1. Illustrating DyadR Using the Truth & Bias Model
David A. Kenny
Powerpoint
davidakenny.net/DyadR/DyadR.ppt
Data
davidakenny.net/DyadR/Acitelli_Individual.sav
davidakenny.net/DyadR/Acitelli_Dyad.sav
davidakenny.net/DyadR/Acitelli_Pairwise.sav
Output
davidakenny.net/DyadR/DyadR_Output.pdf

2. Overview Truth and Bias (T&B) Model Illustration from Acitelli Dataset
Use DyadR Programs to Estimate and Test a T&B Model

3. The Truth and Bias Model (T&B)
West, T. V., & Kenny, D. A. The truth and bias model of judgment (T&B). Psychological Review, 118, 357–378.

4. Basic Idea of the T&B Model
Theoretical and empirical framework designed to address the basic questions of how accuracy and bias operate, and the nature of their interdependence
Judgment is determined by the truth (T) and bias (B).
Accuracy is defined as a relationship not an event.

5. Types of Bias in the T&B Model
Some biases are measured using variables
For the bias of assumed similarity, bias is measured using the perceivers’ self-judgment.
For the bias of perseveration, bias is measured as prior judgment.

6. Directional Bias
How strongly judgments are pulled away from the mean level of the truth
The extent to which perceivers’ under-or-overestimate the average value of the truth
With proper scaling can be obtained from the intercept in the regression equation.

7. Moderators
Variables that inform how the processes of accuracy and bias occur by influencing the strength of the forces
Can moderate the truth force, the bias force, directional bias, or all three
A moderator may have one effect on the truth force and another effect on the bias force.

8. Key Measurement Issue
Measure Truth, Bias Variables, and Judgment using the same scale.
Center Truth, Bias Variables and Judgment using the truth mean.
Directional bias becomes the regression intercept in the model.

9. Acitelli Study
Sample 148 married and 90 dating couples in the Detroit area
Outcome
Your partner’s relationship with the child (children) is good (1 to 5).
Predictor Variable
Your relationship with the child (children) is good (1 to 5).
Covariate: Married (1)vs. dating (-1)

10. T&B Example Woman’s Perception of Man’s Rel. with Child
Woman’s Rel. with Child
Female Bias
Woman’s Perception of Man’s Rel with Child
Female Accuracy
Male Accuracy
Man’s Rel. with Child
Man’s Perception of Woman’s Rel with Child
Male Bias
Because the data are dyadic, there are two accuracy and bias effects.
We can estimate the above model using the Actor-Partner Interdependence Model (Kashy & Kenny).

11. What We Need To Do Restructure Data Examine Distinguishability
Estimate the APIM
We shall use DyadR.

12. What is DyadR? Package of programs written in R for dyadic analysis.
R is open source
free
DyadR is written as a “shiny” app and no knowledge of R is needed.
Provides not only the usual computer output, but also text, tables, suggestions, and figures.
Done in collaboration with others (Tom Ledermann at Utah State, Lara Stas at Ghent, and Rob Ackerman at Texas Dallas)

13. Task 1: Restructure Data
Ordinarily dyad data are entered by individual: one record person.
Analysis requires other formats:
Dyad
Pairwise

14. Individual Data Structure
Dyad Person X Y Z

15. Subscripts 1 and 2 refer to roles like man and woman.
Dyad Data Structure
Dyad X1 Y1 X2 Y2 Z
Subscripts 1 and 2 refer to roles like man and woman.

16. Pairwise Data Structure
Dyad Person X1 Y1 X2 Y2 Z
Subscripts 1 and 2 refer to respondent and partner.

17. Apps Individual to Dyad: ItoD Individual to Pairwise: ItoP
Individual to Pairwise: ItoP

22. ItoD: Text
“The dataset has been transformed from an individual to a dyad dataset called OutputData. The distinguishing variable is Gender_num, and it has two levels, Women (-1) and Men (1). There are 238 dyads and 476 individuals, 238 Women and 238 Men. There are missing data for one or more of the variables in the dataset. The listwise deleted dataset contains 145 dyads and 278 individuals. There are 4 variables, 1 between-dyad variable, 1 within-dyad variable, and 2 mixed variables. There is one variable that is a string or character variable which has been deleted from the dataset and it is Gender. The one between-dyad variable is Married, and the one within-dyad variable is Gender Numeric. The within-dyads variable, Gender Numeric, is a dichotomy and could be used as a distinguishing variable.”

