Case 62: The Importance of Lambda to the Generalizability of Rorschach Findings Reported in the Literature

The Importance of Lambda to the Generalizability of Rorschach Findings Reported in the Literature

Carl Gacono

        In several articles, my colleagues and I have addressed concerns regarding methodological issues with the recent RIM research (see Cunliffe et al., 2012; Gacono, Loving, &Bodholdt, 2001; Smith et. al., 2018). In many studies, the impact of these issues is frequently hidden, masked in a description of statistical procedures and shrouded by the umbrella of a meta-analysis. Findings from these flawed studies influence conclusions that may appear “controversial” but are in actuality an artifact of the individual studies’ inadequate design. Alarmingly, such method-related practices negatively impact the scholarly perception of the Rorschach and are frequently cited by editors for rejecting submitted Rorschach studies for publication, based largely on the presence of “controversy” which does not exist. In this brief commentary, the importance of Lambda to the generalizability of Rorschach findings is discussed.

Piotrowski’s (2017) recent content analysis of the Rorschach (RIM) literature concluded that the field remains mired in “controversy” which address the scope of approaches regarding the validity of a myriad of scoring indices. But do these seemingly discrepant findings really highlight controversies or are these an artifact of something else?

        Those trained in the Comprehensive System (CS), prior to the advent of computer scoring, were able to develop a unique and invaluable perspective on the RIM, how it works, and what it means. Numerous hand-scoring of indices, ratios, and constellations resulted in an understanding of the RIM variables, their relationship to other parts of the RIM, and the nature of the dynamics regarding the functionality of the test; much like insights that are only gained by extensive clinical usage of the Rorschach and providing client feedback1.

For whatever reason, these essential “insights” are much less apparent, or even absent, in studies in recent Rorschach research. A perusal of the Rorschach literature  clearly  indicates  that  statistical 

1 Client’s responses to RIM feedback create a reciprocal interaction that offers “clinical validation” of the meaning of Rorschach variables and indices (client real world behavior confirms the Rorschach data). Feedback about individual criterion (e.g., a constellation such as DEPI) is useful as a starting point for a clinician-client interaction.

Analyses have been relegated to a predominate role at the expense of well conceptualized research designs which demonstrate an understanding of how the RIM actually works (Cunliffe, et al, 2012; Smith et al., 2018).

        This body of poorly designed research frequently results in contradictory and/or counterintuitive findings that are best understood by their methodological errors rather than true findings. Accepted as valid results in the published literature, these findings promote an apparent “controversy” where none actually exists (Gacono, Evans, &Viglione, 2008; Gacono, Loving, &Bodholdt, 2001). Much Rorschach research has become the tail (statistics) wagging the dog (theory and conceptualization). Unfortunately, this has resulted in what Bob Hare refers to as an “armchair quality” surrounding much of recent Rorschach scholarship.

This lack of thoughtful design is most evident in validation studies that do not account for Lambda. Lambda is a straightforward computation, L = F\R-F. By definition, the more pure F in the record relative to R, the higher the Lambda. Therefore, there are fewer other determinants in a record dominated by Pure F.

High Lambda, or constricted protocols, most frequently lack a normative distribution of other determinants and variables (evident in

Carl B. Gacono, PhD, ABAP, Private Practice, Email: drcarl14@aol.com.

Key Words: Rorschach, Lambda, clinical practice

Importance of Lambda: 105

the computation of Lambda). Consequently, validation studies that attempt to assess the relationships among variables and indices, where elevated Lambdas exist, are mostly not appropriate. The assumption that one can utilize a high Lambda sample for validation (unless the constriction is part of the hypotheses) is much akin to studying “psychopathy” in college samples where there are no psychopaths2.

        Dr. Irv Weiner’s oft spoken words; however, remain always true, “A variable means the same regardless of where and when it is produced.” Dr. Weiner’s statement; however, applies to the “interpretation” of the meaning of variables in group data or an individual protocol. Along these lines, in samples with high Lambda what must first be explained, interpretively, is the reason for the constriction (Gacono & Gacono, 2008). High Lambda may not invalidate the study, or an interpretation of the Rorschachs’ portrayal of the populations’ personality functioning. It does become problematic in validation studies or when attempting to use the results in comparison to other samples (generalizability).

        This lack of understanding of the way the Rorschach works, related to Lambda, R, and other variables, is most evident in meta- analyses that fail to consider these issues when assessing the appropriateness or the validity of the studies in the data-pool (Cunliffe et al., 2012; Smith et al., 2018). Other factors, such as IQ, may also impact Rorschach production. While factors that impact Rorschach production do not necessarily need to be controlled (e.g., in descriptive studies where the constriction is an accurate reflection of the personality functioning being examined), they must always be considered and reported in validation studies, where 2 At times quantity of R or elevation of Lambda may fit into hypotheses related to the personality of the sample studied (e.g., Gacono, Meloy, & Bridges, [2000] predicted increased R in certain samples of sex offenders). This allowance for a normally distributed range of R is one of the many strong points of CS administration which also allows for a useful sequence analysis, results will be compared to other studies with normative distributions.

