Why Can't I Do This?!?!?!?

I have been sitting in front of this computer for nearly 5 hours now and I just can't face my Methods notes. I am forcing myself to do something NOW since I was just reminded that the new season of the Shield starts tonight @ 10. So can I do this in two hours? Do I care? (Yes, of course I do - now I just need to convince my brain of that.)

So, now that the multiple guess is over... what'd you think?

So what'd you think?

The last page of Q threw me for a loop - I swear they're taken from the next section or something... I remember one of the q was about what type of variable something was, and the other thing about figs and high income -- was that bivariate??

I thought he put the Q from the worksheets at the beginning of the test, rather than the end, like he did last time. I think I preferred that, rather than getting in there and hitting a brick wall.

I do NOT look forward to the stupid essay part (for his class, it's my least favorite part cause I study and study and study for it but always get in there and forget some important part of it). :-( Yuck.

So much for bed... :-(

So I got in bed, and not so much with the sleep... :-(

From the Ch7 Hmwk, 2 q are throwing me for a loop:

Professor Mallory determines the distribution of students on 5 variables in her target pop. She then selects students who fill the pre-established proportions of people in each combo of variables. Which stratgey?

A: Quota.

WTF does the determining distribution thing mean? She did a pre-study to learn about her pop?!!


Prof Rosenberg takes random sample of stud from 5 MI colleges. He then phrases his report in terms of "all MI college students". To what group of elements is he generalizing?

A: Population

Isn't he also committing the error of ecological fallacy? He sampled only 5 public universities and isn't qualified to make statements about "all" anything. Additionally, how can the answer be population, since the generalized statement he's talking about WASN'T his population?!!

I'm gonna try this sleep thing again, now.

I am so heading to bed... I'm stupid-tired and don't think I'm getting anything out of going over and over and OVER this stuff. I wish I had blank homework assignments so I could test myself (mine are written all over, not like I could just cover the answer up... duh!). I guess I'll know for the future ::shrug:: -- that is, if I live through this damn test. :-( ). I *do* wish the man used a curve, this shit is hard!

Much luck to you tonight and tomorrow. I'll be checking this before chem in the AM and again after class.

Does anyone remember any of the Multiple Guess Questions?

Yeah, at this point it is truly Multiple Guess for me. And as I did not take the time to make notes after the preview of the questions the other day I am now feeling very SCREWED!!!

Crystal, I am starting to reply to your "questions in red" now. Sorry, I was gone all weekend.

7:oopm the night before the MC Methods and I'm just getting started... I feel awful. Especially after seeing all the work you have done so far Crystal.

Let's get it started...

Hope

Ch7

sampling - process of selecting observations
probability sampling - generalizable from sample to population, requires random selection.

NONprobability Sampling:

• Reliance on Available Subjects/Convenience - no control over representativeness of sample, generalization from data is V. limited.

• Purposive/Judgmental - goal to compare left-wing and r-wing students, so you sample 2 orgs only - your goal is comparison so this is OK, but can't generalize to L and R-wing students in general. Also, interviewing students who don't attend school rally to learn about school spirit.

• Snowball - when pop difficult to locate (homeless introduce to other homeless, who introduce to other homeless...). Results are of questionable representativeness, used freq for exploratory res.

• Quota sampling - sampling based on knowledge of a population's characteristics. Selection of sample to match set of characteristics. Quotas based on vars most relevant to study. Quota frame MUST be accurate. (what is QF?)

• Selecting Informants - member of group who can talk about group. Be V careful with selection, to pick a most normal, centrally accessible, many contacts informant.

Probability Sampling:

• to provide useful descriptions of total pop, sample must contain same variations that exist in pop. Every member of pop has = chance of being selected for sample.

• Bias: selections are not typical or representative of larger pop.

• Sample is rep of pop if aggregate char of sample approximate same aggregate chars in pop. Samples that have equal probability of selection method samples are labeled EPSEM.

Element - unit info is collected on and provides basis of analysis (generally people)
Population - “theoretically specified aggregation of study elements” (dumb)
Study Population - aggregation of elements from which sample is actually selected. (Can be artificially limited).
Random Selection - each element has = chance of selection independent of other event in selection process. ('selection' of head or tail in quarter flipping is independent of previous coin tosses)
Sampling Unit - element or set of elements considered for selection in some stage of sampling (??? How is this different from study pop? )
Probability Theory - sampling teqs that produce representative samples and analyze results of sampling statistically. Provides basis for estimating parameters of a pop.
Parameter - summary description of a var in a POPULATION.
Statistic - summary description of a var in a SAMPLE
Sampling error - degree of error to be expected for given sample design (allowed bec of probability theory -- how closely are sample stats clustered around true value?)

