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Research Design II

RDII MidTerm

Cause-and-Effect Sequence the framework for explanation in scientific research policy sciences are a special case because you are evaluating a policy or program intervention to see if it work and if it makes a difference
Proof not the outcome of social science research the most we can do is gradually strengthen our confidence in the validity of a causal sequence by eliminating possible alternative explanations
Repeated testing through repeated testing we build confidence in a causal sequence
Controls In testing controls must be introduced for alternative explanations that are suggested by the theory, intuition, and observation
Science is empirical Measurement in the defining feature of science and precision is achieved though quantitative measurement “When you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind…”
“When you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind…” Measurement is the defining feature of science but quantification isn’t enough (“When you can measure it, when you can express it in numbers, your knowledge is still of a meager and unsatisfactory kind…”)
The principal task of all science is… to explain
The framework for explanation is the _____ sequence cause and effect
The process by which we build confidence is a causal sequence is ____ Repeated Testing
Repeated testing involves introducing controls for alternative explanations suggested by theory, intuition, or observation
Science is empirical with ___ as its defining feature Measurement is the defining feature of science.
Data Sets Collections of measurement for a specified u/a
clean data Not collected but developed by filtering out unwanted influence by statistical analysis and research design
Better data= more reliable study = better validity to relationships
two ways to develop data and to filter out unwanted influences Statistical analysis (most common) and Research design
What is the time orientation, relative emphasis, and variance Control strategy of data development through statistical analysis? ex post facto ("mopping up"), computational, data adjustment
What is the time orientation, relative emphasis, and variance Control strategy of data development through research design ex ante, conceptual, eliminate at source
Research design the study of techniques for organizing a research process to achieve variance control prior to and during data development (compare to after-the-fact cleaning and controlling achieved through statistics and econometrics)
Bias Errors that seep into a structured inquiry at one or more junctures
5 sources of bias sampling, selection, measurement, model specification, statistical
Steps of a Structured Inquiry; Step 1: universe/population Identify "target" population (no sources of bias)
Steps of a Structured Inquiry; Step 2: Sampling Select a workable data subset (Sampling bias: “Is the sample representative of the larger population?”)
Sampling bias “Is the sample representative of the larger population?”
Steps of a Structured Inquiry Step 3: Model Specification Define the basic concept in the Model (Model Conceptualization and Omitted Variable Bias: “Is the model complete?""Are all of the major influences present?")
Omitted Variable Bias “Is the model complete?""Are all of the major influences present?"
Steps of a Structured Inquiry Step 4: Measurement: Variation Creation Assign values to U/A attributes (Measurement Bias: “Do we have sloppy measurement?""Is indicator measurement reliable (accurate) and valid?""Do variables vary sufficiently?")
Measurement Bias “Do we have sloppy measurement?""Is indicator measurement reliable (accurate) and valid?""Do variables vary sufficiently
Steps of a Structured Inquiry Step 5: Data Analysis Approach Select appropriate data analysis model (Statistical Bias: “Are the assumptions underlying the analysis model met?")
Statistical Bias “Are the assumptions underlying the analysis model met?"
Steps of a Structured Inquiry Step 6: Data Analysis Execution Estimation and Inference (Selection Bias: “How adequate are the controls for eliminating alternative explanations?")
Selection Bias “How adequate are the controls for eliminating alternative explanations?"
