “reference
group”, so there are three unstandardized regression coefficients b1 = 3.712,
b2 = -2.326, and b3= -.284 representing married, widowed, and divorced, respectively. These are
the expected mean difference in total family income between married and never married,
between widowed and never married, and between divorced and never married respectively. In
other words, the expected mean income will be 3.712 units higher for married than for never
married, but 2.326 units lower for widowed than for never married, and .284 units lower for
divorced or separated than for never married.

These data demonstrate the fact that the expected mean total family income differs among
marital status. Therefore, I can conclude that the empirical data support the hypothesis that total
family income varies as a function of marital status.



Dr. Ji                                                                          Handout 6
Soc 332


                         CHAPTER 6 BASIC RESEARCH DESIGN
____________________________________________________________________________________

      Contour major social research design

The Experimental Research
1)    Experimental research builds on the principles of a positivist approach more directly than
      do the other research techniques. It is the strongest for testing causal relationships
      because the 3 conditions for causality (temporal order, association, no alternative
      explanations) are clearly met in experimental designs.
                                                                                                44
2)     Experimental research has two features:
       1)    Researchers can manipulate the independent variable - to make it vary as much as
             they wish and whenever they wish.
       2)    There is random assignment of subjects - people who participate in the
             experiment are referred to as the subjects. They are subjected to different levels of
             the independent variable

Manipulating the independent variable
      The experiment provides the most powerful research design for Causal models: time
      order, correlations, and establishment of non-spurious relationship by holding constant
      the control variables.

1)     One-shot case study

       X             O

       X is the independent variable and O is the dependent.
       X is the treatment condition and O stands for observation.

2)     The one-group pretest-post test design

       O1     X              O2

       O1 is the group for pretest to record down scores
       O2 is the same group for post-test to see any effects after exposure to the independent X

3)     The static-group comparison

       X             O1
                      O2

       O1 and O2 are separate two groups. Group O1 is exposed to the X while O2 is not. Then
       compare the effects between group O1 and O2 to see the effects by X.

4)     The pretest-posttest control group design

                      O1     X              O2
       R
                      O3                     O4

       Two groups: O1, O2 is the experimental and O3, O4 the control group.
       R indicates that the subjects of both O1 and O3 are randomly assigned to the groups.
       Pretest groups O1 and O3.
       Expose O2 with X but not for O4.
       Posttest groups O2 and O4, to see the effects.
                                                                                                 45

Randomization
1)    Subjects are assigned randomly to two or more comparison groups.
2)    Establish the equivalence of comparison groups.
3)    Subjects have an equal probability of being assigned to any of the treatment conditions.
4)    To make the groups seeing each combination alike so that they will include the same mix
      of individual characteristics.
5)    To prevent spurious experimental findings.

Significance
1)      Significance test specifies the odds/probability that a given correlation between variables
        was caused by random variation. It tells us when to take an observed result as real and
        when to reject it as a fluke. It is concerned with whether a relationship found in a sample
        is also to be true in the population.

2)     There are two factors affecting the calculation of significance:
       a)     The number of the subjects in the group. The larger the number, the less likely the
              results are random flukes.
       b)     The size of the observed differences/correlation. The larger the
              difference/correlation, the less likely the difference is caused by flukes.

3)     The experiments are the best choice for social research because of manipulation and
       randomization.

4)     The experiments for human beings are most difficult:
       a)     We can’t randomly assign people to be male, Canadian, at age of 5, Protestant, or
              to be female, Japanese, 85, and Daoism, etc.
       b)     Social researchers are more prone to non-experimental design.

Advantages (chapter 7 Reference)
1).   Least vulnerable to problems of internal validity.
2).   Provides strongest inference about cause and effect. Subjects are assigned randomly to
      two or more comparison groups. The comparison group represents the different levels on
      the Independent Variable. Because true experiments rely on random assignment of
      subjects to comparison groups. 1. Subjects have an equal probability of being selected. 2.
      Systematic bias is eliminated.
3).   Provides confidence that the distribution of extraneous variables (which could confound
      the research findings if not controlled)-will be distributed equally across treatment
      conditions.
4).   Establishes the equivalence of comparison groups.
5).   Controls for the potential contaminating effects of all variables, including
      those we are not aware of, simultaneously.
6).   Provide the strongest support for causal inferences.

Disadvantages:
                                                                                                  46
       (4)    Experiments are typically conducted using volunteer subjects, often college
              students, who may not be representative of larger populations.
       (5)    Experiments tend to be highly focused, data is usually collected on only a small
              number of variables. Ana
       (6)    since it is a closed-door and artificial research, it can not be used for generalizing
              purposes.


Survey Research
      Survey research is based on samples of individuals who are interviewed (face-to-face or
      telephone interviews) or who fill out questionnaires. People who are included in the
      survey are referred to as respondents.

Studying “natural” variation
       Survey makes it possible to study variables that are impossible or immoral to manipulate.
       In other words, survey can examine variation as it naturally occurs.
       1)      We can not manipulate political party preference, for example.
       2)      We can not randomly assign people to live in certain areas.
       3)      We get specific information for our research purposes.

Advantages:
      (1)     used for descriptive and explorative purposes;
      (2)     excellent for describing the characteristics of a larger heterogeneous population;
      (3)     wide coverage and many topics can be obtained from a single survey;
      (4)     effective tool for providing information about non-observable characteristics
              ( perceptions, beliefs, feelings, motivations, future plans, past behavior, etc.);
       (5)    provides comparable information across units of analysis;
       (6)    data are easy to code and statistically analyze;
       (7)    can establish relationships between variables;
       (8)    data can be used for multiple purposes;
       (9)    can insure subjects anonymity and confidentiality;
       (10)   relatively fast and safe.

Disadvantages:
      (1)     provide weak tests of cause-effect hypothesis;
      (2)     lacks depth - only provide short term or secondary interaction;
      (3)     place primary reliance on verbal reports of non-verbal behavior;
      (4)     assume subjects have the requisite skills to report verbally;
      (5)     tendency to restrict researchers to individuals as the basic unit of analysis;
      (6)     often difficult to develop a representative sampling frame;
      (7)     poor social indicators of social change because most surveys are cross-sectional;
      (8)     highly reactive measurement tools - may produce changes in the attitudes,
              opinions, and behavior;
      (9)     cost - widely varies by methods.
                                                                                                  47

Field Research
       1)    Field research involves going out to the real field or natural settings to observe
             people as they engage in the activities of interest. Research is conducted in the
             field - in the natural settings in which people and activities of interest are
             normally found and studied.

       2)      Field research is often guided by research hypotheses and exploratory in nature.
               Anthropologists pioneered field research methods.

       3)      Field research produces suggestive hypothesis after the observations are
               completed.

              Covert observation
       People who are observed are unaware that they are the objects of research.

       1)      Researchers must be normal participants.
       2)      Researchers are limited in how inquisitive/curiosity they can be.
       3)      Researchers do not disturb the natural patterns of behavior.
       4)      Able to minimize observer effects.

              Overt observation
       People who are observed know they are the objects of research.

Field Notes/Observational research
       As experiments produce written records and surveys obtain data, field researchers write
       notes about what they see and hear. These notes constitute the data based on which
       conclusions may be drawn. These notes can be computerized and the qualitative data can
       be quantified for analysis. Field research has two limitations.
       1)     It is only suitable for small groups.
       2)     Like all other non-experimental designs, field research cannot offer proof that the
              correlation between attachments to the group and conversation was not a spurious
              relationship.

1)     Observational research provides direct and generally un-ambiguous evidence of overt
       behavior, but it also used to measure subjective experiences such as feelings and
       attitudes. For example, one could measure interpersonal attraction by observing and
       recording the physical distance that two people maintain between themselves, with closer
       distances indicative of greater liking. Besides physical distance and eye contact,
       researchers have used exterior physical signs (e.g. clothing), gestures, and language to
       measure behavior. The categories of observation measures often involve the frequency
       and duration of acts. Observational research applies to properties that can be perceived
       directly by the senses.
2)     Observation is the procedure by which a scientist gathers his data. Whereas measurement
       is the assignment of numbers to the various outcomes one’s variable can exhibit,
                                                                                                   48
       observation is the way in which one undertakes to measure a phenomenon. It may be
       direct, where one actually observes the behavior of the individuals (for example, an
       anthropologist in a primitive society), or indirect, as through the use of questionnaire,
       interviews, projective techniques, or physiological reactions.

Advantages:
      1).     Can often provide a detailed description of individuals and the social relationships
              in which individuals are involved.
       2).    Offers access to actors of complex situations and events.
       3).    Sampling procedures are better than experiments but still leave questions about
              generalizability.
       4).    In observational research, there is often a long period of social interaction
              between the subject and the researcher. The researcher can make observations of
              non-verbal behavior, which may (or may not) be consistent with the subjects’
              self-reports.

Disadvantages:
      1).     Absence of formal controls makes this research least acceptable for testing causal
              hypotheses.
      2).     Vulnerability to experimenter and testing effects depends on the investigation and
              on how well they are accepted by the group they are studying.
      3).     Very difficult to replicate.
      4).     Typically conducted on small, purposely selected samples, thus, generalizability
              of findings to large populations is limited.
      5).     Data from observational research usually consists of text-based notes that are not
              in standardized formats. Such information is difficult to sort through, organize
              and systematically analyze.




