University of Groningen
Everyday Life Attention Scale (ELAS)
Fuermaier, Anselm B. M.; Groen, Yvonne; Tucha, Lara; Weisbrod, Matthias; Aschenbrenner,
Steffen; Tucha, Oliver
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Applied Neuropsychology. Adult DOI:
10.1080/23279095.2019.1605994
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Fuermaier, A. B. M., Groen, Y., Tucha, L., Weisbrod, M., Aschenbrenner, S., & Tucha, O. (2021). Everyday Life Attention Scale (ELAS): Normative data of n = 1,874 Dutch participants. Applied Neuropsychology. Adult, 28(2), 140-147. https://doi.org/10.1080/23279095.2019.1605994
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Everyday Life Attention Scale (ELAS): Normative
data of n = 1,874 Dutch participants
Anselm B. M. Fuermaier , Yvonne Groen , Lara Tucha , Matthias Weisbrod ,
Steffen Aschenbrenner & Oliver Tucha
To cite this article: Anselm B. M. Fuermaier , Yvonne Groen , Lara Tucha , Matthias Weisbrod , Steffen Aschenbrenner & Oliver Tucha (2021) Everyday Life Attention Scale (ELAS): Normative data of n = 1,874 Dutch participants, Applied Neuropsychology: Adult, 28:2, 140-147, DOI: 10.1080/23279095.2019.1605994
To link to this article: https://doi.org/10.1080/23279095.2019.1605994
Published with license by Taylor & Francis Group, LLC © 2019 A. B. M. Fuermaier, Y. Groen, L. Tucha, M. Weisbrod, S. Aschenbrenner, and O. Tucha Published online: 10 May 2019.
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Everyday Life Attention Scale (ELAS): Normative data of n ¼ 1,874 Dutch
participants
Anselm B. M. Fuermaiera, Yvonne Groena , Lara Tuchaa, Matthias Weisbrodb,c, Steffen Aschenbrennerd, and Oliver Tuchaa
a
Clinical and Developmental Neuropsychology, University of Groningen, Groningen, The Netherlands;bPsychiatry and Psychotherapy, SRH Clinic Karlsbad-Langensteinbach, Karlsbad, Germany;cExperimental Psychopathology and Neurophysiology, Centre for
Psychosocial Medicine, University of Heidelberg, Heidelberg, Germany;dClinical Psychology and Neuropsychology, SRH Clinic Karlsbad-Langensteinbach, Karlsbad, Germany
ABSTRACT
The Everyday Life Attention Scale (ELAS) is a sensitive and reliable self-report questionnaire assessing attentional capacities of respondents in nine different situations of daily life. The ELAS has the potential to add relevant information to the clinical evaluation of attention deficits, to guide treatment planning, as well as to evaluate treatment outcome. The present study provides normative data of 1,874 Dutch speaking participants, ranging from 18 to 76 years of age, with mixed levels of education and a roughly equal distribution in gender. Normative data are calculated based on multiple linear regression models for each of the nine ELAS situations. In this article, the ELAS questionnaire as well as norm data are offered free of use. Use of normative ELAS data as presented in this study enables its use in clinical practice and research. Potential applications of the ELAS and future directions are discussed.
KEYWORDS
Attention; ADHD; cognitive complaints; cognition; neuropsychological assess-ment; self-report
Introduction
The Everyday Life Attention Scale (ELAS) is a recently developed self-report questionnaire asking for atten-tional capacities of the respondent in nine different sit-uations of daily life (Groen et al.,2019). The ELAS was developed to provide clinic and research with a reliable and sensitive tool for the assessment of attentional capacities by considering major limitations of existing attention questionnaires. For example, existing atten-tion quesatten-tionnaires often rely on a participant’s own judgement of difficulty or frequency of a particular attentional impairment, without providing a clear ref-erence point of what is regarded as “difficult” or “often” (Broadbent, Cooper, Fitzgerald, & Parkes,1982; Conners,1999; DuPaul et al.,1998: DuPaul et al.,2001; Ponsford & Kinsella, 1991; Suslow, Arolt, & Junghanns, 1998). As another limitation, most of the existing attention questionnaires ask about attentional difficulties in general, which fails to consider the con-text in which the difficulties are experienced. In clinical practice, patients find it often difficult to provide gen-eral answers because their experiences are often closely
linked to specific situations. There are some
questionnaires available that specify situations or task demands in which symptoms of inattention may be experienced; however, the scoring of these question-naires collapse the responses across situations to derive one general attentional ability score (Caterino, Gomez-Benito, Balluerka, Amador-Campos, & Stock, 2009; Schepers, 2007; Suslow, Arolt, & Junghanns,1998). The psychometric evaluation of the ELAS demonstrated that the most optimally fitting model of attention in every-day life was the situation-specific approach, meaning that everyday attentional capacities should be evaluated in the context of the tasks at hand. Furthermore, it must also be pointed out that for the assessment of certain clinical conditions, such as attention deficit hyperactivity disorder (ADHD), it is important to assess symptoms of inattention in various situations as pervasiveness of symptoms across settings is an important criterion for the diagnosis (American Psychiatric Association,2013).