23. ItoD: First Table
The descriptive statistics for the variables as individuals are below.
Sample sizes are given in the last column of the table for each variable.
Mean sd
Minimum
Maximum
Intraclass r N
Gender Numeric
0.000
1.001
-1.000
1.000
476
Married
0.244
0.971
Close to Child
0.487
-1.767
0.233
0.207
279
Partner Close to Child
-0.066
0.583
-2.767
0.133
278

24. ItoD: Second Table
The descriptive and inferential statistics for the mixed and
within-dyads variables as dyads are below. Sample sizes
are given in the last column in both tables. For table
below, the number of cases refers to the number with
complete data for both members. Degrees of freedom for
the test of mean difference are one less the sample size
and for the test of standard deviation difference and the
test of the correlation are two less the sample size.
Mean sd
Women
Men p r N
Close to Child
0.017
-0.028
.398
0.481
0.505
.567
.230
.007
134
Partner Close to Child
-0.135
0.015
.023
0.645
0.482
<.001
.129
.137
133

25. Task 2: Determine whether dyad members are distinguishable.
Each dyad consists of a man and woman.
Can we ignore gender? If so, dyad members are said to be “indistinguishable.”
By doing so, we obtain a simpler model with more power to test hypotheses.

31. Results from Dingy
Conclusion: “Because all of the models have poor fit (both in terms of the chi-square test and the RMSEA), and because the SABIC is lowest for the model of complete distinguishability (see Table 2), it seems reasonable to assume that dyad members are fully distinguishable.”
Table 3: Tests of Hypotheses of Different Types of Distinguishability
chi square df
p value
Means
8.138 2
.017
Correlations
8.535 4
.074
Variances
15.178
<.001

32. Task 3: Use the APIM to Estimate and Test T&B, Treating Dyad Members as Distinguishable
Pairwise Dataset
Perceptions of Partner’s Closeness to the Child is predicted by Actual Closeness and Own Closeness, an APIM.

38. Suggestions from APIM_MM
1. There is one outlier (standardized residual greater than 4.0 or less than 4.0) for P Close to Child. Examine the output to see what observation might be considered to be an outlier. 2. There is evidence of negative skew in the residuals of P Close to Child. (many other suggestions are possible)

39. T&B Results: Truth and Bias Results

40. Differences by Gender?
“The test that the two actor effects are statistically significantly different is not significant, Z = (p = .057).”
“The test that the two partner effects are statistically significantly different is not significant, Z = (p = .324).”
“The combined actor effect across both Women and Men is equal to and is statistically significant (p < .001) and the standardized effect equals (r = .448 and a medium effect size). The combined partner effect across both Women and Men is equal to and is statistically significant (p = .033) and the standardized effect equals (r = .143 and a small effect size).”

41. T&B: Directional Bias? Women: -0.213, p < .001
Overall: , p = .017
Gender Difference: p = .008
Moderation by Marital Status: 0.097, p = .027
Married:
Dating:

42. Future Work APIM Options Moderators Non-normal outcomes
Remove outliers
Mediation models
SEM estimation
Alternative dyadic models
Common fate & mutual influence
Alternative dyadic designs
SRM and One-with-Many designs
Longitudinal dyadic models

43. Thank You!
Powerpoint davidakenny.net/DyadR/DyadR.ppt Data davidakenny.net/DyadR/Acitelli_Individual.sav davidakenny.net/DyadR/Acitelli_Dyad.sav davidakenny.net/DyadR/Acitelli_Pairwise.sav Output