1.   Examining the Sensitivity and Incremental Validity of an MMPI-2-RF Combined Response Inconsistency (CRIN) Scale for Detecting Mixed Responding
Good Afternoon! Today I’ll be discussing the sensitivity and incremental validity of a MMPI-2-RF CRIN scale for detecting mixed responding.
Nicole M. Lemaster - Ball State University
Kendall Whitney & Danielle Burchett - California State University, Monterey Bay
Tayla T.C. Lee – Ball State University

2. Mixed Responding A type of non-content based responding that includes:
Random Responding
Acquiescent Responding
Counter-Acquiescent Responding
Mixed responding has recently been investigated as a form of non-content based responding in which participants concurrently engage in random, acquiescent and counter-acquiescent responding. Previous research has suggested that a combined response inconsistency scales, or the CRIN scale, from the MMPI family of instruments may be useful in measuring mixed responding.

3. Combined Response Inconsistency Scale (CRIN)
CRIN was developed on the MMPI-A-RF (Archer, Handel, Ben- Porath, & Tellegen, 2016) to:
Augment the shortened VRIN-r and TRIN-r validity scales
Serves as a global measure of non-content based responding
Minimal research has been conducted for the use of CRIN on the MMPI-2-RF
A CRIN scale was originally developed for the MMPI-A-RF to augment the shortened VRIN-r and TRIN-r scales and serve as a global measure of non-content based responding.
But to date, minimal research has been conducted on the utility of a CRIN scale on the MMPI-2-RF for detecting mixed responding.

4. CRIN Components: VRIN-r
53 item pairs
A point is assigned when an examinee inconsistently answers a pair of items that were written in the same direction
Total score = number of pairs answered inconsistently
What allows CRIN to serve as a global measure of non-content based responding and a potential measure of mixed responding, is CRIN’s three major components: VRIN-r and the subcomponents of TRIN-r (TRIN-r – TRUE and TRIN-r – FALSE)
VRIN-r on the MMPI-2-RF consist of 53 item pairs that are written in the same direction and consist of similar content. Examinees obtain a points towards their raw score for each of the pairs that they respond to inconsistently.
For example, an item pair could consist of items like “I love New Orleans” and ”New Orleans brings me joy”. Examinees would receive a point on VRIN-r if these are answered inconsistently, so answering one of these true and the other one false. Then, VRIN-r's total score is the sum of item pairs answered inconsistently.

5. CRIN Components: TRIN-r
26 pairs
A point is assigned when an examinee inconsistently answers a pair of items that were written in the opposite direction
“Tug-o-War” scoring
TRIN-r
True
False 11
TRIN-r is the other major component of CRIN and on the MMPI-2-RF TRIN-r consist of 26 item pairs that are keyed in the opposite direction of one another. Just as for VRIN-r, examinees earn a point on TRIN-r when they inconsistently respond to an item pairs, but TRIN-r is unique in that examinees can earn points on one of two subscales, TRIN-r TRUE or TRIN-r FALSE.
For example an item pair on TRIN-r could be “New Orleans is a great place to be” and “New Orleans is a makes me sad”. Responding to both these items in the TRUE direction earn a point on TRIN-r TRUE and then if you answered both of items false you would earn a point on TRIN-r FALSE
Then to obtain the total scores of TRIN-r you subtract the raw score for TRIN-r - TRUE from TRIN-r – FALSE and then add 11. This method of scoring TRIN-r creates a tug-o-war dynamic which will come back to later.
**DO NOT SAY**
TRIN-r Breakdown: 15 pairs in the true direction and 11 pairs in the false direction.