What do we have to know of sampling and std error? I've covered nothing, here.

Confidence interval/level of confidence - express accuracy of sample stats in terms of level of confidence that stats fall w/in an interval from the parameter (sample estimate, cause we don't know the Parameter). Additionally, provides basis for det sample size.

How much of this do we need to know?

Sampling frame - list of elements from which probability sample is selected. (sample of students taken from roster, then the roster is the sampling frame). Why is this not the study pop?!!

Types of Sampling Designs:

Simple Random - once sampling frame established, assign random number to element in list, then use table of random numbers to pick study sample.

Systematic - every kth element in a sampling frame, with first element sel at random. Be V AWARE of the dangers of periodicity (every 5h house on block is the corner house, and as such, is abnormal).
Sampling interval - std dist bet elements sel in sample.
Sampling ratio is proportion of elements in pop that are selected. (1/10 if every 10^th person is selected)

Stratified - greater degree of representativeness. Org list into homogeneous subsets and pull every kth element from that list, making sure that subsets are in same proportion as pop.

Multistage Cluster Sampling
: initial sampling of clusters, then sel of elements w/in each sel cluster.

- Highly efficient but less accurate sample. 2 stage cluster sample is subject to two sampling errors. Maximize # of clusters while dec # of elements w/in each cluster.

• Typically elements composing given cluster w/in pop are more homogeneous than is total pop. (residents of block more alike than nation)

• Additionally Multistage cluster sampling can be Stratified.

PPS (Probability Proportionate to Size) Sampling: type of cluster sampling. Used when clusters are of greatly differing sizes (ie city block vs suburban block) so that everyone gets the same = chance of being selected.

Further refinement is the Disproportionate Sample and Weighing: Disproportionate - you may decide to sample to get higher # of some small subpopulation so that you can have sufficient #s to analyze results with some meaning. Analysis of 2 samples needs to be separate - but then they can be compared. Weighting happens when you want a composite sample of the entire pop, then you have to weigh samples when 'adding them back together'.

Ch 6

Multiple indicators - several q on survey addressing same concept, also interviewers asking 'essential q' and then 'extra q' that asks that same info slightly differently.

Composite measures are used freq in quantitative res: 1. no single indicator covers meaning, 2. want to use ordinal measure of var with a range of variation, 3. data analysis.

Indexes and scales (esp scales) are efficient data reduction devices: assign scores, not loosing details of response.

Index v Scale:
Both:
ordinal
rank-order units of analysis in terms of vars
score gives indication of relative 'religiosity'
use composite measures: measurements based on more than one data item. SHOULD ONLY MEASURE ONE DIMENSION. (unidimensionality)
Diffs:
Index: accumulate score assigned to individual attributes (1 point for each q).
Scale: assign score to patterns of resp; takes advantage of diff in intensity among attribs of same var to ID distinct patterns of response.
'idealized action patterns'

Index Construction:

1. In selecting items for index, does the item have face validity?

2. Are you measuring the concept in a general way or a specific aspect of the concept? (bal measure of religiosity or a measure only of ritual participation?)

3. Select items differing in variance (1 item ID as conservative, another might pick up a few more)

*I don't like the 'general or specific' and the 'exam of empirical rel' step on p150. Help?

4. Examine empirical rel among items included in proto-index. (Empirical Rel - when answers to one q let predict answers to other qs).

5. Find bivariate relationships among items and drop items w/o relationships to other items on index, unlikely that they really measure concept. Bivariate Rel - rel bet 2 vars, responses on 2 vars likely to get same responses. Also, drop items that VERY strongly correlate as they're prob the same q.

ASIDE:

Indicators should be rel if they are 'effects' of same var. However, not case when indicators are 'cause' rather than 'effect' of variable.

Social interaction - time spent w/ fam, friends, coworkers. 3 indicators 'cause' degree of social interaction.

Self-esteem - 'good person', 'like self'. Person w/ high self esteem should Y both.

Decide if indicators are causes or effects of var before using inter correlations to assess validity.

Here's another place I fall apart -- do we need to know the percentage tables he used to analyze his physician example? I don't remember Nigem covering it in class or making a big deal out of it - do we not need to know it? Can you summarize? (?!! P 154 - 155?!!)

Index Scoring:
Assign scores for particular responses.
Decide desirable range of index scores (how many index 'points' is conservative?).
How far into extremes does index extend (consider variance and the tails of the normal curve). Your goal is to have an adequate # of cases at each point on the index, generally index scoring is equally weighted.