Comparative Research/Aggregate Research

1)     Unlike studying individuals as units of analysis, comparative research usually identifies
       comparisons of large social units - typically the units of analysis involve the whole
       nations, societies, states, cities, counties, etc.
2)     Comparative research usually uses aggregate data from a larger areas or districts such as
       from countries, cities, towns, counties, states, etc.
3)     These aggregate data are already organized or published in a tabulated form with few
       statistical techniques known to researchers. Examples of such data as crude birth rates,
       death rates, infant mortality rates, population density, urbanization rates, divorce rates,
       marriage rates, migration rates, poverty rates, crime rates, unemployment rates, etc.
4)     Little can be done for further quantitative data analysis.
5)     The purposes for comparative research are to seek similarities, differences, or explore
       dynamic trend of topics of interests.
                                                                                               49


Data “Collection”
       Unlike other research designs, data collection for comparative studies are primarily based
       on search for governmental documents or international publishing such as almanac in
       libraries because data are primarily collected by governments agencies.


Using official data to compare nations
       There is a number of very useful almanac:

1)     The World Almanac and Book of Facts - ethnic, religious, language compositions; GNP;
       infant mortality, life expectancy; rates of TV; radios and telephones, etc.
2)     The World Fact Book
3)     The Demographic Year Book - health statistics such as causes of death, etc
4)      Population Council - issuing fertility and contraceptives, etc.
4)     World Bank - report on economic development and international trade.

       Example (Henan Elderly Data, 1987)

       Sources Of Medical Care Of The Aged By Gender And Region In Henan
       _______________________________________________________________
                    Male                 /     Female
       _______________________________________________________________
                    Urban          Rural /     Urban        Rural
       Self         593            8273 /      3140         9658
                    9.0            92.7 /      42.5         96.5

       Half-self      785            270     /      2299           318
                      11.9           3.0     /      31.1           3.2

       Public         5200           384     /      1946           33
                      79.1           4.3     /      26.4           .3

       Total       6578        8927 /      7385        10009
                   42.4        57.6 /      42.5        57.5
       _______________________________________________________________

Cross-cultural samples? P119


Content Analysis
1)    Verbal and non-quantitative textual and graphic materials are coded to produce
      quantitative data in order to make further analysis for research purposes.
                                                                                               50
2)     Many verbal and textual materials are produced without the producers having any
       thought about subsequent use or analysis. These materials can produce very important
       results by coding such materials to create variables.
3).    A research method for analyzing the symbolic content of any communication. It is a
       systematic examination of written documents such as novels, government publications,
       recorded speeches, and so forth. The basic idea is to reduce the total content of a
       communication to a set of categories that represent some characteristics of research
       interest.

There are three types of content analysis:
1).    Pragmatical, which classifies signs according to their probable causes or
       effects/relationships;
2).    Semantical, which classifies signs according to their meanings;
3).    Sign-vehicle, which classifies content according to the psycho-physical properties of the
       signs, “e.g., counting the number of times the word ‘Germany’ appears.”

       Examples

       Are there Liberal-Conservative differences among news magazines such as Times or
       Newsweek, etc?

       Are the restroom-wall graffiti related to the type of social environment in which the
       restrooms exist? (survey plus content analysis)

       Are there any changes in using the terms of “Blacks” and “African Americans” in today’s
       textbooks compared with what was used in 1950s?

       How do medium portray minorities differently than they did three decades ago?

       What are the most frequently grammatical mistakes in the student’s term paper -
       misspellings, tenses, compound sentences, subjunctive mood, etc?


Advantage:
1).   providing systematic examination of materials that are more typically evaluated
      on an impressionistic basis;
2).   guard against any inadvertent biases one may build into the examination.

Disadvantage:
1).   may not provide most appropriate reflection of the variable under study;
2).   scoring methods almost always contain an arbitrary element.



Studying Change
                                                                                                 51
      Changes over time are central to social research. These changes involve attitudes,
      policies, programs, products, procedures, demographic characteristics, etc.

1).   Surveys are taken over a period of time to examine the changes.
2).   Field research report changes based on observations.
3).   Comparison research is based on immense literature to study change.
4).   Censuses are used to study changes by comparing the same question items between-
      censuses such as population density, urbanization, fertility levels, contraceptive uses, etc.




               4 BASIC EXPERIMENTAL DESIGNS – SUMMARY ILLUSTRATION FOR
                                      CHAPTER 6


1)    One-shot case study

      X             O

      X is the independent variable and O is the dependent/subject.
      X is the treatment condition and O stands for observation.
      We manipulate X to O to see the effect on O from X.
                                                                                               52
2)     The one-group pretest-post test design

       O1     X              O2

       O1 is the group for pretest to record down scores.
       O2 is the same group for post-test to see any effects after exposure to the independent X.
       Effects are identified by comparing the pre- and post tests on O1 and O2.


3)     The static-group comparison

       X             O1
                             O2

       O1 and O2 are separate two groups.
       O1 is exposed to the X while O2 is not.
       The effects of X on group O1 and the absence of X on O2 are examined to see the
       difference.


4)     The pretest-posttest control group design

                      O1     X              O2
              R
                      O3                     O4

       Two groups: O1&O2 are the experimental group and O3&O4 the control group.
       R indicates that the subjects of both O1 and O3 are randomly assigned to the groups.
       Pretest groups O1 and O3.
       Expose O2 with X but not for O4.
       Posttest groups O2 and O4 to see the effects.



Dr. Ji                                                                     Handout 7
Soc 332
Spring 2002

                            CHAPTER 7 SURVEY RESEARCH
____________________________________________________________________________________

      Overview of the practice of survey research
      Evaluation of survey results, advantages and disadvantages

Simple and Proportional Facts
                                                                                                 53
Simple facts
       A simple fact is a concrete and limited state of affaires. It is a claim that something
       happened or exists and usually having to do with only one or few cases.
            African Americans are a minority in USA
            Jerry’s middle class family and earns an income about $ 46, 000 a year.

Proportional Facts
       A proportional fact states a distribution of something among a number of cases. It may
       also be the joint distribution of several things among a number of cases.
              What is the proportion of African Americans in the USA?
              What is the proportion of the middle class in America?

       Comparisons
           a simple fact is less expensive and easy to establish
           a proportional fact is more expensive and difficult to establish
           proportional facts need specific methods to obtain
           reliable proportional and specific facts need scientific survey research

Reliability
1      The extent to which the measurement instrument procedures produce consistent and
       stable results on repeated trials.
2      It is concerned with questions of stability and consistency.
3      Reliability is reduced from random error, but not nonrandom error.
4      Reliability deals with an indicator’s dependability.
5      If you have an eligible indicator or measure, it gives you the same result each time the
       same thing is measured (as long as what you are measuring is not changing).
6      Reliability means that the information provided by indicators (e.g. questionnaire) does
       not vary as a result of characteristics of the indicator, instrument, or measurement device
       itself.
7      A measure can have high reliability but not validity.

       Example
       If you get on your bathroom scales and read your weight. You get off and get on them
       again and again. You have a reliable scale if it gives you the same weight each time,
       assuming, of course, that you are not eating, drinking, changing clothing, and so on. An
       unreliable scale is one that registers different weights each time even though your “true”
       weight does not change. Another everyday example is a car speedometer. If the
       speedometer jumps between 10 mph and 80 mph when you are going at a constant slow
       speed over a smooth, flat surface, it is not a reliable indicator.

Three main factors cause unreliability in survey research data:

1      Can’t answer/can’t answer accurately
       1)     Respondents really don’t know the answer.
              e.g. don’t know how many TV hours watching a week.
                                                                                                 54
       2)      Having difficulties answering income.
               e.g. is that annual income, or hourly, weekly, monthly? Is that gross or net income
               or take-home income? Is that individual income or family income? It is hard to
               know family income because members really don’t know each other’s actual
               income.
       3)      Questions are irrelevant to respondents. They don’t have any opinions about the
               issues asked; it is not because respondents are stupid simply because they don’t
               care about one way or another.
               e.g. political opinion about which candidate is likely to be selected.
       4)      Misunderstanding the questions. Some questions are badly formulated - the
               primary reason for misleading.
               E.g. “Does it seem possible or does it seem impossible to you that the Nazi
               extermination of the Jews never happened?” - double negative, not meaningful,
               hard to interpret, badly written.

2      Won’t answer/won’t answer honestly
       1)    People tend not to give some answers honestly.
             E.g. people tend to report their ages are 29, 39, 49, or 59 but not 30, 40, 50, or 60
             simply not to admit they passed the one of the milestone ages. So the best way to
             ask ages is to ask their year of birth.
       2)    People tend to give inaccurate answer for some questions such income, ages,
             marital status, sexual activities, etc.