6. CRIN Calculation on the MMPI-2-RF
VRIN-r
TRIN-r
True
False
CRIN
To calculate a CRIN score on the MMPI-2-RF, you sum the raw scores of VRIN-r, TRIN-r – TRUE, and TRIN-r – FALSE. This allows scores on CRIN to be a global measure of non-content based responding as it theoretically measures random responding through VRIN-r, acquiescent responding through TRIN-r – true , and counter-acquiescent responding through TRIN-r False.
*DO NOT SAY*
Also, notice that CRIN circumvents the tug-o-war that is inherent in TRIN-r scoring.
53 Pairs
15 Pairs
11 Pairs

7. Previous Research: Whitney et al. (2018a):
Calculated raw scores and Linear T Scores using the MMPI-2-RF normative sample
Examined the basic properties of CRIN scale scores on the MMPI-2-RF in a forensic inpatient sample
Found that scores on CRIN flagged an extra 3% of cases
Because scores on CRIN theoretically assess both random and fixed responding, previous research by Whitney and colleagues investigated the basic properties of a CRIN scale for the MMPI-2-RF. To do this, they first investigated the number of cases in the RF normative sample that were considered invalid based on a t scores greater than 80 on VRIN-r, TRIN-r, and CRIN.
One of their primary findings was that scores on CRIN were able to flag an additional 3% of cases beyond scores on VRIN-r and TRIN-r. Suggesting that CRIN scores add uniquely beyond scores on VRIN-r and TRIN-r in detecting invalid responding.

8. Previous Research
Given that CRIN scores uniquely detect cases, Whitney et al. (2018b) investigated CRIN scores’ ability to measure mixed responding
Used computer-generated mixed responding
Investigated 40% mixed responding
These findings prompted Whitney and Collegues to conduct a follow up study in which they investigate if CRIN scores were able to assess mixed responding and, if so, if scores on CRIN have utility in detecting mixed responding beyond scores on VRIN-r and TRIN-r.
To achieve this, they simulated mixed responding at a rate of 40% and examined the mean scores of VRIN-r, TRIN-r and CRIN. What they found was that CRIN scores elevated above a tscore of 80 across nearly all of the mixed responding conditions, suggesting that scores on CRIN may indeed assess mixed responding.
Since they had evidence that CRIN scores may assess mixed responding, they then investigated if scores on CRIN add to VRIN-r and TRIN-r scores in detecting invalid profiles as a result of mixed responding.

9. Findings for Whitney et al. (2018b)
The table presented displays the percentage of overall cases that were uniquely identified as invalid by each of the respective scales across all of their mixed responding conditions.
To orient you to the table, percentage are located on the vertical axis, while the 6 mixed responding conditions are located across the horizontal axis.
Broadly, their findings suggest that scores on VRIN-r do most of the work for detecting invalid profiles, while TRIN-r contributes very little, which is likely due to its tug-o-war scoring. Then we see that CRIN scores, represented in red, uniquely flags 11-22% of cases as invalid. Suggesting that CRIN may have incremental utility for detecting mixed responding on the MMPI-2-RF.

10. Current Study
Replicate and extend the work of Whitney et al. (2018b) by:
Investigate CRIN scores incremental validity to VRIN-r and TRIN-r scores in detecting mixed responding at a rate of %
Investigate how splitting scores on TRIN-r into TRIN-r True and TRIN-r false impacts the incremental validity of CRIN score in detecting mixed responding alongside VRIN-r, TRIN-r True, and TRIN-r False
While these findings are generally supportive of a CRIN scale on the MMPI-2-RF and CRIN scores ability to assess mixed responding, this research did have a couple of limitations that we aim to address.
First, they only investigated the ability of scores on CRIN to measure of mixed responding at a rate of 40%. And second, they did not examine how splitting TRIN-r into its subcomponents would impact CRIN's incremental utility for detecting mixed responding.
To counter these limitations, we aim to replicate and extend whitney and collegues work by examining CRIN scores utility for detecting simulated mixed responding at a rate of 40% as well as higher rates up to 100% responding in increments of 10.
Then we also examined how utilizing the subcomponents of TRIN-r (TRIN-r TRUE and TRIN-r FALSE) impacts the incremental validity of CRIN scores in detecting mixed responding.

