RDII MidTerm
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| 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
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| 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
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| Repeated testing | through repeated testing we build confidence in a causal sequence
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| Controls | In testing controls must be introduced for alternative explanations that are suggested by the theory, intuition, and observation
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| 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…”
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| “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…”)
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| The principal task of all science is… | to explain
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| The framework for explanation is the _____ sequence | cause and effect
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| The process by which we build confidence is a causal sequence is ____ | Repeated Testing
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| Repeated testing | involves introducing controls for alternative explanations suggested by theory, intuition, or observation
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| Science is empirical with ___ as its defining feature | Measurement is the defining feature of science.
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| Data Sets | Collections of measurement for a specified u/a
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| clean data | Not collected but developed by filtering out unwanted influence by statistical analysis and research design
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| Better data= | more reliable study = better validity to relationships
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| two ways to develop data and to filter out unwanted influences | Statistical analysis (most common) and Research design
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| 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
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| What is the time orientation, relative emphasis, and variance Control strategy of data development through research design | ex ante, conceptual, eliminate at source
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| 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)
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| Bias | Errors that seep into a structured inquiry at one or more junctures
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| 5 sources of bias | sampling, selection, measurement, model specification, statistical
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| Steps of a Structured Inquiry; Step 1: universe/population | Identify "target" population (no sources of bias)
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| Steps of a Structured Inquiry; Step 2: Sampling | Select a workable data subset (Sampling bias: “Is the sample representative of the larger population?”)
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| Sampling bias | “Is the sample representative of the larger population?”
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| 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?")
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| Omitted Variable Bias | “Is the model complete?""Are all of the major influences present?"
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| 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?")
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| Measurement Bias | “Do we have sloppy measurement?""Is indicator measurement reliable (accurate) and valid?""Do variables vary sufficiently
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| 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?")
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| Statistical Bias | “Are the assumptions underlying the analysis model met?"
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| Steps of a Structured Inquiry Step 6: Data Analysis Execution | Estimation and Inference (Selection Bias: “How adequate are the controls for eliminating alternative explanations?")
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| Selection Bias | “How adequate are the controls for eliminating alternative explanations?"
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| “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
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| 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
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| 3 Types of Data Structures | 1) Panel 2) Cohort 3) Cross Sectional
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| Panel Data Structure | Has an internal focus
Use U/As as own controls
controls for unwanted variance associated with individual differences
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| Cohort Data Structure | External focus
Controls for sources of unwanted influence associated with period influences
controls for temporally defined influences
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| Cross Sectional Data Structure | Controls specified variables
least useful data structure
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| Types of Design Structures | True Experiment ** Best
Quasi-Experiment
Cross Sectional
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| True Experimental Design Structure | Controls for all extraneous influences (known and unknown)but effectiveness can be diminished over time
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| 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
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| Cross-Sectional Design Structure | Depends on theory guided model specification
Weakest and least effective control strategy
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| Experimental Design Notions from 19th Century | IV and DV, effect, comparison group, randomization, ceteris paribus
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| Randomization | assigning units of analysis randomly to differing levels of the IV-refers to external validity
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| Control | rule out threats to causal inference
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| 5 Schools of Thought | Positivism (Hume), Essentialism (probabilistic versus deterministic), Concomitant Variation (Mill), Falsification (Popper), Activity (cause" as manipulation)
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| Philosopher for the Positivist Tradition | Hume
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| 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)
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| Positivism | guided by Hume's 3 conditions for inferring cause
Hume only recognized observable phenomena
causal relationship based solely on high correlations between variables
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| 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
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| 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
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| Philosopher of Concomitant Variation | Mill
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| Concomitant Variation | key to design is establishing counterfactual
implied comparisons sets up elementary comparison with and without treatment
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| 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)
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| 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
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| 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
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| 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
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| 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.
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| 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”
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| The purpose of research is to | maximize variance explained and minimize error variance
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| Three kinds of variance | 1) experimental
2) extraneous
3) error
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| Experimental Variance is associated with | the Main IV
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| The goal of experimental variance is to | MAXimize the variation
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| Extraneous variance is associated with | the covariates (Zs)
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| The goal of extraneous variance is to | CONtrol by transforming all other possible variables into constants
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| The goal of error variance if to | MINimize
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| MaxConMin | MAXimize the required systemic variance (MAX variance in X) CONtrol “unwanted” variance (CONtrol variance in Zs) MINimize the error variance
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| We want to MAXimize | required systematic variance as a way of ensuring internal validity (“the whole point of science”)
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| 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)
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| the “operational key to a model’s internal validity” (Mill’s 3rd criterion) | CONtrolling extraneous variability
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| To ensure internal validity | must control the influence of covariance and the influence of error
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| 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
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| 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)
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| 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
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| 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
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| Two sources of error variance | 1) Specification error (ex: omitted variable bias)
2) Measurement error
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| 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
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| What lowers error variance? | 1) Controlled conditions of the design
2) Measurement reliability
3) High experimental variance
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| The greater the reliability of the measurement process, the ____ the error variance | lower
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| The larger the experimental variance, the ___the error variance | lower
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| Three types of Descriptive Studies | correlational, case study, cross-sectional
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| Correlational Descriptive Study | uses measures that represent attributes of entire populations (aggregates)
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| 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
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| Advantages and Disadvantages of Case Study Descriptive Study | advantages: useful emphasis on description; valuable insights for conceptualization and measurement
disadvantages: no variation (n
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| Cross-Sectional Survey | measures are taken on individual nits of analysis within some population at a given point in time
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| 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
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| 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
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| 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
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| Longitudinal Design Types | time permitted to vary. While more costly to execute, only longitudinal designs can provide data capable of demonstrating a causal relationship.
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| 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
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| 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
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| 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.
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| Designing with Time (Mill’s second criterion) means | keeping the order of cause preceding effect
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| Time-Mapping | moving from “point-in-time” to “process” and dealing with a discrete point versus a dynamic process.
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| Micromediation | the detailed linkage and understanding of the mechanism by which the change generated
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| 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
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| Process focus of time mapping | covariation, observed variation of variable over time
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| Theory guidance of time mapping | when data should be collected, length of interval between measurements, overall duration of study
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| Six Dimensions of the Dynamic Process of Time Mapping | Continuity
Magnitude
Rate of Change
Trend
Periodicity
Duration
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| Continuity | whether a variable has a consistent nonzero value through time
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| 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.
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| Forward-moving strategy | anchored starting year - we open the window of time wider year by year keeping the base time the same
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| Backward moving strategy | anchored ending year
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| 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
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| Magnitude | the absolute level of a variable at any time
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| Rate of Change | how fast the magnitude changes per unit of time
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| Trend | long-term change in magnitude
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| Periodicity | the length in time between a common point in a cycle (if one exists)
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| Duration | the length of time a variable holds a nonzero value
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| The elaboration paradigm address Mill's ___ Criterion and ______ bias. | Mill's Third Criterion; Omitted Variable Bias
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| Elaboration of a Model | adding influences into the equation one by one (allows control of more and more of error variance)
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| 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
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| 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
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| Six Outcomes of the Elaboration Paradigm | Replication
Explanation
Interpretation
Specification
Distorter
Suppressor
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| 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
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| Specification | when the tf introduced, the partials respond differently
tf may be either antecedent or intervening
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| Suppressor | No original relationship but one emerges in the partials
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| Distorter | When tf introduced, the direction of the original relationship is reversed in the partials
the tf distorted the original relationship
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