“All models are wrong, but some are useful." A fantastic design helps limit the bias in a model so it is less wrong; even though a model may not be perfect it can still help say things about the world
Unit of Analysis The major entity that you are analyzing in your study U/A determined by the actual analysis done in the study; all of the study factors are things that describe the u/a
3 Types of Data Structures 1) Panel 2) Cohort 3) Cross Sectional
Panel Data Structure Has an internal focus Use U/As as own controls controls for unwanted variance associated with individual differences
Cohort Data Structure External focus Controls for sources of unwanted influence associated with period influences controls for temporally defined influences
Cross Sectional Data Structure Controls specified variables least useful data structure
Types of Design Structures True Experiment ** Best Quasi-Experiment Cross Sectional
True Experimental Design Structure Controls for all extraneous influences (known and unknown)but effectiveness can be diminished over time
Quasi-Experiment Design Structure Depends on design structure Uses either time or comparison groups or both to diminish/eliminate specific threat to internal validity Introduced in 1950s by Campbell Do not use randomization to create comparison groups
Cross-Sectional Design Structure Depends on theory guided model specification Weakest and least effective control strategy
Experimental Design Notions from 19th Century IV and DV, effect, comparison group, randomization, ceteris paribus
Randomization assigning units of analysis randomly to differing levels of the IV-refers to external validity
Control rule out threats to causal inference
5 Schools of Thought Positivism (Hume), Essentialism (probabilistic versus deterministic), Concomitant Variation (Mill), Falsification (Popper), Activity (cause" as manipulation)
Philosopher for the Positivist Tradition Hume
Hume's 3 conditions for inferring cause 1)contiguity (no middle factor x→Y) 2) temporal precedence (order) 3) constant conjunction (apparent all over - when X varies Y always varies)
Positivism guided by Hume's 3 conditions for inferring cause Hume only recognized observable phenomena causal relationship based solely on high correlations between variables
Why is the concept of "cause" even needed? Challenger to Positivism? B. Russell challenged positivism and the need for causal relationships Causal chains or intervening variables established the unacceptability of positivism
Essentialist Tradition associated with reductionism and determinism cause suggests variables that are necessary and sufficient for the effect factor must work all of the time to be a cause observed causal relationships are more likely to be probabilistic than deterministic
Philosopher of Concomitant Variation Mill
Concomitant Variation key to design is establishing counterfactual implied comparisons sets up elementary comparison with and without treatment
Mill's Criteria for Cause-and-Effect Relationships 1) Variables must co-relate 2) Time order (cause before effect) 3) Eliminate alternative explanations (the reason control is critical)
Falsification from Popper base of knowledge is on ruling out alternative explanations can never prove a theory to be true - but can prove it to be false the more causal variables are ruled out, the more confident we become with the relationship between IV and DV
Activity theory of Causation purposefully move X to observe effect on Y the concept of cause implies manipulation started idea of bringing idea into the lab
Great Contribution of Concomitant Variation Great Contribution: to realize the comparison of situations where a particular threat to valid inference was/wasn't operating is key to assessing whether the threat may occur for any observed relationship
Variance of a random variable (or distribution) the expected, or mean, value of the square of the deviation of that variable from its expected value or mean.
Statistical Variation is a measure of the amount of variation within the values of that variable, taking account of all possible values and their probabilities the “currency for scientific exchange”
The purpose of research is to maximize variance explained and minimize error variance
Three kinds of variance 1) experimental 2) extraneous 3) error
Experimental Variance is associated with the Main IV
The goal of experimental variance is to MAXimize the variation
Extraneous variance is associated with the covariates (Zs)
The goal of extraneous variance is to CONtrol by transforming all other possible variables into constants
The goal of error variance if to MINimize
MaxConMin MAXimize the required systemic variance (MAX variance in X) CONtrol “unwanted” variance (CONtrol variance in Zs) MINimize the error variance
We want to MAXimize required systematic variance as a way of ensuring internal validity (“the whole point of science”)
Extraneous variability is variability that comes from outside the model and is unexplained by the model; it is a change that involved an alteration of a variable that is autonomous (unaffected by the workings of the model)
the “operational key to a model’s internal validity” (Mill’s 3rd criterion) CONtrolling extraneous variability
To ensure internal validity must control the influence of covariance and the influence of error
We want to CONtrol extraneous variability to protect internal validity (the X -> Y relationship, does it exist, how strong is it, is it a plus or minus?) we want to pull out those Zs “cleanly”and turn them into constants so we have controlled for the other influences
Five techniques for CONtrolling the influence of an extraneous variable 1) Eliminate the variable (ex: only study females is gender is an issue) 2) Randomization (best way!) 3) Build the variable into the model 4) Match U/As (“poor man’s substitute”) 5) Statistical control (ex post facto)
Statistical Control as a Technique for CONtrolling the influence of an extraneous variable this is a method of data adjustment where you subtract the variance attributable to the control variable from the group means