11. Hypotheses
Similar to Whitney et al. (2018b), CRIN scores will demonstrate elevations in the presences of mixed responding.
Scores on CRIN will incrementally add to scores on VRIN-r and TRIN-r in detecting mixed responding across all of the rates of mixed responding.
CRIN scores’ incremental utility for detecting mixed responding beyond VRIN-r and TRIN-r scores will be negatively impacted by splitting TRIN-r into TRIN-r – True and TRIN-r – False
We had three hypotheses for the current study. First, we anticipated that we would replicate Whitney and Collegues findings by observing elevations on CRIN scores when mixed responding is simulated at 40%, then we expected to continue to see elevations as the rate of mixed responding increased.
Similarly, for our second hypothesis we anticipated that CRIN scores would uniquely add to the detection of mixed responding when simulated at a rate of 40% and that this pattern would continue as the rate of mixed responding increased.
And finally, we anticipated that the CRIN scores ability to add to the detection of mixed responding beyond what scores on VRIN-r and TRIN-r can do would be diminished when we circumvented TRIN-r tug-o-war scoring by utilizing TRIN-r's subcompondents (TRIN-r TRUE and TRIN-r FALSE.)

12. Method
Moving on to our methods.

13. Methods Participants:
Stringent exclusionary criteria were used to exclude all invalid protocols (Burchett et al., ):
CNS ≥ 15; VRIN-r ≥ 70; TRIN-r ≥ 70; F-r ≥ 79; Fp-r ≥ 70; Fs ≥ 80; FBS ≥ 80; RBS ≥ 80; L-r ≥ 65; K ≥ 60
Four samples of college students were combined to form this large sample:
All samples were collected at Midwestern universities between the years
Age: M = 19.30; SD = 2.44; Gender: 538 men, 1195 women
Ethnicity: 85.9% White; 8.3% African American, 2.6% Hispanic, 1.6% Asian, 2.6% another
3,298
College Students
Exclusion
Criteria
1,781
For the current study we used a large sample of 3,298 college students. This sample was obtained by compiling four smaller college student samples in which students completed the MMPI-2 or MMPI-2-RF between Strict exclusionary criteria was used to exclude protocols that were invalid or possibly invalid and that criteria is presented for you above. After making these exclusions, we had a final sample of 1,781 college students that we used for the present analyses – these participants demographic information is listed on the slide for you
*Pause*

14. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Split MMPI-2-RF into three sections
Section 1: Items 1-113; Section 2: ; Section 3:
Items 1-113
Items
Items F T
Using the final sample, we replicated Whitney and colleagues methodology for simulating mixed responding. This consisted of us using computer-generated responding to simulate mixed responding across our different response rates ( in increments of 10). To do demonstrate how we did this I’ll provide an example for the 40% response rate. First, we split the MMPI-2-RF items into three sections. Then, for each section we inserted either random, acquiescent, or counter-acquiescent responding. Then, we…

15. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Items 1-113
Items
Items F T
selected 40% of the items in the first section.
40% Chosen

16. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Generate random, acquiescent, or counter acquiescent responding
Items 1-113
Items
Items T F
40% Chosen

17. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Generate random, acquiescent, or counter acquiescent responding
Items 1-113
Items
Items T F
And inserted random T or F responses into those items.
40% Random
(True or False)

18. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Generate random, acquiescent, or counter acquiescent responding
Items 1-113
Items
Items T F
Then we selected 40% of the items in the second section.
40% Random
(True or False)
40% Chosen

19. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Generate random, acquiescent, or counter acquiescent responding
Items 1-113
Items
Items T F
40% Random
(True or False)
40% Chosen

20. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Generate random, acquiescent, or counter acquiescent responding
Items 1-113
Items
Items T F
And inserted all true responses into those selected items.
40% Random
(True or False)
40% Acquiescent
(All True)

21. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Generate random, acquiescent, or counter acquiescent responding
Items 1-113
Items
Items T F
Then we selected 40% of the items in the final section.
40% Random
(True or False)
40% Acquiescent
(All True)
40% Chosen

22. Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Generate random, acquiescent, or counter acquiescent responding
Items 1-113
Items
Items T F
40% Random
(True or False)
40% Acquiescent
(All True)
40% Chosen

23. 40% Counter-Acquiescent
Methods - Procedure
Replicated the methodology of Whitney et al. (2018)
Used computer-generated responses to simulate mixed responding at various rates
Selected designated percentage of items from each section (Ex. 40%)
Generate random, acquiescent, or counter acquiescent responding
Items 1-113
Items
Items T F
And inserted all false answer into those selected items.
40% Counter-Acquiescent
(All False)
40% Random
(True or False)
40% Acquiescent
(All True)

24. Methods - Procedure ACR ARC RCA RAC CAR CRA C = Counter-acquiescent
This entire process was complete 6 different times to account for six different variations of mixed responding
This was repeated across the 7 different variations in mixed responding rate (40-100%)
A = Acquiescent
C = Counter-acquiescent
R = Random
ACR
ARC
RCA
This process was completed 6 different times to account for the 6 different variations of mixed responding that occur using this definition. Throughout this presentation, we refer to these various conditions by these acronyms, which indicate the order in which we inserted the response styles.
This simulation process then repeated across each of the mixed responding rates which ranged from 40%-100% in increments of 10.
RAC
CAR
CRA

25. Results
For our results today, I’ll be showing a subset of our findings to provide you with an overview of the trends that we found. So I’ll be specifically be presenting our findings across four mixed responding rates, 40, 60, 80, and then 100% mixed responding.

26. Hypothesis # 1: CRIN scores will demonstrate elevation in the presence of mixed responding.
The first set of results I’ll be discussing were conducted to answer our first hypothesis, in which we replicated and extended the findings of Whitney and colleagues and hypothesized that CRIN scores would demonstrate elevation across all of our mixed responding rates.

27. To investigate this, we examined how the mean scores of VRIN-r, TRIN-r, and CRIN were impacted across these mixed responding rates, which is what is displayed in the present table.
To orient you all to these tables, we have the 6 mixed responding conditions located across the horizontal axis

28. and t scores are displayed on the vertical axis.

29. and t scores are displayed on the vertical axis.

30. Looking at the 40% mixed responding condition - we see that scores on CRIN were indeed elevated in the presence of mixed responding, VRIN-r scores were similar to CRIN scores, and then TRIN-r scores were stuck between the t score range. This trend is also present in the 60% mixed responding condition.

31. Then the 80% responding condition and also 100% conditions as well.

32. Hypothesis # 2: Scores on CRIN will incrementally add to scores on VRIN-r and TRIN-r in detecting mixed responding across all of the rates of mixed responding.
For our second hypothesis, we examined if scores on CRIN added incrementally to scores on VRIN-r and TRIN-r for detecting mixed responding

33. CASES NOT DETECTED
To do this, we examined the percentage of cases uniquely flagged as invalid by VRIN-r, TRIN-r, both VRIN-r and TRIN-r, and then CRIN based on a score greater than 80. These findings are displayed in the current table.

34. CASES NOT DETECTED
For this table, the percentages are along the vertical axis.

35. CASES NOT DETECTED
And, as before, the various mixed responding conditions are displayed along the horizontal axis.

36. CASES NOT DETECTED
Looking at the 40% mixed responding condition – we see similar patterns as what Whitney and colleagues observed.
First, we see that scores on VRIN-r do most of the work for detecting mixed responding across all of the mixed responding rates, while TRIN-r scores do very little.
Then, it appears that scores on CRIN add to scores on VRIN-r and TRIN-r for detecting mixed responding.

37. CASES NOT DETECTED
This pattern is consistent when 60% of mixed responding is simulated.

38. 24% 6%
CASES NOT DETECTED
As well as 80% mixed responding.

