Test of Memory Malingering (TOMM): Normative data for the Latin American Spanish speaking adult population

2.1Participants

The sample consisted of 2,266 healthy individuals who were recruited from Argentina, Bolivia, Chile, Mexico, Paraguay, Peru, and Puerto Rico. The participants were selected according to the following criteria: a) were between 18 to 95 years of age, b) were born and currently lived in the country where the protocol was conducted, c) spoke Spanish as their native language, d) had completed at least one year of formal education, e) were able to read and write at the time of evaluation, f) scored≥23 on the Mini-Mental State Examination (MMSE, Folstein, Folstein, & McHugh, 1975; Ostrosky-Solís, López-Arango, & Ardila, 2000; Villaseñor-Cabrera, Guàrdia-Olmos, Jiménez-Maldonado, Rizo-Curiel, & Peró-Cebollero, 2010), g) scored≤4 on the Patient Health Questionnaire– 9 (PHQ-9, Kroenke, Spitzer, & Williams, 2001), and h) scored≥90 on the Barthel Index (Mahoney & Barthel, 1965).

Participants with self-reported neurologic or psychiatric disorders were excluded due to a potential effect on cognitive performance. Participants were volunteers from the community and signed an informed consent. Table 1 presents socio-demographic and participant characteristics for each country. The multi-center study was approved by the Ethics Committee of the coordinating site, the University of Deusto, Spain.

2.2Instrument administration

The TOMM begins with the presentation of stimuli notebook 1 (learning test), which contains 50 drawings. Following presentation of the stimuli, the participant is shown 50 pairs of pictures where one of the drawings appears for each panel previously presented and a new drawing. The task is to indicate what the picture was presented in the learning phase. Then, the participant is presented with notebook stimulus 2 (learning test 2), which also contains 50 drawings. The task and the process for learning in this second test is the same as in the previous (Tombaugh, Vilar-López, García, & Puente, 2011).

2.3Statistical analyses

The detailed statistical analyses used to generate the normative data for this test are described in Guàrdia-Olmos, et al., (2015). In summary, the data manipulation process for each country-specific dataset involved five steps: a) t – tests for independent samples and effect sizes (r) were conducted to determine gender effects. If the effect size was larger than 0.3, gender was included in the model with gender dummy coded and female as the reference group (male = 1 and female = 0). b) A multivariate regression model was used to specify the predictive model including gender (if effect size was larger than 0.3), age as a continuous variable, and education as a dummy coded variable with 1 if the participant had >12 years of education and 0 if the participant had 1– 12 years of education. If gender, age and/or education was not statistically significant in this multivariate model with an alpha of 0.05, the non-significant variables were removed, and the model was re-run. Then a final regression model was conducted that included age (if statistically significant in the multivariate model), dichotomized education (if statistically significant in the multivariate model), and/or gender (if effect size was greater than 0.3) [yˆi=β0+(βAge·Agei)+(βEduc·Educi)+(βGender·Genderi)]; c) residual scores were calculated based on this final model (ei=yi−yˆi);d) using the SD (residual) value provided by the regression model, residuals were standardized: z = e _i /SD _e , with SD _e (residual) = the standard deviation of the residuals in the normative sample; and e) standardized residuals were converted to percentile values (Strauss et al., 2006). Using each country’s dataset, these steps were applied to TOMM Trial 1 scores and TOMM Trial 2 scores. In certain countries, there was no effect of gender, nor age, nor education on the scores. In these cases, the normative data were generated directly using the raw scores.

3.1TOMM Trial 1

Regarding the effect of gender on TOMM Trial 1 scores, the t-tests did not show significant differences between men and women in all countries. Table 2 shows the results of the gender analyses by country on TOMM Trial 1 scores. As shown in Table 2, the effect sizes for all countries were less than 0.3, and therefore gender was not taken into account to generate TOMM Trial normative data for any of the countries in the study.