3      Refuse to answer
              For some other reasons, some people refuse to give answers.


Validity
1      Validity refers to the extent of matching or goodness of fit between an operational
       definition and concept it is purported to measure. It answers the question “Are you
       measuring what you intend to measure with this operational definition?” “Does the
       indicator reflect differences between units of analysis on the characteristics that we seek
       to measure?
2      When a measure is not valid, it means that the observed differences on the empirical
       indicator do not reflect the true differences on the characteristics of interest.
3      Validity is reduced from both random and non-random error.
4      Validity is the degree to which the empirical indicator measures what it purports to
       measure. Validity is something to be argued, not proven. Validity can be thought of as a
       set of standards by which you judge things. If an indicator is valid, observed differences
       on the indicators reflect true differences on the characteristic that we seek to measure.
       When a researcher says that an indicator is valid, it is for a particular purpose and
       definition. The same indicator can be valid for one purpose but may be less valid for
       another.(e.g. a measure of morale might be valid for measuring morale among teachers
       but invalid for measuring the morale of police officers).
                                                                                                   55
5      At its core, measurement validity is the degree of fit between a construct and indicators of
       it. It refers to how well the conceptual and operational definitions mesh with each other.
       The better the fit, the greater the measurement validity.
6      Validity is more difficult to achieve than reliability. Researchers cannot have absolute
       confidence about validity, but some measures are more valid than others. The reason we
       can never have absolute validity is that the constructs are abstract ideas, whereas
       indicators refer to concrete observations. This is the gap between our mental pictures
       about the world and the specific things we do at particular time and place.
7      Measuring attitudes must be distinguished from measuring behavior.
       1)       Attitude is an enduring, learned predisposition to respond in a consistent way to a
                particular stimulus/set of stimuli. We all have attitudes toward significant things
                surrounding us: people, objects, ideas, events, and so on.
                An attitude has three components:
                a) a belief - I believe there is a God.
                b) an evaluation - Religion is supportive to human being’s spiritual world.
                c) a behavioral disposition - I support to establish a new church in neighborhood.

       2)      Behavior is what living creatures do. Behavior involves action. We hold attitudes
               but perform behavior.

       3)      Some questions measure an attitude. Some attempt to measure a behavior rather
               than attitude.
               “Women are better suited to stay at homes than men.” - (gender bias attitude)
               “Who did most of your family chores, you or your wife?”(division of labor)

       4)      Links between attitude and behavior
               Attitude can predict behavior but are not measures of behavior.
               Questions that ask about how a respondent might act in a hypothetical situation
               are only weak measures of behavior.


Relationships between reliability and validity (random error and systematic error)
       A highly un-reliable measure cannot be valid — How can you get at whatever you want
       to measure if your results fluctuate wildly? On the other hand, a very reliable measure
       still may not be valid — you could be measuring very reliable something other than what
       you intended to measure — suppose we measure the “INTELLIGENCE” of students by
       standing them on a bathroom scale and reading the number off the dial. Such an
       operational definition could be highly reliable as repeated scale readings would yield
       consistent results. However, it could never be a valid measure of an individual’s
       “INTELLIGENCE.”

Interviews and Questionnaires
        Survey research is based on interviews and questionnaires. There are three kinds: Face to
face, telephone, and mail questionnaires.
                                                                                                56
Face-to-face interviews
Advantages:
       (1)     highest completion rate (80-90%);
       (2)     greater complexity of question formats and topics;
       (3)     can complete long questionnaire and longer interview schedules;
       (4)     lower rate of item non-response;
       (5)     greater control over the environment where the interview takes place;
       (6)     can examine non-verbal behavior;
       (7)     can maintain question order and sequencing;
       (8)     control over who responds;
       (9)     record date of interview;
       (10) can separate sample coverage from non-response.
Disadvantages of personal interview:
       (1)     cost most expensive;
       (2)     difficult to cover wide geographic areas;
       (3)     likely to stimulate socially desirable answers;
       (4)     potential for interview bias;
       (5)     potential for other interviewer errors-asking, probing, recording errors, etc.
       (6)     difficult to supervise interviewers;
       (7)     difficult to control time;
       (8)     sometime dangerous to interview in certain areas;
       (9)     inconvenience - intrusion into lives of the respondents;
       (10) difficult to consult records.

Mailed Questionnaires
Advantages:
       (1)    cost less expensive than face-to-face or telephone interviews;
       (2)    wide geographical coverage;
       (3)    less prone to social desirability;
       (4)    no interviewer’s bias;
       (5)    standardized question wording;
       (6)    easier to provide assurance of confidentiality;
       (7)    require fewer resources;
       (8)    questionnaire can be completed at respondent’ convenience.
Disadvantages:
       (1)    lower completion rates;
       (2)    require literate population;
       (3)    difficult to use open-ended questions;
       (4)    difficult to use complicated questions;
       (5)    verbal behavior only;
       (6)    no control of question order;
       (7)    no control over who complete the questionnaire;
       (8)    more prone to item non-response;
       (9)    increased likelihood of sample bias.
                                                                                                  57
Telephone interviews
Advantages:
      (1)     very fast;
      (2)     cost cheaper;
      (3)     wide geographical coverage;
      (4)     easier to train interviewers;
      (5)     easier to supervise interviewers;
      (6)     less threatening to respondents;
      (7)     allow use of random digit dialing- alleviate problems of developing sampling
              frames;
      (8)     higher completion rates than mailing questionnaires but lower than face-to-face
              interviews.
Disadvantages:
      (1)     lower completion rate than face-to-face interviews;
      (2)     difficult to use complex question formats;
      (3)     potential for interview bias;
      (4)     tends to under-represent the poor;
      (5)     tends to under-represent those whose unlisted in the phone books;
      (6)     less control over the respondent’s environment;
      (7)     often in-convenient for subjects, similar to personal interviews.

Measuring Concepts with Survey Questions
Standard versus Original Items
       Having been used for decades, many of the same concepts are measured in survey after
       survey. Consequently, many questions have been accepted as standard items, which have
       several advantages.

       1)     permit meaningful comparison across studies;
       2)     highly qualifies
       3)     save time and effort
       Standard items, however, do not exit for many of the primary concepts to be measured in
       most surveys. Original items should be always pre-tested before being used.

Closed versus Open-ended Questions
       Survey questions come in two basic forms: Closed and open-ended.

Closed questions
       ask respondents to select responses from a set of questions provided by the researchers.
       Advantages:
       1) maximize comparability;
       2) no re-coding;
       3) more efficient;
       4) more advantageous over open-ended.
       Disadvantages:
       1) suppress variation;
                                                                                         58
       2) may mis-classify respondents because questions may be worded poorly or ambiguous.

Open-ended question
      permit respondents to answer in their own words.
      Advantages:
      1) respondents are free to express themselves;
      2) useful for exploratory studies;
      3) working better for sensitive questions;
      4) providing juicy quotes for more report
      Disadvantage:
      1) hard to make comparisons
      2) difficult to code
      3) not standardized items
      4) time-consuming and more expensive

Evaluating closed questions
       1)      Unbiased language
               The way questions are worded has an extraordinary impact on how they are
               answered:

              “Should laws be passed to eliminate all possibilities of special interests giving
              huge sums of money to candidates?”
                     Yes 99%
                     No     1%

              “Please tell me whether you favor or oppose the proposal: The passage of new
              laws that would eliminate all possibility of special interests giving large sums of
              money to candidates.”
                     Favor          70%
                     Oppose         28%
                     Don’t know 2%

              2)     Clarity
                     Good survey items must be clearly worded.

                     “Do you think that McDonald’s should have not been found not liable for
                     damages when a woman spilled a cup of coffee in her lap?”
                     - double negative - bad

                     “Do you think that McDonald’s should have been found liable for
                     damages when a woman spilled a cup of coffee in her lap?”
                     - drop the negative entirely - better

              3)     Mutually exclusive and exhaustive categories
                     Categories must not be overlapped or omitted.
                                                                                          59

              What is your occupation? - exclusive/overlap
                     Professional
                     Managerial
                     Technical
                     White collar
                     Skilled craft

              What is your religious belief? - not exhaustive
                     Protestant
                     Catholic
                     Jewish

       4)     Forcing variation
              A frequent error in question construction is inappropriate collapsing of
              categories.

               “ how many different people have you had sex in the past 12 months?”
                       - 0-1
                       - 2-3
                       -4+
               (What about those having no sex? How to distinguish from them?)
Cafeteria and forced option questions
Cafeteria option questions/Multiple response items
       Respondents are allowed to pick and choose from a large selection of responses,
       indicating only the responses they select.
       To select all options that applies.

       e.g. Here is a list of groups or organizations. Could you tell me whether or not
       you are a member of each type. Check all that applies:
              Service clubs
              Veteran’s group
              Political clubs
              Labor unions
              Sports groups
              Youth unions
              School service groups
              Farm organizations
              Professional societies
              Any other groups

Forced option questions
       Respondents are required to indicate an appropriate response for each option
       To respond to every option. Either say Yes or No, one must have an option.
                                                                                                   60
       Comparisons
            Cafeteria questions are not good ways to measure things because the researchers
            don’t know if the respondents agree or disagree by skipping options from the
            respondents. In comparison, forced questions can let researchers know
            respondents’ attitudes.