39. CASES NOT DETECTED
And 100% mixed responding too. Overall CRIN scores consistently adds to the detection of mixed responding and detects up to 26% of cases.
**DO NOT SAY***
Further, these trends also suggests that when VRIN-r scores are weaker at detecting mixed responding (conditions RAC and ARC), that scores on CRIN are able to make up the difference there.

40. Hypothesis # 3: CRIN scores’ incremental utility for detecting mixed responding beyond VRIN-r and TRIN-r scores will be negatively impacted by splitting TRIN-r into TRIN-r – True and TRIN-r – False
Finally, TRIN-r scores were observed to contribute very little in detecting mixed responding, likely because of TRIN-r’s tug-o-war, we investigated if CRIN scores’ incremental utility for detecting mixed responding beyond VRIN-r and TRIN-r scores and anticipated that CRIN's utility would be negatively impacted.

41. These graphs displayed are similar to some of the others I have walked through today. They display the mean t scores for the scales under examination across 40, 60, 80, and 100% mixed responding. These graph are actually exactly like the graphs I showed you for our first hypothesis, except this graph displays TRIN-r split into its subcomponents.
Looking at the orange and green lines displayed at 40%, we see that splitting TRIN-r into its subcomponents allowed for TRIN-r scores to have more range and no longer restricted to the t score range as before. Also, we see scores on TRIN-r TRUE adn FALSE raise in the presence of mixed responding, lending some support for our hypothesis that the tug-o-war scoring of TRIN-r did impact its utility for detecting mixed responding.
These patterns were observed at the 40% and 60% response rate

42. As well as, the 80 and 100% response rate.

43. Next we examined the incremental utility of CRIN scores for detecting mixed responding when TRIN-r was split into its subcomponents.
Once again, this table is very similar to previous tables that we have looked at and displays the percentages of cases that were flagged as being invalid cases by VRIN-r, TRIN-r TRUE, TRIN-r FALSE, CRIN, and a combination of VRIN and TRIN-r’s subcomponents.
For the 40% response rate, we see that VRIN-r scores continue to do most of the work, but that TRIN-r – TRUE and FALSE scores do demonstrate more assistance in detecting mixed responding than TRIN-r alone did. Though CRIN's contribution to detecting invalid cases was diminished by splitting TRIN-r, CRIN scores still continue to uniquely detect up to 10% of additional cases.

44. This pattern continues to emerge in the 60% mixed responding condition.

45. As well as 80% mixed responding.

46. As well as when all of the responses are simulated
As well as when all of the responses are simulated. However, its important to note that we are also dectecting less cases overall as invalid. I’ll discuss this more later.

47. Discussion
Moving on to our discussion of these findings.

48. Hypothesis 1: CRIN scores will demonstrate elevation in the presence of mixed responding
Successfully replicated and extended the findings observed by Whitney et al. (2018b)
VRIN-r and CRIN scores were elevated across all rates of mixed responding
However, TRIN-r scores remained between across all the rates of mixed responding
As a reminder, we first hypothesized that CRIN scores would demonstrate elevations in the presence of mixed responding. To this aim, we successfully replicated and extended the findings observed by whitney and collegues and observed that VRIN and CRIN scores elevated across all rates of mixed responding, while TRIN scores remained within the range. We attributed TRIN-r scores attenuation to the tug-o-war scoring of TRIN-r.

49. Hypothesis 2: Scores on CRIN will incrementally add to scores on VRIN-r and TRIN-r in detecting mixed responding across all of the rates of mixed responding.
Scores on CRIN add incrementally to VRIN-r and TRIN-r score for detecting mixed responding across all of the different rates of mixed responding.
CRIN scores flagged 0-27% unique cases across all conditions
Second, we hypothesized that CRIN scores would incrementally add to VRIN-r and TRIN-r scores in detecting mixed responding. In line with these hypotheses, CRIN score added incrementally to scores on VRIN-r and TRIN for detecting mixed responding across all of the mixed responding rates, specifically detecting 27% of cases. Further, it appears that CRIN scores are able to make up the difference in detecting mixed responding when VRIN-r scores are weaker at doing so.