The development of the ELAS took these limita-tions into consideration by sketching nine different situations that people commonly face in everyday life (e.g., reading or cooking). The ELAS contains ques-tions about several attentional capacities in each of CONTACTAnselm B. M. Fuermaier a.b.m.fuermaier@rug.nl Department of Clinical and Developmental Neuropsychology, Faculty of Behavioral and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, The Netherlands.
Published with license by Taylor & Francis Group, LLCß 2019 A. B. M. Fuermaier, Y. Groen, L. Tucha, M. Weisbrod, S. Aschenbrenner, and O. Tucha
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
APPLIED NEUROPSYCHOLOGY: ADULT 2021, VOL. 28, NO. 2, 140–147
these nine situations, which are rated on an 11-point Likert scale of how much focus or unbroken time can be spent on the task. By asking for absolute values of attention capacity (that are labelled), participants are not required to make a self-judgement about their level of impairment. Instead, impairment is deter-mined by the clinician who compares the respondent’s score to normative data.
In a previous study (Groen et al., 2019), the ELAS was shown to be a reliable measure for attention capacities with good internal consistency for each of the nine situations. Also the test–retest reliability after four weeks was good for the majority of situations. However, as also stressed in the original publication of the ELAS, normative data of a large number of healthy participants is necessary in order to increase clinical utility and facilitate its use. Automated scoring and norm data calculation would further support its dissemination among clinicians and researchers. This study presents and provides norm data of 1,874 Dutch participants who completed the ELAS. The ELAS as well as normative data are made freely avail-able in this article to everyone who is interested. The application of the ELAS and future directions will be discussed.
Methods Participants
Four hundred and fifty-seven participants were recruited in The Netherlands via contacts by the researchers (e.g., personal requests, e-mail, social media) or by word of mouth. Participation for those respondents was voluntary and unpaid. Furthermore, 2,029 participants were approached via a national online platform of Dutch panel members. This plat-form invites people to register as a panel member and take part in online research in exchange for a financial reward. Only participants with no reported psycho-logical or psychiatric disorders were considered for inclusion. One hundred fifty-five respondents were excluded from this sample because they failed one or both sets of validity items, which means they failed more than one of three mathematical calculations (n¼ 136) and/or endorsed more than one of three neg-lect symptoms (n¼ 45; see the Material section for more explanations about these validity items). Thus, 1,874 participants entered data analysis. Characteristics of participants are presented in Table 1. Participants were aged between 18 and 76 years, with a mean of 52.0 years (SD¼ 18.1). Roughly half of the participants (n¼ 988, 52.7%) were female. In order to facilitate
international application of the ELAS, we indicated level of completed education in three levels based on the International Standard Classification of Education (ISCED; de Vent et al., 2018; UNESCO, 1997), that is, low (primary education; n¼ 30), medium (lower and upper secondary, as well postsecondary education; n¼ 924), and high (tertiary education; n ¼ 920). Self-reported ADHD symptom scores based on DSM-crite-ria (ARS, see the MateDSM-crite-rial section for a description of this measure) ranged from 23 to 73 (34.80 ± 7.85), with inattention subscale scores ranging from 11 to 39 (16.32 ± 4.0), and hyperactivity/impulsivity subscale scores ranging from 12 to 38 (18.48 ± 4.62). All
partici-pants completed the questionnaire in the
Dutch language.
Materials
The Everyday Life Attention Scale (ELAS, see
Appendix) was administered to all participants. Questionnaire development, psychometric properties, scoring, as well as interpretation, is comprehensively described in the original article by Groen et al. (2019). The questionnaire consists of nine sketches of situa-tions that many people encounter in everyday life, that is, reading a book (Reading), watching a movie or documentary (Movie), performing an indoor activity (Activity), attending a lecture or open evening (Lecture), having a conversation (Conversation), doing an assignment/administration (Assignment), preparing a meal (Cooking), cleaning up the house (Cleaning up), and driving a car (Driving). Participants were asked to imagine an average week or day on which they come across the described situation, and to answer the questions about this situation. Particular situations could be omitted in individual cases if par-ticipants indicated to never encounter the respective
Table 1. Characteristics of participants (N¼ 1874).