How do you handle missing data?

• If few cases, simply exclude from index construction. (Will exclusion result in biased sample?)

• Treat missing data as one of available responses (you might decide that failure to answer meant no, if respondent answered yes and left some blank)

• Analysis may yield meaning - respondents failed to answer a Q were generally consistently conservative on other items -- you may decide to score accordingly.

• Assign 'middle' value

• Use proportions of what observed (if 4/4 answered strongly conservative, may score '6', if 2/4, may score '3')

Best method is to construct index through multiple methods and compare results.

Index Validation - does the index measure what it says it measures? (Does your index rank-order people in their degree of conservatism?)

1. Item Analysis - internal validation, examine extent to which composite index is related to or predicts responses to individual items it comprises. If item adds nothing, trash it.

2. External Validation - people who scored as politically conservative on your index should score as conservative by other methods as well. (most conservative index scorers should be most conservative on all other q on survey)

3. Bad Index vs Bad Valida tors - This can be a problem, check carefully.

Scale Construction:

Scales offer more assurance of ordinality by taking into consideration intensity structures among indicators. (Is the senator who voted for 7 moderately conservative bills more conservative than the senator who voted for 4 strongly conservative bills (rejecting the others cause they were too moderate)?

Bogardus Social Distance Scale -- teq for determining willingness of people to socially relate to certain other people. If person allows contact next door, they'd allow person to live in country... etc. Logical structure of intensity.

1. live in country?

2. Live in community?

3. Live in neighborhood?

4. Next door?

5. Marry child?

Thurstone Scale - format for generating groups of indicators of var that have empirical structure to them. Judges given list of indicators of a var and rated on intensity. Disagreement among Judges gets indicators tossed as ambiguous. Then items selected to represent each scale score, which then used in survey. Respondents who hit a strength of 5 would be expected to 'hit' the lower indicators too, but not hit indicators above 5.
Incredibly resource intensive, would have to be updated periodically.

Likert Scaling - goes one step beyond regular index construction, calcs avg index score for those agreeing with individual statements making up 'index'. As result of item analysis, respondents could be rescored using the avg index score for each item.
Too complex to be used frequently.

Semantic Differential - determine dimensions you want subjects to judge something and then find 2 polar opposite words along each dimension. (dimension of music: enjoyability, use enjoyable, unenjoyable. Dimension of music: complexity, use complex and simple. Etc). Allow individuals to check box along those continuums.

Guttman Scaling - based on notion that anyone giving strong indicator of some variable will also give weaker indicators.

Scale types - patterns of response that form a scale. See Table 6-2 for example.

I'm iffy on the ex given in book - I understand the example but can't extract a def from it. Skipped rest of Guttman Scale.

Typologies: summary of intersection of 2 or more variables, creating set of categories or types. Typologies MUST be used as the independent variable.

Also iffy on this, need def from somewhere else??

Ch5 Recap

Ch 5 Review:

Scientists like to use measurement for the word observation, because it refers to careful, deliberate, observations for the purpose of describing attributes composing a variable.

If it can be conceptualized, it can be measured.

(Note: I hate this chapter and the concept and subconcepts involved in conceptualization.)

conception - ideas about a subject, internal, individual, 'mental images'
conceptualization - process of coming to agreement about meaning of term
concept - result of conceptualization (Kaplan: is a "family of conceptions")

Kaplan's 3 classes of observables\types of observation:
Direct - simply (checkmark)
Indirect - more removed than direct observation (minutes of past mtgs)
Constructs - created, theoretical, not-directly-observable (IQ)

Reification - reguarding constructs as real

indicator - sign of absence or presence of concept (helping animal, crying during movie: indicators for concept of compassion)
dimension - grouping of indicators within a concept, a specifiable aspect of concept (compassion for animals vs compassion for humans).

Complete conceptualization involves specifying dimensions and finding indicators for each.

Interchangeability of indicators - idea that if indicators are valid, then they all rep same concept, then all will give same results. (We should get same research results no matter which indicators we use, as long as our indicators are valid).

Nominal defination - assigned to term w/o claim that def rep 'reality'. Arbritrary.
Operational defination - specifies exactly how concept will be measured. Max clarity about concept for given study.
Working defination - def for the purposes of inquiry - whatever you want it to be.

Hermeneutic circle -- cyclical process of deeper understanding

Conceptualization is also the continual refinement of the understanding of a concept.

Progression of measurement steps:

Conceptualization
|
\/
Nominal Definition
|
\/
Operational Definition
|
\/
Measurements in the real world

Srole scale: another measure of anomia (5 statements)
Durkheim theories about suicide and anomie.