50. VRIN-r, TRIN-r – True, TRIN-r - False, & CRIN
Hypothesis 3: Splitting TRIN-r into TRIN-r – True and TRIN-r – False will negatively impact CRIN scores ability to incremental add to scores on VRIN-r, TRIN-r – True, and TRIN-r – False in the detection of mixed responding.
VRIN-r, TRIN-r, & CRIN
VRIN-r, TRIN-r – True, TRIN-r - False, & CRIN
Mixed Responding Rate
% Detected by CRIN
40%
10 – 16%
6 – 10%
50%
6 – 21%
3 – 10%
60%
3 – 27%
2 – 11%
70%
1 – 27%
0 – 11%
80%
0 – 27%
1 – 10%
90%
1 – 26%
0 – 9%
100%
0 – 24%
0 – 7%
Allowed scores on TRIN-r – True and TRIN-r - False to elevate beyond the t-score range
Negatively impacted CRIN scores ability to incremental add to scores on VRIN-r, TRIN-r – True, and TRIN-r – False in the detection of mixed responding
 Finally, we investigated how splitting TRIN-r into its subcomponents would impact the incremental validity of CRIN scores for detecting mixed responding, with the expectation that it would negatively impact it.
It appears that the tug-o-war scoring of TRIN-r was attentuating TRIN-r scores and contribution to detecting mixed responding, because splitting TRIN-r into its subcomponent allowed for scores on TRIN-r TRUE and FALSE to elevate beyond the t score range that they were stuck at before. Then we also observed scores on TRIN-r subcomponents contribute more to the detection of mixed responding.
In line with out hypotheses, utilizing scores on TRIN-r subcomponents did negatively impact the incremental utility of CRIN scores for detecting mixed responding beyond scores on VRIN-r and TRIN-r’s subcomponents.

51. VRIN-r, TRIN-r – True, TRIN-r - False, & CRIN
Hypothesis 3: Splitting TRIN-r into TRIN-r – True and TRIN-r – False will negatively impact CRIN scores ability to incremental add to scores on VRIN-r, TRIN-r – True, and TRIN-r – False in the detection of mixed responding.
VRIN-r, TRIN-r, & CRIN
VRIN-r, TRIN-r – True, TRIN-r - False, & CRIN
Mixed Responding Rate
% Detected by CRIN
40%
10 – 16%
6 – 10%
50%
6 – 21%
3 – 10%
60%
3 – 27%
2 – 11%
70%
1 – 27%
0 – 11%
80%
0 – 27%
1 – 10%
90%
1 – 26%
0 – 9%
100%
0 – 24%
0 – 7%
Allowed scores on TRIN-r – True and TRIN-r - False to elevate beyond the t-score range
Negatively impacted CRIN scores ability to incremental add to scores on VRIN-r, TRIN-r – True, and TRIN-r – False in the detection of mixed responding
Specifically, the current table displays the decrease in cases detected as invalid by CRIN scores when we utilized TRIN-r subcomponents instead of the standard scoring on TRIN-r. Specifically, we see a 0-16% drop in cases detected by CRIN scores when we utilized TRIN-r subcomponents, but scores on CRIN still appear to have incremental utility for detecting mixed responding across all of the mixed responding rates.