       Contingency questions
       1)     Contingency questions divert respondents to different questions and give direction
              to skip questions on responses to a prior question.
       2)     Certain questions are only asked if the respondents have answered a previous
              question in a particular way.

               e.g. Have you ever been rewarded?
                      Yes, very often
                      Yes, a few times
                      Yes, but only once

              If you have been warded, was it in high school or in college?
                                      –College
                                      – High school
                                      – Both
Item order (p. 148 example)
       1)     what the study is about and why it is worthwhile for their participation;
       2)     start with interesting questions
       3)     no sensitive question at the beginning
       4)     building up trust
       5)     put personal background in the end (age, occupation, race, marital status,
              education, religion, etc.)

Response bias
      There are two commonly found biases in respondents. Conformity and Response set.

Conformity
      refers to the tendency of respondents to select the “normal” or “non-controversial”
      answers, based on their perceived social standards, in order to make a good impression
      about conforming to social expectations but may not give the most accurate answers.

              introduce a few questions that virtually everyone would disagree early in the
               interview to get the respondents accustomed to disagreeing.

Response set
      refers to the tendency of respondents to fall into a pattern of responding without regard
      for variation in content. This occurs partly due to the same set of response categories of
      all items.
                                                                                                     61
              the solution is to alternate negative and positive items in order to prevent such a
               pattern from developing.

Non-response
      Means to reduce non-response though controversial.
Motivating respondents
      1)      cover letter stating the importance of the survey
      2)      offer money $ 2 - 5
      3)      offer merchandise
      4)      prizes
      5)      paying respondents $15 - 50 as time consumed

Assessing non-response bias
       To discover how non-response may have influenced the results of a survey.
       1)     sex
       2)     age
       3)     occupation
       4)     race/ethnicity


Interviewing guidelines
       Basic points about the selection and training of interviewers.

Selection of interviewers
        1)      develop a warm and friendly impression
        2)      honest
        3)      ask questions in a neutral way
        4)      hear and record faithfully
        5)      select interviewers similar to the intended respondents (combination of age, sex,
                race, or ethnicity of interviewers matter very much sometimes)

Training of interviewers
       1)      get familiar with interview schedule
       2)      practice interviews prior to field work
       3)      read questions precisely and use the exact wording
       4)      write down exactly as said (open-ended questions)
       5)      probes are offered as provided in necessary

Studying change
Trend studies
       A trend study - a given general population may be sampled and studied at different
       points in time. Each sample represents the same population, though different persons are
       studied in each survey. Trend studies often involve a rather long period of data collection.
       Trend studies are based on descriptions of a general population over time, though the
       members of that population will change. i.e. the Gallop Polls on political campaign. By
                                                                                                 62
        comparing the results of these several polls, researchers may determine shifts in voting
        intentions. S56
        Trend study: A type of longitudinal study in which a given characteristic of some
        population is monitored over time. An example is the series of Gallop polls showing the
        political-candidate preferences of the electorate over the course of a campaign, even
        though different samples are interviewed at each point. It consists of a cross-sectional
        design in which each survey collects data on the same items or variables with an
        independent sample of the same target population. Don’t use the same sample; only the
        same population (GSS). Identifies which variables change over time. Trend studies
        often involve a rather long period of data collection. Trend studies are based on
                descriptions of a general population over time, although the members of that
        population will change. Persons alive and represented in the first study might be dead at
        the time of the 2nd, and persons unborn at the time of the 1st study might be represented in
        the 2nd. Typically, you do not personally collect all the data used in a trend study but
        instead conduct a secondary analysis of data collected over time by several other
        researchers.

Panel
        A panel design - involves the collection of data over time from the same sample of
        respondents. i.e. you re-interview all your panels to ask their opinions about political
        attitudes. The then “switchers” and “non-switchers” may tell you the reasons for
        switching. Panel studies can reveal which individuals are changing over time because the
        same respondents are surveyed again and again. S58
        Panel designs use a sample of the same respondents over a period of time to determine
        which individuals are changing over time and attempt to determine the causes for this
        change. The panel study is a powerful type of longitudinal research. In a panel study, a
        researcher observes exactly the same people, group, or organization in different time
        periods. In other words, he observes the same thing at multiple times. Panel research is
        difficult to conduct and very costly. Tracking people over time is difficult because some
        people die or cannot be located. The results of a well-designed panel study are
        extremely valuable. For example, a researcher might interview all families living on a
        city block in 1975. He would then locate the same families, no matter where they were
        living, in 1980, 1985, & 1990. This would give him 4 observations over a 15-year
        period. Ana

Longitudinal studies
       Time-series design - refers to any set of data in which “a well-defined quantity is
       recorded at successive, equally spaced time points over a specific period.” An important
       source of time series data is the CENSUS. However, where the recording interval is not
       equally spaced, one may still speak of a time series if data over time are involved, but the
       problems of interpretation of such a series will be much greater. D521
       Time-series research is a type of longitudinal research that is easier to conduct than a
       panel study because it does not have to use the same individuals or organizations.
       Consider the example of the families living on a block in 1975. With time series
       research, the researcher interviews anyone who lived on the block. Thus, for a panel
                                                                                                  63
       study the researcher must track down the families who were neighbors in 1975, no
       matter where they are, whereas for the time series study he just interviews whoever lives
       there--even if every family from 1975 has left and a whole new set of people live on the
       block in 1990. Time series results are less powerful than those from panel studies,
       but it is less difficult or costly to obtain them. Time series designs analyze changes in
       single variables over time, such as income, birth rate, etc. ana

Age effect
       People change as they get older. They may be more satisfied with their lives as they
       mature. Age effects are reflected between different age groups. This is called age effects.

       Older people differ from the younger people not only because they get older but because
       of differences between cohorts. Cohort differences are reflected between generations of
       the different cohorts. Cohort analysis is based on birth cohort - people born within the
       same period of time. These are called the cohort effects.

       Substantial differences occur over time because everyone is changing. These differences
       reflect the influences associated with a particular historical time period. Differences that
       occur over time are called period effects.

       E.g. Do you think there should be laws against marriage between blacks and whites?

       Age effects:
       Age                    20-29 30-39 40-49 50-59 60-69 70-79
       Yes, should be laws    21% 33% 40% 43% 48% 70%

       Period effects:
                                      1973    1984   1993
       Yes, should be laws            38%     25%    16%

       Cohort effects:
       Yes, should be laws
                                      %
               Age group      1973    1984    1993

               20-29          21      13      11

               30-39          33      13      8

               40-49          40      26      13

               50-59          43      35      14

               60-69          48      41      27
                                                                                               64
               70-79          70     54      44

Cohort effect
       A cohort design - focuses on the same specific population each time data are collected,
       though the samples are studied may be different. A cohort consists of persons who
       experience the same life event within a specified period of time. Cohorts are identified by
       their birth date. For example, 1960 cohort would be 20 years old by 1980 survey, 30
       years old in 1990 survey. Another example is the selection of a sample of students
       graduating from State University in the class of 1990. Five years later, we may select and
       study another sample drawn from the same class of 1990. While the sample may be
       different each time, we would still describe the class of 1990. S56
       Cohort design--A study in which some specific group is studied over time although data
       may be collected from different members in each set of observations. A study of the
       occupational history of the class of 1970, in which questionnaires were sent every five
       years, for example would be a cohort study. An additional example would be if we select
       a sample of students graduating from State University in the class of 1998 to determine
       their attitudes toward work. Five years late, we might select and study another sample
       drawn from the same class. While the sample would be different each time, we would
       still be describing the class of 1998. Ana Consists of a sample of individuals having the
       same statistical event in common (usually birth year). Enables a researcher to
                disentangle the influence of life course, cohort, and historical periods. Ana

Data archives
      Survey data are never out of date. They can be used any time for specific purposes by
      both current as well as historians. There are two primary survey data archives in USA.
      1)      The Roper Center for Public Opinion Research, at the University of Connecticut,
              maintaining more than 10,000 surveys by the Gallup Poll and by the Roper Poll,
              from the mid-1930s.
      2)      The Inter-university Consortium for Political and Social Research, at the
              University of Michigan, although just more recent archives.

Ethical concerns
       Social research deals with human beings. The principle is that: there must be no coercion,
       and no harm comes to those who are studied.

       1)      Respondents are entirely free to not participate to refuse to answer any questions.
       2)      The privacy of the respondents must be preserved. Respondents’ name, addresses,
               ID, etc. cannot be revealed. Respondents’ information must be destroyed once the
               survey is complete. Then system is to ensure confidentiality and no court will
               force a researcher to reveal any information about a respondent.



Dr. Ji                                                                     HANDOUT 8
SOC 332
                                                                                                    65


      CHAPTER 8 COMPARATIVE RESEARCH: USING AGGREGATE UNITS
________________________________________________________________________

      Learn the basic aspects of aggregate units
      Learn the skills in comparative research

               Aggregate Units

1      Aggregate units refer to units of analysis larger than the individual such as counties,
       cities, states, provinces, nations, or even the world.

2      Aggregate units analysis is based on aggregate data, which cases are larger units of
       analysis and which describe the social reality surrounding the individual. Aggregate data
       are combined larger groupings based on individual scores.