Defs more problematic for descriptive research than for explanatory res. Why?
For example - descriptive: What does 'being unemployed' mean? Who qualifies? What people can be unemployed (children)?
Explanatory Res: Does conservatism inc with age? Not matter what def of conservative used, the relationship bet that and age is of interest, not the exact def used.

Conceptualization is the refinement and specification of abstract concepts, and operationalization is the dev of specific procedures.

Operationalization choices:
Range of Variation - how much is acceptable?
Variations between the Extremes - degree of precision, how fine are your distinctions among attributes composing your variable? (Do you care if person is 17 or 18?)
Be clear about which dimensions of a concept you're covering.
Attributes composing your variable should be exhaustive as well as mutually exclusive.

Levels of Measurement:
Nominal: labels for characteristics (gender, hair color), analysis available is that 2 people are the same or different.
Ordinal: rank-ordered (conservatism, alienation), analysis can say that Person A is “more” than B in terms of var.
Interval: distance separating attributes of a var HAS meaning, but lack of absolute zero. (IQ test) Can say 'how much' more A is over B, can not say IQ of 150 is 50% more intelligent than someone w/ IQ of 100.
Ratio: Intervals with a true zero point. (K temp, age, income). Can say that A is twice B.

Precision - fineness of distinctions made bet attributes of var. (43 vs 'in 40s')
Accurate - how closely something matches reality.
Reliability - does your teq give repeatable results when measuring the same object? Problem with researcher subjectivity.

Methods of ensuring reliability:
Test-Retest method: measure multiple times and compare results (survey repeated 3mo later - get same results?)
Split-Half: make more than one measurement of same concept. (Take q that test concept and analyze after splitting Q list in half).
Use Established Measures - use someone elses'
Test reliability of res workers -- call subset of samples and verify info, code results through multiple people

Validity
-- Does a measure reflect real meaning of concept?
Face validity - common sense
criterion-related validity - predictive validity - external criteria (validity of driver's test det by scores people get and rel to later driving records)

*Need help on predictive validity, in the making examples step. I understand the concept but can't make em, yet.

Construct validity - logical rel among vars. (marital satisfaction relates to cheating)
Content validity - does the measure cover range of meanings w/in concept. (does our measure consider all types of prejudice?)

Back at it.... :-(

Ch4 Summary:

3 goals of research:

1. Exploration - familiarization, focus groups. Shortcomings: seldom satisfactory answers, not representative. 3 reasons for use:

   • curiosity

   • pre-study for larger study

   • dev methods for larger study

2. Description - describe accurately and precisely chars of a population (Ex: US Census)

3. Explanation - Answer Why? (Ex: ID vars to explain why City A higher crime rate than City B)

   • Nomothetic Explanation - few factors that lead to 'most' changes in results

   • Idiographic Explanation - all the reasons, all the time

Criteria for Nomothetic Causality; 3:

1. Variables correlated (established relationship)

2. cause before effect (time order)

3. vars non spurious (no 3rd var)

Note what is NOT an interest of Nomothetic causality:

• Complete explanation of causes,

• Exceptions don't disprove the rule

• Rel can be true even if it only applies to minority of cases

Defs:
Necessary Cause - Must be present for result (take classes to get degree)
Sufficient Cause - Condition that guarantees effect, not the only way to get effect (take right classes, get good GPA, get degree)

Idiographic causes are sufficient but NOT necessary. (Anyone with your details of life would have attended college, however, other people attend college through different means).


Units of Analysis

Units of analysis (thing seeking to describe) are generally also units of observation, individuals being most typical. Assertions about one unit of analysis need to be based on exam of that same unit of analysis.

Ecological Fallacy - applying findings from group to individuals (Ex: crime rates in large cities, large African American pop, can't say that the AA are making the crime.)

• Individuals

• Groups

• Organizations

• Social Artifacts (are you studying marriage or marriage partners?)

Individualistic fallacy - probabilistic statements not invalidated by individual exceptions

Reductionism - reducing complex phenomena to simple explanation (Ex: answering What caused the Am revolution? - with a single factor).


Other types of Studies/Time-based:

Cross-Sectional Study - observation of sample at _one_ point in time (single US Census)
Longitudinal Study - sampling over time

• Trend Study - re sampling of same population more than once, over time

• Cohort Study - following an age group and re sampling that age group, over time

• Panel Study - follow the same group of people over time (special problems with panel attrition and possibility that dropouts will be atypical)

Triangulation - use of several different research methods to test same finding