We wish to MINimize error variance which is variability as a result of randomness and are self-correcting has little to do “error” but variability due to errors can be a part of the error variance really refers to sources of variability we are not focused on
Two sources of error variance 1) Specification error (ex: omitted variable bias) 2) Measurement error
Because systematic variance is due to those variables under investigation and error variance encompasses every other source of variability, the two together equal the total observed variance
What lowers error variance? 1) Controlled conditions of the design 2) Measurement reliability 3) High experimental variance
The greater the reliability of the measurement process, the ____ the error variance lower
The larger the experimental variance, the ___the error variance lower
Three types of Descriptive Studies correlational, case study, cross-sectional
Correlational Descriptive Study uses measures that represent attributes of entire populations (aggregates)
Advantages and Disadvantages of Correlational Descriptive Study advantages: easy to get data; measures of association are easy to compute disadvantages: ecological fallacy; inability to control for confounding factors; no comparison group; correlation does not imply causation
Advantages and Disadvantages of Case Study Descriptive Study advantages: useful emphasis on description; valuable insights for conceptualization and measurement disadvantages: no variation (n
Cross-Sectional Survey measures are taken on individual nits of analysis within some population at a given point in time
Advantages and Disadvantages of Cross-Sectional Survey advantages: useful for generating variance for statistical analysis disadvantages: must use expost facto statistical adjustments of data; unable to infer causality – time is constant and constants can’t explain variance
Cross-sectional study and time time held constant (therefore no valid causal inference is justifiable); variation in all variables takes place at one point in time. Offers no real basis for inferring a causal relationship. The basis for the bulk of social science research findings
Trend study and time Consecutive cross-sectional snapshots of DV across time offers no real basis for inferring a causal relationship can compare groups sharing the same age, but “period” effects are left to vary time varies opening up possibilities for other influences
Longitudinal Design Types time permitted to vary. While more costly to execute, only longitudinal designs can provide data capable of demonstrating a causal relationship.
Panel Study and Time powerful way to elimiate effects of individual differences (and their correlates) follow individuals in groups across time use Ss as their own controls e.g. pre/post-test designs and time-series designs
Cohort Study and Time Measurements are taken on different units of analysis across time Groups of units of analysis whose timing on a background factor (birth, date of entry into the labor force) are used in sequence The groups themselves are different people
Temporal Order: Addressing Mill’s Second Criterion Time allows separation of events, things in space, relative to one another. A huge factor of structured pattern influence.
Designing with Time (Mill’s second criterion) means keeping the order of cause preceding effect
Time-Mapping moving from “point-in-time” to “process” and dealing with a discrete point versus a dynamic process.
Micromediation the detailed linkage and understanding of the mechanism by which the change generated
Conventional View of Time Mapping Regression coefficients are the ‘laws’ of social science but these coefficients usually come from cross sectional designs so the alternative is that we must focus of dynamic processes using longitudinal designs
Process focus of time mapping covariation, observed variation of variable over time
Theory guidance of time mapping when data should be collected, length of interval between measurements, overall duration of study
Six Dimensions of the Dynamic Process of Time Mapping Continuity Magnitude Rate of Change Trend Periodicity Duration
Continuity whether a variable has a consistent nonzero value through time
Temporally recursive regression shows the lifecycle effect and allows us to see the coefficient in the process of becoming; We are trying to determine the variety of forces influence the DV over time.
Forward-moving strategy anchored starting year - we open the window of time wider year by year keeping the base time the same
Backward moving strategy anchored ending year
Diagonal moving strategy Looking at a period of time, sequentially, increment on a yearly basis, continued influence of a time period e.g. the 40’s on
Magnitude the absolute level of a variable at any time
Rate of Change how fast the magnitude changes per unit of time
Trend long-term change in magnitude
Periodicity the length in time between a common point in a cycle (if one exists)
Duration the length of time a variable holds a nonzero value
The elaboration paradigm address Mill's ___ Criterion and ______ bias. Mill's Third Criterion; Omitted Variable Bias
Elaboration of a Model adding influences into the equation one by one (allows control of more and more of error variance)
Replication shows same relationship the new test factor doesn't change the original relationship the test factor may have an independent effect on the DV but we don't know the original relationship found in both partials so can get rid of test factor
Explananation The original result goes away in the partials - effect reduced to zero or close to it the third variable is antecedent (Z->X->Y) so the original relationship is spurious
Six Outcomes of the Elaboration Paradigm Replication Explanation Interpretation Specification Distorter Suppressor
Interpretation The original result goes away in the partials - effect reduced to zero or close to it the third variable is intervening (between) (X->Z->Y) so the original relationship is spurious
Specification when the tf introduced, the partials respond differently tf may be either antecedent or intervening
Suppressor No original relationship but one emerges in the partials
Distorter When tf introduced, the direction of the original relationship is reversed in the partials the tf distorted the original relationship
Created by: 18801171