3      The aggregate data are also called ecological data and the variables represent theses data
       are called ecological variables, because aggregate data are based on larger units
       representing spatial or geographic areas. Examples are murder rate, percent urban,
       average income, population density, percent Hispanic in the 50 states, etc.

               Two Kinds of Aggregate Units

1      Areal units, which consists of aggregate units having geographic boundaries – an area.
       The commonly used areal units are nations, states, countries, cities, communities, or
       neighborhoods.

2      Social units, which consists of aggregate units having social boundaries – a residential
       district, an administrative division, an organization, or an large institution such as all
       persons in New York or Chicago, or all the Whites in Mississippi State.

3      Studies based on aggregate units are usually referred to as comparative studies.

Rates and Bases
       Rate is a ratio of the occurrence in a group category to the total number of elements in a
       group expressed out of 100.

       Rates reduce raw numbers to a common base. Rates are expressed as the numbers per
       100, or numbers per 1000, per 10,000, or per 100,000. Rates are therefore the
       standardized measurements that make comparisons possible and meaningful.

       Example: murders in Wyoming and California
       Difficult to compare and the comparisons are not meaningful
                                                                                                 66
       Murders                Population              State
       11                     480,000                 Wyoming
       205                    32,000,000              California

       The comparisons are not meaningful unless we calculated in rates.

       Wyoming Murder rate:           11/480000 X 1000               = 22.9
       California Murder rate:        205/32000000 X 1000            = 0.006

       Crude Birth Rate
             the number of lively birth in a given year per 1,000 population.
       Crude Death Rate
             the number of deaths in a given year per 1,000 population.

               Reliability
       Aggregate data are more reliable than any data based on individuals.
       There are less measurement errors involved in aggregate measures.
       Aggregate data produce higher correlations than survey data.
       Aggregate data produce zero correlations if there is no significant correlation between
       variables while the survey data may produce small correlations.

Benefits of Large Numbers
       Aggregate data are based on censuses, registration, records (births or death), or votes
       (hundreds of thousands of people) collected by governments or agents at all levels, and
       by systematic registration, which is involved large number of respondents and larger
       areas. In comparison, surveys are based on a limited number of questionnaires with a
       limited number of individuals. Therefore, aggregate data are more consistent and reliable
       than survey data (often fluctuated and less reliable).

Limits of Official Statistics
1      Many official statistics are aggregate data such as crime, births, deaths, disease, marriage,
       divorce, suicide, education, manufacturing, retail sales, public expenditures, poverty,
       welfare, and other governmental factual data such as weather, pollution, food and water
       supplies, irrigated land, paved roads, wildlife, etc.

2      Limitations
       1)     Omissions and biases-because of the way they define or collection and these
              shortcomings are unavoidable.
                      For example, US rape rate is 32.7 per 100,000 population in 1999. If only
              women are included, the rate is 70/100,000; if only for women aged 15 to 50, the
              rate rose to 130/100,000. Which is correct?
                      For another example, official crimes are underreporting: only 75% of auto
              thefts reported; 60% of rapes reported.
                      Underreporting: children under age of 12 may not be interviewed; victims
              who were murdered may not be reported.
                                                                                                 67
                      International omission and biases may be even serious because their
               accuracy depends upon hundreds or even thousands of separate cities, counties, or
               communities.

       2)      False precision: some reports provide very precise statement such as
               unemployment rate rising by one-tenth of a percent – from 5.9% to 6.0%.
               Actually this report is far too unreliable (p.168).


Validity
       Validity refers to the indicators that are used to measure the concepts they are intended to
       measure. Validity in this chapter is to answer the question: Do the indicators measure the
       concepts they are intended to measure? The primary test of validity for aggregate data
       relies on face validity. That is, to test the validity by looking more closely at how the
       indicator is defined and calculated.

       Example: “How old are you?” is the question indicator to measure the concept of “Age.”
       It has a face validity that is clearly defined.

Faulty Indicators
        Some taken-for-granted indicators are seemingly popular but may prove invalid.

       Example: Governmental unemployment rate is defined as “being out of work.”
                Seeking more valid measure is very important.

       It is calculated only based on who are currently seeking work.
       What about those who lie but still work?
       Those who stopped looking for work because they could not find one?
       Those who are really looking for jobs but are not counted?
       Those who are seeking work but are still at work?

Assessing Validity
       Validity can also be assessed by relating a particular measure to other measures or
       indicators believed to measure the same concept (convergent validity).

       Example:
       1)    Measure of conservatism ought to be highly correlated with the percent voting
             Republican elections;
       2)    Measures of infant and childhood mortality should be highly correlated;
       3)    Children’s death rates ought to highly correlate to overall life expectancy,
             availability of medical services, etc.
       4)    Percent of literacy, electricity consumption, per capita income, number of phones,
             etc. should be correlated if they are valid measures of economic developmental
             indicators.
                                                                                                  68
Selecting Cases
       Selecting cases involve two major concerns: cases should have adequate comparability
       and sufficient number of cases to ensure a statistical analysis.

Comparability
     When using aggregate data from different nations/countries, try to use the comparable
     units so that the comparisons are meaningful.
     Example:
     1)       To compare 42 nations in the world, use the same question items;
     2)       When comparing rural areas, do not mix it with cities/urban areas;
     3)       When comparing nations, cities, communities, neighbors, do not mix them each
              other;
     4)       When using social aggregates such as schools, universities, colleges, grocery
              stores, TV stations, use the same aggregates.

Number of cases
     Statistical analysis technique requires a substantial number of cases to produce
     meaningful results. Number of cases equal to or more than 30 are generally accepted. The
     general rule is that: the larger the number of cases, the more reliable of the data, the better
     the results.

       As aggregate data are on the whole involve nations/countries, states, provinces, cities,
       counties, or neighborhoods, many nations’ data sets are limited.
       Example:
       1)     192 nations in the world
       2)     300 metropolitan statistical areas in US but 25 in Canada
       3)     3141 counties and 50 states in US but 55 in Britain
       4)     3000 colleges and universities in US
       5)     47 prefectures in Japan
       6)     30 provinces in China
       7)     77 neighborhoods in Chicago and 49 in St. Louis

Analyzing Aggregate Data
      There are two commonly used approaches: Case-oriented approach and variable-oriented
      approach.

Case-Oriented Approach
1     Case-oriented approach selects two or more cases and examines them closely in order to
      explain some striking differences between them.
2     Case-oriented studies produce impressive scholarship with insights and ideas.
3     Case-oriented studies are fertile sources of concepts and theories.
4     Case-oriented studies are unsuitable for testing hypothesis.
5     It is impossible to illustrate cause-effect statements.
      Example: Comparing Japan and America has become a publishing industry.
                                                                                                 69
Variable-Oriented Approach
1      Variable-oriented approach selects a set of cases and tests hypothesis by applying
       statistical techniques such as correlation and regression to variables based on an
       appropriate set of aggregate cases.
2      Variable-oriented approach is used to test theories by checking out their implications on
       an appropriate set of units of analysis.
3      Level of measurement is based on interval and ratio because nearly all aggregated
       variables meet the criteria of ratio measurement such as crime rates, fertility rates,
       illiteracy, per capita income, poverty rates, unemployment rates, etc.
4      Discovering and excluding outliers because outliers distort correlation and cause non-
       existent relationships existent. Nevada is a typical example as an extreme when
       correlating marriage rates and alcohol (p. 176).
5      Since aggregate data are based on a universe or a total population such as all 50 states in
       US, significance therefore is irrelevant because we are using censuses but not samples
       and we are using parameters but not statistics.
6      Statistical limits of small n. n stand for the number of cases. Aggregate data sets often
       offer smaller number of n. As a rule, it is suggested not to include more than one
       independent variable per eight or ten cases (8-10 cases). With 50 states in USA, for
       example, one is limited to use 5 to 8 independent variables (p. 178).

Studying Change
      Studying social change involves aggregate units and nearly always areal units. They
      involve comparisons either by case-oriented or variable-oriented approaches.

Historical Data
        Although not always ready to use, there is a wealth of historical data existent.

       1. Available data based on governmental or agent documents related to registration,
          crimes, murders, immigrants, casualties, accidents, catastrophic mortality, etc.

       2. Creating data for research purposes. Historical data may not be ready for use for
          modern techniques. They may need recoding and quantifying procedures to meet
          research purposes, but they may be very useful for analyzing social changes.

Time Series Analysis
      Time series analysis examines the relationship between two or more variables based on
      the same universe but measured at a number of points in time. Time series analysis is
      based on repeated measures of the same variables over time. Points in time are the units
      of analysis in time series analysis.

       1. Nonlagged Time Series – when both dependent and independent variables are
          measured at the same time for any given case, it is called nonlagged time series. For
          example, to comparing how much time spent on TV and reading newspapers.
                                                                                                   70
       2. Lagged Time Series – when there is a systematic time lag between dependent and
          independent variables or among variables, that is lagged time series. For example, to
          test the hypothesis that when the interest rates rise, the housing sale falls. Since
          housing sales may not fall immediately after interest rates rise, so researchers may
          calculate the correlation between interest rates at time 1 with housing sales at time 2.