        Often what I have called counterintuitive findings (those that occur counter to theory and other well-designed studies) are solely an artifact of methodological errors as described elsewhere (Gacono & Gacono, 2006; Gacono, Loving, Bodholdt, 2001). At a minimum, all research considered for publication should require authors to list the mean Lambda, its SD and ranges, as well as the presence or absence (appropriate descriptive data) of other factors such as IQ and the presence of brain damage, which may impact Rorschach production. This allows others to evaluate these issues and form their own opinions as to the weight of the study’s findings. Both scientifically and ethically, there should be nothing to hide in conducting research of this kind.

        In a similar vein, all manuscripts considered for publication should be required to list the mean, SD, and frequency for all variables studied. This allows for comparison of the sample investigated with other studies. It also allows for a determination of whether the author(s) utilized the appropriate statistical analyses. These descriptive data provide a measure of the relative distribution of the variables/indices under investigation. Exner (1995) frequently commented on the problems with abnormal distributions for many Rorschach variables and referred to these distributions as J-shaped curves. Parametric procedures, which are used frequently, are inappropriate for analyzing these variables. Yet, studies I have reviewed continue to ignore this concern.

Reporting descriptive data of the type discussed here will go a long way to elucidating many seemingly controversial findings. As researchers/practitioners, scholars should heed John Exner’s (1995) notion that not everything “appearing in the literature was truth” (p. 4). Too often the findings of studies are accepted without questioning basic issues related to their design and how the RIM operates in practice.

        This may, in part, be due to a generation of researchers with little or no experience hand- scoring the RIM (Structural Summary) and/or inadequate experience using the RIM in clinical settings. These two practices, hand- scoring and clinical usage, provide basic building blocks for understanding how the instrument works. Mastery of the Rorschach cannot be learned or understood solely from reading a book or books and/or administering many Rorschachs.

        Psychologists are vulnerable to accepting findings in which the statistical analyses may overwhelm, impress, or be difficult to comprehend. For many who practice clinically, statistics was never their favorite course in Graduate School. For many, i.e., journal editors and reviewers, their knowledge of the Rorschach lacks the necessary sophistication to be aware of these critical issues—yet, they continue to pass judgement on manuscripts submitted for publication. Conclusions of flawed meta-analyses are utilized for guiding decisions. This results in a cycle of rejection for psychometrically credible Rorschach research.

        In Rorschach terms, one strategy for dealing with confusing findings of Rorschach research is to narrow the stimulus field; to focus on the Ds and Dds rather than getting lost in the Ws created by statistics. These details entail examining carefully the author(s) stated methodology and determining whether the above issues have been addressed. Researchers need to review, also, some of the individual studies included in the meta- analysis before accepting the conclusions. The astute research consumer must be Ma instead of Mp and should resist allowing the flaws in the methodology to be coated by statistical gloss. As scientists it is imperative to be knowledgeable about statistical procedures, but not to become so enamored with statistical prowess (blindly accept meta- analytic findings) that one forgets to scrutinize the individual studies analyzed (see Smith et al., 2018).

References:

Cunliffe, T.B., Gacono, C.B., Meloy, J.R., Smith, J.M., Taylor, E.E., & Landry, D. (2012). Psychopathy and the Rorschach: A response to Wood et al. (2010). Archives of Assessment Psychology, 2(1), 1-31.

Exner, J. E. (Ed.). (1995). Issues and methods in Rorschach research. Mahwah, NJ: Erlbaum.

Gacono, C.B., Evans, F.B., & Kaser-Boyd, N., & Gacono,

L.A. (Eds.). (2008). The handbook of forensic Rorschach assessment. New York, NY: Lawrence Erlbaum Associates.

Gacono, C.B., Evans, F.B., & Viglione, D.J. (2008). Essential issues in the forensic use of the Rorschach. In C.B. Gacono, F.B. Evans, N. Kaser-Boyd, & L.A. Gacono (Eds.), The handbook of forensic Rorschach assessment (pp. 3-20). New York, NY: Lawrence Erlbaum Associates.

Gacono, C.B., & Gacono, L.A. (2006). Some caveats for evaluating the research on psychopathy. The Correctional Psychologist, 38(2), 7-9.

Gacono, C.B., Loving, J.L., & Bodholdt, R.H. (2001). The Rorschach and psychopathy: Toward a more accurate understanding of the research findings. Journal of Personality Assessment, 77(1), 16-38.

Gacono, C.B., Meloy, J.R., & Bridges, M.R. (2000). A Rorschach comparison of psychopaths, sexual homicide perpetrators, and nonviolent pedophiles: Where angels fear to tread. Journal of Clinical Psychology, 56, 757-777.

Gacono, L.A., & Gacono, C.B. (2008). Some considerations for the Rorschach assessment of forensic psychiatric outpatients. In C.B. Gacono,

F.B. Evans, N. Kaser-Boyd, & L.A. Gacono (Eds.), The handbook of forensic Rorschach assessment (pp. 421-444). New York: Lawrence Erlbaum Associates.

Piotrowski, C. (2017). Rorschach research through the lens of bibliometric analysis: Mapping investigatory domain.

Journal of Projective Psychology & Mental Health, 24(1), 34-38.

Smith, J.M., Gacono, C.B., Fontan, P., Taylor, E.E., Cunliffe, T.B., &Andronikof, A. (2018). A scientific critique of Rorschach research: Revisiting Exner's (1995) Issues and Methods in Rorschach Research. Rorschachiana, 39(2), 180-203.