The final seven TOMM Trial 1 multivariate linear regression models for each country are shown in Table 3. In all countries except Bolivia and Chile, the TOMM Trial 1 score increased for those with more than 12 years of education (see Table 3) and, in all countries except Puerto Rico, TOMM Trial 1 scores decreased in a linear fashion as a function of age. The amount of variance explained in TOMM Trial 1 scores ranged from 4% (in Argentina) to 29% (in Paraguay).

3.2TOMM Trial 2

Regarding the effect of gender on TOMM Trial 2 scores, the t-tests did not show significant differences between men and women in all countries. Table 4 shows the results of the gender analyses by country on TOMM Trial 2 scores. As shown in Table 4, the effect sizes for all countries were less than 0.3, and therefore gender was not taken into account to generate TOMM Trial normative data for any of the countries in the study.

The final seven TOMM Trial 2 multivariate linear regression models for each country are shown in Table 5. In all countries except Bolivia, Chile, and Mexico the TOMM Trial 2 score increased for those with more than 12 years of education (see Table 5) and, in all countries except Puerto Rico, TOMM Trial 2 scores decreased in a linear fashion as a function of age. The amount of variance explained in TOMM Trial 2 scores ranged from 2% (in Mexico) to 33% (in Paraguay).

3.3Normative procedure

Norms (e.g., a percentile score) for the TOMM Trial 1 and Trial 2 scores were established using the five-step procedure described above. To facilitate the understanding of the procedure to obtain the percentile associated with a score on this test, an example will be given. Suppose you need to find the percentile score for an Argentine man, who is 50 years old and has 13 years of education. He has a score of 47 on the TOMM Trial 1. The steps to obtain the percentile for this score are: a) Check Table 2 to determine if the effect size of gender in the country of interest (Argentina) on this test and task (TOMM Trial 1) is greater than 0.3 by country. The column labelled r in Table 2 indicates the effect size. In this example, the effect size is 0.027, which is not greater than 0.3. For Argentines on this test, gender does not influence scores to a sufficient degree to take it into account gender when determining the percentile. b) Find Argentina in Table 3, which provides the final regression models by country for TOMM Trial 1. Use the B weights to create an equation that will allow you to obtain the predicted TOMM Trial 1 score. The corresponding B weights are multiplied by the actual age and dichotomized education scores and added to a constant in order to calculate the predicted value. In this case, the predicted TOMM Trial 1 score would be calculated using the equation [yˆi=48.164+(-0.016·Agei)+(0.763·DichotomizedEducationalLeveli)] (the values have been rounded for presentation in the formula). The subscript notation i indicates the person of interest. The person’s age is 50, but the education variable is not continuous in the model. Years of education is split into either 1 to 12 years (and assigned a 0) or more than 12 years (and assigned a 1) in the model. Since our hypothetical person in the example has 13 years of education, his educational level value is 1. Thus the predicted value is yˆi=48.164+(-0.016·50)+(0.763·1)=48.164-0.810+0.763=48.117. c) In order to calculate the residual value (indicated with an e in the equation), we subtract the actual value from the predicted value we just calculated (ei=yi-yˆ). In this case, it would be e _i  = 47 - 48.117 = -1.117. d) Next, consult the SD _e column in Table 3 to obtain the country-specific SD _e (residual) value. For Argentina, it is 2.461. Using this value, we can transform the residual value to a standardized z score using the equation (e _i /SD _e . In this case, we have (-1.117)/2.461 = -0.454. This is the standardized z score for an Argentine man aged 50 with 13 years of education and a score of 47 on the TOMM Trial 1. e) The last step is to look up the tables in the statistical reference books (e.g. Strauss et al., 2006) or use a trusted online calculator like the one available at http://www.measuringu.com/pcalcz.php. In the online calculator, you would enter the z score and choose a one-sided test and note the percent of area after hitting the submit button. In this case, the probability of 0.454 corresponds to the 32th percentile. Please remember to use the appropriate tables that correspond to each test (Trial 1vs. Trial 2) when performing these calculations. If the percentile for the TOMM Trial 2 score is desired, Tables 4 and 5 must be used.