Ethical Concerns

       People may suffer harm when aggregate units are smaller.
       For example:
       1)     Publishing family income when in a small town there is only one Hispanic family.
       2)     When studying rates of drunkenness, cocaine use, sexual activity, cheating in
              exams, etc. names of any colleges may not be identified.
       3)     US Bureau of the Census refuses to publish any small units in order to avoid
              harms.


Dr. Ji                                                                        Handout 9
Soc 332

                             CHAPTER 9 FIELD RESEARCH
______________________________________________________________________________

      Basic Procedures of Field Research

              Field Research
    1 Field research allows us the opportunities to see significant social phenomena as they are
      but not as they appear.

       Field research involves going out to the real field or natural settings to observe people as
       they engage in the activities of interest. Research is conducted in the field - in the natural
       settings in which people and activities of interest are normally found and studied.


    2 Field research is to study processes and events and to observe phenomena of interest in
      the actual settings in which the things naturally occur.

       Field research is often guided by research hypotheses and exploratory in nature.
       Anthropologists pioneered field research methods.


    3 The field is a place but not a method.

       Field research produces suggestive hypothesis after the observations are
       completed.
                                                                                                  71
   4 In comparison with surveys and censuses, which are appropriate for obtaining
     proportional facts, field research gathers data on processes involving interpersonal
     interactions.

Advantages:
      1).     Can often provide a detailed description of individuals and the social relationships
              in which individuals are involved.
       2).    Offers access to actors of complex situations and events.
       3).    Sampling procedures are better than experiments but still leave questions about
              generalizability.
       4).    In observational research, there is often a long period of social interaction
              between the subject and the researcher. The researcher can make observations of
              non-verbal behavior, which may (or may not) be consistent with the subjects’
              self-reports.

Disadvantages:
      1).     Absence of formal controls makes this research least acceptable for testing causal
              hypotheses.
      2).     Vulnerability to experimenter and testing effects depends on the investigation and
              on how well they are accepted by the group they are studying.
      3).     Very difficult to replicate.
      4).     Typically conducted on small, purposely selected samples, thus, generalizability
              of findings to large populations is limited.
      5).     Data from observational research usually consists of text-based notes that are not
              in standardized formats. Such information is difficult to sort through, organize
              and systematically analyze.

               Reliability
       The greater reliability of field research is based on the ability to directly observe behavior
       rather than trying to infer how people will act on the basis of their verbal reports.
       However, in some cases, reliability is based on what researchers heard or was told.


Deception
       1)     Field research is usually conducted by one researcher
       2)     No one else besides the researcher to evaluate what the researcher saw or heard
       3)     The researcher might engage deception
       4)     The deception may be either intentional or unintentional.
       5)     Field research is more reliable if involving multiple observers.

Field Surveys and Censuses
       1) Field surveys refer to observation in the field on some specific group of people for
           some specific purposes.
       2) A census refers to surveys on everyone in a given community of people. If research
           purpose is for proportional facts for a manageable community, a census is needed.
                                                                                                     72

       Example of a census by Napoleon Chagnon (p.192)

               A study of a “violent society” which involves 12 villages, where 70% of villagers
       over age of 40 had at least one close relative being killed by another member of tribe and
       nearly 1/2 of the males over age of 25 took part in a murder.
               Eileen Barker’s field study of Moonies/people with mental problems in Britain by
       field observation- watching, listening, taking notes, and recording - for many years

               Validity
       1)      Validity refers to measuring what you think you are measuring. It involves using
               right indicators to measure the right concepts.
       2)      Field research tends to be exploratory and descriptive. It is not directed by
               theories and does not involve links between concepts and indicators.
       3)      Questions of validity are obvious for survey research but far less obvious for field
               research because it is difficult for field researchers to classify their observations.
       4)      Reliability rather than validity is the primary concern for field research.

       Selecting Topics and Sites
       All field research begins by defining a topic of interest and identifying a set of units of
       analysis- what do you want to know and where to find out?

Relevance
      1)       Field research is exploratory and not guided by theories. Therefore there is
               immense latitude in selecting topics and places to study.
       2)      Freedom of field research tends to select topics/sites frivolous/playful so that the
               research objective may be distorted.
       3)      Field research begins with a clearly defined reason and an objective. Field
               researchers know exactly what they want to watch, listen, take notes, record,
               photograph, and learn.
       4)      The topics and sites selected must be guided by and relevant to their research
               purposes.
       5)      People who have no specific questions in field research must discover a reason
               relevant during the research.

Accessibility
       The matter of first importance for field research is the accessibility. Can you get into the
       site to observe? To observe a new pope selection? A dating teenagers? A quarreling
       couple?

Risk
       1)      Field research involves serious risk.
       2)      Privacy – dating partnership, cohabitating partnership,
       3)      Illegal crimes – drugs, thefts, robberies, etc.
       4)      Terrorism membership
                                                                                                73
       5)      Safety – housing, residences, properties, estates, etc.
       6)      In group members or outsiders?

               Entering the Field
       To enter the field, one needs to solve two problems: to be the overt or the covert observer
       roles. In either case, one need to get permission from the authorized agent otherwise may
       face severe ethical problems.

Negotiating Access
       1)     Explain the importance of the field research.
       2)     Establish benefit or positive image of the observed.
       3)     Establish trust.
       4)     Three principles: a) maximize your access- all pertinent sites; b) do not grant
              rights of censorship/editing – let the observed read the draft of your report which
              may result in important corrections but maintain the final judgment by your own;
              c) secure independent funding – so that not to be controlled or interrupted by
              others.

Neutralizing Observer Effects
       The disturbance caused by the presence of the known observers can be neutralized with
       three useful tactics.
       1) Stay long enough so that people becomes accustomed to having you around. “As a
           dull but harmless presence, to which one does not have to pay much attention.”
       2) Keep a rather low profile in the beginning as you learn to fit in and as others get used
           to you.
       3) Do not begin to be too inquisitive/curious or too intrusive/invasive until you are
           familiar with the environment and you are seen as a trustworthy person.

               Observation
       1)      Observation requires an immense amount of patience and concentration.
       2)      It can be intense boredom, hard, and difficult.
       3)      One cannot observe everything but make choices among them.
       4)      There are two observations: Unstructured and Structured

Unstructured
       Unstructured observations are informal, often impromptu/unprepared, and usually are
       recorded in a narrative fashion. Field notes take everything and sort them afterwards for
       specific purposes.

Structured
       Structured observations are more focused, intentional, and often are recorded on forms
       prepared for research purposes. Special forms designed to guide what is to be observed
       and how is to be recorded.
                                                                                                   74
              Covert observation
       People who are observed are unaware that they are the objects of research.
       1)     Researchers must be normal participants.
       2)     Researchers are limited in how inquisitive/curiosity they can be.
       3)     Researchers do not disturb the natural patterns of behavior.
       4)     Able to minimize observer effects.

              Overt observation
       People who are observed know they are the objects of research.

               Interviewing In The Field
       Field research literally is in the real world based on actual observation. It is to study
       something by looking at it; to stand close to it; to scrutinize it; to go anywhere one finds it
       and watch it. Empirical observation is the basis of social science.

       Observation is not the primary sources of data for field research, however. Much of the
       vital data by field researchers is not gathered by researchers who watch but rather by one
       who listens and who asks questions. Interviewing, therefore, is the primary tool of field
       research.

       Field researchers need rely on two kinds of people: Informants and respondents.

Respondents and Informants
      People who respond to the interviewer or the questionnaires are respondents.
      Respondents answer questions about themselves.

       Informants are persons who are assumed well informed on the mater of interest to field
       researchers and who can provide information about the group as a whole or about a
       particular members or subgroups.
               A) Informants are very valuable research resource who knows customs and
                   rituals of the group and can save lots of time for the researcher.
               B) Informants can be misleading because informants may be marginal to the
                   group, have biases, harbor resentments, and provide misinformation.
               C) It is wise to have a number of informants and check everything across
                   informants.

Systematic or Impromptu Interviewing
      Most of field research is based on impromptu interviewing – that is, unplanned and
      spontaneous, because the interaction is occurring in the “natural settings.” This is the
      most appropriate style of interaction at the beginning of the research.
      It puts people at ease and lets people become used to having observers around. It
      maximizes the opportunities for unanticipated discoveries.

       If to shape some specific conclusions and to establish proportional facts, researchers will
       go for systematic interviewing (involving a numbers of questions, for example).
                                                                                                75

              Field Notes
       1)     Record what you see and hear.
       2)     Interpret what you see and hear.
       3)     Write down or dictate field notes regularly.
       4)     Do it as soon as possible.
       5)     Organize and analyze the field notes

       These notes constitute the data based on which conclusions may be drawn. These notes
       can be computerized and the qualitative data can be quantified for analysis. Field research
       has two limitations.
       1)     It is only suitable for small groups.
       3)     Field research cannot offer proof that the correlation between attachments to the
              group and conversation was not a spurious relationship.

Organizing Field Notes
       Organizing the field notes for professional use.
       1)     Type the filed notes or get notes typed
       2)     Dictate notes typed by others.
       3)     Construct elaborate indexes to field notes.
       4)     Make multiple copies and filed under a number of topics.