52. VRIN-r, TRIN-r – True, TRIN-r - False, & CRIN
Hypothesis 3: Splitting TRIN-r into TRIN-r – True and TRIN-r – False will negatively impact CRIN scores ability to incremental add to scores on VRIN-r, TRIN-r – True, and TRIN-r – False in the detection of mixed responding.
VRIN-r, TRIN-r, & CRIN
VRIN-r, TRIN-r – True, TRIN-r - False, & CRIN
Mixed Responding Rate
% Not Detected
40%
39 – 80%
24 – 58%
50%
4 – 30%
10 – 42%
60%
4 – 31%
7 – 29%
70%
2 – 20%
6 – 21%
80%
0 – 12%
7 – 14%
90%
0 – 7%
9 – 28%
100%
0 – 2%
9 – 27%
However, by splitting TRIN-r into TRIN-r – True and TRIN-r – False, fewer invalid cases were detected than when simply using TRIN-r scores
However, utilizing TRIN-r subcomponents instead of the standard scoring of TRIN-r led to fewer cases being detected as invalid. The chart displayed is similar to the one before, but this time it illustrates the percent of cases not detected. As you can see, generally more cases went undetected when we used TRIN-r subcomponents compared to the standard scoring of TRIN-r.

53. VRIN-r, TRIN-r – True, TRIN-r - False, & CRIN
Hypothesis 3: Splitting TRIN-r into TRIN-r – True and TRIN-r – False will negatively impact CRIN scores ability to incremental add to scores on VRIN-r, TRIN-r – True, and TRIN-r – False in the detection of mixed responding.
VRIN-r, TRIN-r, & CRIN
VRIN-r, TRIN-r – True, TRIN-r - False, & CRIN
Mixed Responding Rate
% Not Detected
40%
39 – 80%
24 – 58%
50%
4 – 30%
10 – 42%
60%
4 – 31%
7 – 29%
70%
2 – 20%
6 – 21%
80%
0 – 12%
7 – 14%
90%
0 – 7%
9 – 28%
100%
0 – 2%
9 – 27%
However, by splitting TRIN-r into TRIN-r – True and TRIN-r – False, fewer invalid cases were detected than when simply using TRIN-r scores
Specifically, when 100% of responding was simulated 9-27% of cases were not flagged as invalid when TRIN-r subcomponents were utilized, but only 0-2% of cases went undetected when we utilized Vrin-r, TRIN-r, and CRIN.

54. Conclusions
In the presence of mixed responding, scores on CRIN appear to add incrementally to scores on VRIN-r and TRIN-r.
Though CRIN’s utility is limited when TRIN-r is split into TRIN-r – True and TRIN-r – False, splitting TRIN-r appears to limit the number of invalid cases detected.
Overall, adding a CRIN scale on the MMPI-2-RF and future instruments may be more useful than using TRIN-r - True and TRIN-r - False in the detection of mixed responding.
Overall, these results suggest that scores on CRIN appear to add incrementally to scores on VRIN-r and TRIN-r for detecting invalid cases due to mixed responding.
And though CRIN’s utility is limited when TRIN-r split into TRIN-r true and false, CRIN scores still add incrementally AND utilizing TRIN-r subcomponents leads to fewer cases being detected as invalid for mixed responding.
Thus, utilizing a CRIN scale on the MMPI-2-RF and future instrument may be more useful than working towards splitting TRIN-r into stand alone measures of acquiescent and counter-acquiescent responding.

55. Limitation and Future Directions
Limitations
Future Directions
Simulation design
Operationalization of mixed responding
Explore different method of operationalizing mixed responding
Investigate if/how mixed responding occurs naturally
The findings of the current study, however, are limited in there generalizability because we utilized a simulation design AND operationalized mixed responding in a farily artificial way. Future research should explore different operationalization of mixed responding and investigate if and how mixed responding occurs naturally.

56. Acknowledgments:
Contact information:
University of Minnesota Press & MMPI Lab at Kent State University
Nicole M. Lemaster
Ball State University
Department of Psychological Science
North Quadrangle Building
Muncie, Indiana 47306
Finally, I’d like to briefly thank and acknowledge the University of Minnesota Press for allowing us permission to use the normative sample to obtain t-scores for TRIN-r TRUE and TRIN-r FALSE and the MMPI lab at Kent State that facilitated these analyses. Thank you so much.