3.4User-friendly normative data tables

The five-step normative procedures explained above can provide more individualized norms. However, this method can be prone to human error due to the number of required computations. To enhance user-friendliness, the authors have completed these steps for a range of raw scores based on small age range groupings (see Guàrdia-Olmos, et al., 2015) and created tables that clinicians can more easily use to obtain a percentile range associated with a given raw score on this test. These tables are available by country and type of test (TOMM Trial 1vs. TOMM Trial 2) in the Appendix. In order to obtain an approximate percentile for the above example (converting a raw score of 47 for an Argentine man who is 50 years old and has 13 years of education) using the simplified normative tables provided, the following steps are recommended. (1) First, identify the appropriate table ensuring the specific country and test. In this case, the table for TOMM Trial 1 scores for Argentina can be found in Table A1. (2) Note if the title of the table indicates that it is only to be used for one specific gender. In this case, gender is not specified. Thus Table A1 is used for both males and females. (3) Next, the table is divided based on educational level (1 to 12 vs. more than 12 years of education). Since this man has 13 years of education, he falls into the “>12 years of education” category. These data can be found in the top section of the table. (4) Determine the age range most appropriate for the individual. In this case, 50 falls into the column 48– 52 years of age. (5) Read down the age range column to find the approximate location of the raw score the person obtained on the test. Reading down the 48– 52 column, the score of 47 obtained by this Argentine man corresponds to an approximatepercentile of 30.

The percentile obtained via this user-friendly table method (30th) is slightly different than the more exact one (32th) obtained following the individual conversion steps above because the table method is based on an age range (e.g., individuals aged 48– 52) instead of the exact age (individuals aged 50). If the exact score is not listed in the column, you must estimate the percentile value from the listed raw scores.

4.1Limitations and future directions

The current study has several limitations and directions for future research. First, the TOMM is a fairly common measure of memory malingering in Latin America, but several other common measures should be normed using the same procedures to improve their accuracy in Latin America. Also, future studies should examine the ecological validity and psychometric properties of the TOMM and other malingering instruments in Latin America. Researchers need to create malingering instruments within Latin American cultures with high ecological validity, as the TOMM was developed and validated initially in a Western culture which may differ in important ways from those cultures in Latin America. Developing malingering assessments in the context of local cultures, instead of simply translating and norming them from other cultures and global regions, would be an important advance in the assessment of malingering throughout the region.

Second, neuropsychologists need to exercise caution when using the TOMM norms from the current study in assessing malingering with the TOMM in countries other than those in this study. Future studies should develop TOMM norms in Latin American countries not included in this study, such as Ecuador, Uruguay, Venezuela, and Panama. Despite this, the TOMM norms from the current study may represent a more accurate assessment of malingering in other Latin American countries than some of the norms currently in use, but this generalizability is a key area for future research.

Third, participants in this study spoke Spanish as their primary language, but they may have spoken secondary languages also. TOMM scores may differ according to bilingualism, which was not assessed in the current study. Future research in Latin America should include this as a potential research question. Participants were recruited from specific regions and cities in each country, not nationally within the countries. Although the current study was the largest TOMM normative project yet conducted in Latin America, or in any global region, it should be seen as a first step toward larger, nationally representative studies. The current sample was also limited in that although many participants had fewer than 12 years of education, participants who were illiterate were excluded. Therefore, the current TOMM norms may not apply well to adults unable to read, so future studies should collect data on illiterate individuals, as well as those with neurological conditions and among children.

Despite these limitations, only small-scale and limited studies have produced TOMM norms in Spanish-speaking populations such as Spanish-speakers from Spain or the U.S. (Ramírez et al., 2004; Strutt et al., 2012; Vilar-Lopez et al., 2008). Therefore, the current study was the first systematic and large-scale study to create TOMM norms across seven countries in Latin America, with over 2,000 participants. It was one of the most comprehensive TOMM normative studies to date in any global region, and its norms have the potential to improve the assessment of malingering with the TOMM in Latin America unlike any study before it.