Analyzing Field Notes
      Sorting field notes for analyzing them.
      1)      Sorting field notes into categories.
      2)      Searching for common patterns across incidents or discover the need for
              additional categories.
      3)      Counting the frequency of various kinds of events.
      4)      Furthering subdivide categories.
      5)      Creating cross-tabulations to seek correlations or even to seek causal
              relationships.
      6)      Many cross-tabulations correlations make it possible to seek potential causal
              relationships.

       Studying Change
       Field research is not a rapid style for studies of change. It involves lengthy period of
       weeks, months, even years of observation. The typical time is two years in the field; some
       spent 6 years as Eileen Barker’s study of Moonies in the book, for example.

       Ethical Concerns
       Serious ethical problems are involved in field observation.
       1)     The right of primacy. Not to violate privacy but interested in privacy by
              researchers. To solve this “dilemma” is to resort to fictitious names such as “
              Yankee City, Middletown, Westville, The corner boys, the Blue Gangs, etc.
                                                                                              76
       2)     The issue of deception. How far can a researcher go in concealing one’s true
              identity or motives?
       3)     The issue of take-part-in-activities/involvement. Often unwillingly, researchers
              contribute to and take part in the activities they observe. The “involvement”
              seems innocent but can be risky or dangerous in some cases. What about the drug
              sale, terrorist acts, robbery, fight between a couple, etc.?
       4)     Maintaining one’s objectivity and intellectual independence.
              But unfortunately, some field observers become committed participants in the
              group; several jointed the cults/religious group they were studying; some stayed
              with the tribes they were observing.


Dr. Ji                                                                    Handout 10
Soc 332

                       CHAPTER 10 EXPERIMENTAL RESEARCH
______________________________________________________________________________

      A basic understanding of the logic of experimental research
      Experimental designs and characteristics



Experimental Research
       1)     Research conducted by manipulating variables and randomly assigning subjects
       2)     Controls time order and eliminates potential sources of spuriousness
       3)     A typical research method to establish causal relationships

              Experimental Settings
       There are three kinds of experiments: Laboratory experiments, Field experiments, and
       Survey experiments.

Laboratory Experiments
       1)    Laboratory experiment provides maximum control over all relevant
             variables.
       2)    Lab can prevent interruptions.
       3)    Lab experiment is good for natural sciences.
       4)    Lab is hard for human beings: people cannot be brought into labs; people cannot
             be kept long; labs are ill suited for human beings; lab cannot control over people.

Field Experiments
       1)     Field experiment usually consists of two groups: experimental and control groups
       2)     Experimental groups – persons who are randomly assigned to be exposed to the
              experimental stimulus (independent variables)
       3)     Control group – persons who are randomly assigned to be NOT exposed to the
              experimental stimulus
                                                                                                77
       4)      The purpose of the control group is to provide a baseline in order to compare the
               effects of the experimental stimulus of the experimental group

Experiments in Surveys
      1)      Experiments are incorporated into surveys.
      2)      The purpose of experiment in surveys is to assess survey techniques.




               Reliability
       Reliability concerns consistent measurement.
       Replication is the primary method for assessing reliability in experiment.
       Failure to obtain similar findings is taken as proof that the original findings were
       unreliable.
       Two sources of bias produce faulty results. Subject bias and experimenter bias.

Subject Bias/Demand Characteristics
       1) Subject bias is introduced by letting the experimental subjects know that they are
           being treated while the control subjects know they are NOT being treated. That
           knowledge alone will bring subjects bias.
       2) As a result, the subjects will try to meet the expectations of the social scientists. The
           subjects tend to be cooperative and to respond to demand characteristics.
       3) To prevent subject bias, the solution is to make sure that the subjects are unaware of
           what the experiments expect and to minimize the demand characteristics.

Experimenter Bias
      Experimenter bias is introduced when the experimenters unwillingly alter their behavior
      in ways that can influence the outcomes such as more friendlier to subjects, more
      carefully give instructions, etc.

               Validity
       If things are done right/in a valid way in the experiments, then the results must be valid.

Assessing the Manipulation
       To assess the manipulation is to make sure that the independent variable really varies –
       that the manipulation is effective, so that the dependent variable could be affected. If the
       independent variable does not vary, the manipulation will not be effective.

       X               Y              Y’              (Y  Y’)
       Film            Prejudice      Prejudice

       (X = Independent variable of film. Everyone Y is exposed to X. No manipulation of X.
       The independent variable does not vary. No comparison is to be made. How do you know
       the Y changed?)
                                                                                               78

Realism
       1) A major criticism of experiments is that they are artificial and the independent
          variables are not sufficiently real, especially in a laboratory setting.
       2) The dilemma is, the more realistic the experiment attempts to be, the less convincing
          the validity is claimed to be.

              Assessing Experimental and Quasi-Experimental Designs

       Four models of experimental designs are illustrated. They are demonstrated by
       establishing the relationships between “Showing Movies” (Independent variable) and the
       effect on “Racial Prejudices” (dependent variable).

       The Independent Variable      is      Movies
       The Dependent Variable        is      Racial Prejudice

       All school children are given permission to watch Movies once a week after school to
       reduce racial prejudice. By the end of the school term of 6 months, all students are given
       questionnaires and prejudice scores are measured.


              Design 1


                                     Independent            Dependent
                                     Variables              Variable

                                                           

              Subjects              Movies                Prejudice
                                     are shown              is measured


       Features:
       1)     The subjects are students.
       2)     Every student saw the movie.
       3)     Do students who did not see the movie score higher or lower compared with
              whose students who saw it? No comparison. No answer!
       4)     The independent variable does not vary!
       5)     The average prejudice score is 25 (on a scale of 0 – 1000). We learn nothing from
              this design.

              Design 2
                                                                                       79
       Pretest of              Independent             Posttest
       Prejudice               Variables               Prejudice
                                                      

       Prejudice              Movies                 Prejudice
       Is measured             are shown               is measured


Features:
1)     Prejudice is measured before and after seeing the film.
2)     Comparisons can be made by checking scores before and after the film is shown.
3)     The pretest score is 50 compared with posttest score of 25.
4)     Design 2 is better than design 1. But,
5)     Can we say that “Showing the film does cause prejudice to decrease?” No.
       Because of following reasons:
       (1) History – any event occurring simultaneously with the independent variable
           that might account for the observed changes. For example, what about
           students reading stories, seeing other films, or other events occurring that
           influenced students’ prejudice?
       (2) Maturation – the effects of subjects getting older as time passes by (maturing
           for six months in this example). When getting older, people may see things
           differently as they saw it before.
       (3) Testing Effects – simply by being measured or pre-tested, the subjects may
           change their attitudes or behavior.
       (4) Instrument Effects – differences produced by variations in the accuracy of
           the multiple measurements. Pre-test questionnaires different from the post-test
           ones may cause scores to decline or to increase!
       (5) Regression to the Mean – the tendency of extreme scores to move/regress over
           time toward the group average. This is a well-known statistical principle that
           when cases are selected on the basis of scoring far from the mean, they will
           score closer to the mean on a subsequent measurement over time. So it is hard
           to know the changes due to time effect or due to the independent variable.


       Design 3


                             Pretest of              Independent         Posttest
                             Prejudice               Variables           Prejudice
                                                                       

    Experimental            Prejudice              Movies             Prejudice
    Group                    Is measured             are shown           is measured

    Control                 Prejudice              No Movies          Prejudice
                                                                                              80
          Group                  Is measured            are shown              is measured




      Features:
      1)     Introducing a control group compared with design 2.
      2)     By comparing the two groups, the differences caused by History, Maturation,
             Testing effects, Instrument effects, and Regression to the mean can be ruled out.
      3)     Selection biases, however, may occur when people are assigned to the
             experimental or control groups in Non-random ways. That means, when people
             know they are being tested or not tested, they are likely to act more responsive to
             authority.


             Design 4


                                 Pretest of             Independent            Posttest
                                 Prejudice              Variables              Prejudice
                                                                             

          Experimental          Prejudice             Movies                Prejudice
          Group                  Is measured            are shown              is measured
                 
          Randomization
                 
          Control               Prejudice             No Movies             Prejudice
          Group                  Is measured            are shown              is measured


      Features:
      1) Introducing random assigning subjects to both experimental and control groups.

      2) By giving random assignment, the experimental and control groups will look alike in
         every ways except their exposure to the independent variable.
      3) When randomization is the basis for selection, the study is a true experiment. Design
         4 is a True Experiment.
      4) Since designs 1, 2, and 3 are not based on randomization, their designs are called
         Quasi-Experiments.

Quasi-Experiments
                                                                                                   81
       Studies posed as experiments but failed to meet the essential experimental standards of
       internal validity. A not randomly assigned subject is an example. [Quasi mean “having
       superficial resemblance to.”]

              Internal Validity
       Consists of eliminating all of the sources of bias – history, maturation, testing effects, etc.
       To ensure internal validity, there must be two groups to compare and the subjects must be
       randomly assigned (as illustrated by Design 4).

Subject “Mortality”
              Refers to the loss of subjects over the duration of an experiment (due to moving,
       drop out, death, etc.)


      External Validity
   External validity of an experiment is the extent to which its findings can be generalized.

   There are two basic types of generalization: Statistical generalization and Theoretical

   generalization.


       Statistical generalization involves successfully inferring that observations based on a
       sample apply to the unobserved member of that same population.

       Theoretical generalization involves increasing the scope of tests of a theory by applying
       the theory to a variety of settings.

External Validity of Laboratory Experiments
   Laboratory experiments are rarely based on a sample of any known population of interest.

   Subjects are volunteers. There is nothing of the same kind from one group of subjects to

   another. Therefore, laboratory experiments rarely have external validity for statistical

   generalization.


       However, laboratory experiments are well designed to support theoretical generalizations.
       Virtually all laboratory experiments are designed to test a theory/theories and their results
       are generalized to the theory. That is, laboratory experiments have external validity for
       theoretical generalization.
                                                                                                   82
       Laboratory experiments do not generalize through the wall of the laboratory, but through
       its ceiling – to higher levels of abstraction. They generalize to the abstract theories from
       which their hypotheses are derived.


               Studying Change
       1)      Experiments are powerful method for studying the causes of changes.
       2)      But experiments are limited to time span. Experiments usually go for an hour or
               two, seldom span goes over two or three years.
       3)      Therefore, social scientists seldom use experiment research to study changes
               unless in some special cases.


               Ethical Concerns
       1)      Experiments on human beings, either in experimental group or control group, are
               likely bringing about negative result. The Salk polio vaccine experiments resulted
               in 4 deaths in control group – who received no vaccine injection!
       2)      The dilemma is that, however, without control group, it is impossible to know that
               vaccine really causing reducing polio for kids.
       3)      The principle in social scientific experiment is to risk no lives but there is no
               simple resolution to such concerns.
       4)      Human experiments sometimes need deception. However, when subjects were
               deceived, they must be fully debriefed at the end of the experiment.
       5)      The rule is to lower the harm to human beings at the lowest level.



Dr. Ji                                                                       Handout 11
Soc 332

                                CHAPTER 11
            CONTENT ANALYSIS AND OTHER UNOBTRUSIVE TECHNIQUES
____________________________________________________________________________________

      Content Analysis
      Evaluation Reliability and Validity


Content Analysis
1)    Systematic transformation of qualitative materials such as verbal, visual, or textual
      materials into quantitative data to which standard statistical analysis technique can be
      applied.
2)    Content analysis creates a set of codes based on concepts and rules for applying these
      codes. Content Analysis is a kind of unobtrusive measurement.
                                                                                                  83
Unobtrusive measurement
      It is a measurement based on which the objects being studied don’t know that they are
      being studied. This measurement has no effects on the subjects being studied.

Cultural artifacts
1)     An artifact is any object made by human work. A cultural artifact is any such object that
       informs us of about the physical and /or mental life of some set of human being. The
       word of object here means very broadly to include written, filmed, and recorded material.

2)     Human beings leave traces of their behavior and an immense wake of cultural artifacts
       such as the suits of armors, sculptures, buildings, tools, and other immense body of
       scholarship.

3)     Studies based on cultural artifacts involve simple elaboration such as counting,
       comparing, coding schemes, and converting into rates (known as content analysis).

Occult activities
       People may be interested in occult activities such as religious, mystical, magical beliefs,
       or practices such as psychic readings, astrology, fortune-telling, meditation, or even
       businesses, locations, etc. There are many ways to explore these occult activities.

1)     Yellow books
       listing of business firms
       listing of personal phones
       listing of names, addresses of companies, or persons.

2)     Internet homepages
       providing detailed personal information as well as information for firms and companies.
Reliability
       Content analysis is an entirely unobtrusive measure. Speeches, letters, movies, lyrics,
       magazines, and portraits, belong to certain categories and are all created for specific
       audiences. These materials are of primary interest to content analysis.

Manifest and Latent Content

1)     Manifest content refers to the explicit, clear, and perhaps superficial meaning of verbal,
       visual, or textual materials. Latent content refers to implicit meanings. “I love that kind of
       car,” for example, is an expression of manifest content that may mean the person really
       likes that car. However, the facial expression or the tone of voice of that speaker may
       suggest that the person really dislikes that car.

2)     Content analysis involves imposing categories on materials.

3)     Coders must make subjective judgments to distinguish latent from the manifest content.
                                                                                               84
4)     Two primary sources for unreliability in content analysis: unreliable codes and unreliable
       coders.

Reliable Codes
       Codes are not too subjective
              not too vague
              not biased.
       Categories do not overlap
              are exhaustive
              provide sufficient scope for variation

Reliable Coding
       Reliable coding depends on coders. If coders code scores as 2 on a variable to be coded
       as 1, then the result won’t be reliable.

       Solution:
              use several coders/multiple coders
              each codes the same materials
              compare their coding
              disagreement is carefully checked
              content analysis will not be accepted unless multiple coders are used
              ensure intercoder reliability




Validity
Deposit Bias
       Circumstances in which only some portion of the pertinent materials was originally
       included in the set of materials to be coded. As a result, that portion was not a random
       sample of the population, hence comes the deposit bias, leading to problematic validity.

       What kind of people, after all, tended to burst forth in love letters, write novels, or
       compose songs? Only less than 5% of the population. Do you intended to study them?

Survival Bias
       Circumstances in which only some portion of the pertinent phenomena was retained in
       the set of materials to be coded.

Invalid Generalizations
        The major source of invalidity in use of content analysis is to generalize findings based
        on verbal, visual, or textual materials to human populations.
                                                                                                 85

       Content analysis focuses on objects but researchers really want to know is the people’s
       behavior. Therefore, drawing conclusion about the population from a sample is the real
       focus of a researcher for content analysis.

       Example - Leo Lowenthal studied cultural values shifts by coding biographies that
       appeared in popular magazines between 1901 and 1941.

       Hypothesis:     There is a major shift from “idols of production” (founders of larger
                       corporations) to the “idols of consumption” (movies and sports stars) from
                       1901 to 1941.

       Findings:       Idols of production dominated magazine biographies prior to WWI but the
                       pattern had shifted to biographies of idols of consumption after 1920s.

       Results:        Supported. Good example of generalizations.


Selecting Units and Cases
       Different units of analysis are always problematic to generalize results based on one kind
       of units of analysis to another variety - from aggregates to individuals, for example. Be
       sure that always draw conclusion about the people from any objects or materials under
       study.




Appropriate Units
      Be clear about units of analysis under study.

       Hypothesis usually tells the units of analysis.
       “Are movies more violent today?”
       -The units of analysis are movies.

       “TVs have become important means of socialization of kids.”
       - The units of analysis is TVs.

       “MacroCase is one of the mostly used soft-wear techniques”
       - The units of analysis are MacroCase.

       “The American business communication is less formal”
       - The units of analysis business letters.

Sample versus Census
                                                                                                 86
       Whether to sample or use census depends on how many cases you want.

       It is usually necessary to select a sample of cases. In whatever cases, one needs to define
       the population or universe of cases to be sampled, and then to use random procedures to
       select cases.

       Example
       If one is to sample ads for a particular newspaper with interest in “the shifts or changes,”
       one needs to code all ads of a particular kind for a period of years, say, from 1960 to
       2000, so that to draw conclusion from the sample


Constructing Coding Schemes
1)    A content analysis coding scheme is a questionnaire that coders fill out on behalf of each
      case. It must adhere to the fundamental principles of question construction (Chapter 7).

2)     Prepare a draft of the coding scheme and try it out on a few cases. Find problems and fix
       it to fit your research objective.

3)     When creating and refining a coding scheme, check the following several issues to ensure
       the coding scheme fit your research goal:
       A)     what are you going to do with the data after they are coded?
       B)     what are your hypotheses?
       C)     what data do you need to test them-are you leaving something important?
       D)     multiple coders will be used to ensure intercoder reliability.



Studying Change
1)    Content analysis often examines social change and trends by studying cultural artifacts,
      magazines, newspapers, and by coding Yellow Page listings from past years.
2)    Studies of change are usually based on verbal, visual, or textual materials.
3)    Try to avoid deposit or survival biases.
4)    The selection of units of analysis is appropriate and the coding categories clear.


       Examples of president’s campaigns:

       1)     The average quote of a candidate’s words was           14 lines        1960

                                                                     6 lines         1992

       2)     Most campaign statements represent                     their polices   1962

              80% of campaign statements were                        basis of strategy1992
                                                                                                87
               interpreted on the

       3)      Leading publications have shifted
                     from reporting what the candidates say to                    1962
                     featuring partisan interpretation of what is really going on 1992



Ethical Concerns
1)     In general, unobtrusive methods are of no harm, but may pose problems if not careful.
2)     Company documents or private letters must be given permission before one can use them
       or code them.
3)     The confidentiality of the people involved must be preserved. Individual information
       cannot be identified or revealed. The same principle should apply to individual records,
       arrest records, credit histories, academic transcripts, personal files, etc. Otherwise, one
       may commit ethical or even legal violations.

				
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