Age group Age Gender Education
range (years) N M ± SD (years) % female Low Medium High
18–21 89 19.8 ± 1.0 79.8 3 22 64 22–25 99 23.4 ± 1.1 82.8 0 20 79 26–29 98 27.5 ± 1.1 58.2 0 40 58 30–33 96 31.5 ± 1.1 58.3 0 41 55 34–37 107 35.6 ± 1.1 48.6 1 43 63 38–41 101 39.4 ± 1.1 40.6 0 50 51 42–45 144 43.6 ± 1.1 47.2 5 76 63 46–49 198 47.6 ± 1.1 52.0 0 101 97 50–53 59 50.9 ± 1.1 52.5 0 33 26 54–57 22 55.8 ± 1.1 81.8 0 10 12 58–61 52 59.4 ± 1.2 53.8 3 29 20 62–65 69 63.5 ± 1.1 65.2 2 43 24 66–69 263 68.0 ± 0.9 54.4 2 160 101 70–73 331 71.4 ± 1.1 44.1 10 186 135 74–76 146 74.6 ± 0.6 32.2 4 70 72
situation. The participants indicated their level of attention on an 11-point Likert scale, ranging from 0 to 100% with steps of 10%. For questions addressing “sustained attention,” the participants were asked to indicate the duration of their unbroken attention in minutes (with a maximum of 120 minutes), which was recalculated to percentages of the maximum time. Scoring provided a mean scale score for each of the nine situations (or less if situations were not applic-able for individual participants).
It must be noted that self-report scales, including those measuring attention, are susceptible to noncred-ible responses and the importance of the assessment of symptom validity has been stressed consistently in original articles, consensus reports, and statement papers (Bush et al., 2005; Fuermaier et al., 2018; Heilbronner, Sweet, Morgan, Larrabee, & Millis, 2009; Tucha, Fuermaier, Koerts, Groen, & Thome, 2015). Therefore, to ensure validity of responses, six validity items were included to the questionnaire, representing one set of simple mathematical calculations (3 items, e.g., “10 þ 20 =?”) and another set of neglect symp-toms (3 items, e.g., “I bump into things that are located on the left hand”). Participants were excluded if they have more than one incorrect answer to a mathematical calculation, or if they endorse more than one neglect symptom.
Furthermore, in order to evaluate the usefulness of the ELAS for the assessment of ADHD in adulthood, the ADHD Rating Scale (ARS) for current ADHD symptoms (during the past 6 months) was included to assess the severity of self-reported ADHD symptoms according to DSM-criteria (Kooij et al., 2005, 2008). In this scale, the participant is requested to complete 23 items, each on a 4-point Likert scale (0¼ “rarely or never,” 1 ¼ “sometimes,” 2 ¼ “often,” 3 ¼ “very often”), representing behavioral symptoms of ADHD in adult-hood. Subscale sum scores for inattention and hyper-activity/impulsivity, as well as a total sum score were computed.
Procedure
All participants took part in the study online. Participants were requested to complete first a short questionnaire asking for personal characteristics, fol-lowed by the ELAS and the ARS, taking about 15–20 minutes in total. Only participants that com-pleted the ELAS were considered for data analysis. The questionnaire contained six additional validity items that were embedded in the survey. The study was approved by the Ethical Committee Psychology
(ECP) affiliated with the University of Groningen, the Netherlands. All participants provided active informed consent by clicking the option in the online form that they agreed with participation in this study.
Statistical analysis
Normative data were derived from multiple linear regression models with stepwise selection of predictor variables. The variables age, gender, and education were entered as predictors in each model. Level of education was dummy coded with medium level of education as the reference, resulting in two dummy variables representing lower education (LE) and higher education (HE). Criterion in each of the regression models was the individual mean scale score for the respective ELAS situation.
The assumptions of regression analysis, that is, homoscedasticity, normal distribution of the residuals, absence of multicollinearity, and absence of influential cases, were tested for each of the models depicting the ELAS situations (van der Elst, van Boxtel, van Breukelen, & Jolles, 2005). Homoscedasticity was visu-ally inspected by plotting the residuals against the pre-dicted values. Normal distribution of the residuals was evaluated by inspection of histograms and the normal probability plots. The occurrence of multicollinearity was checked by means of Variance Inflation Factors (VIFs). Possible influential cases were identified by calculating the Cook’s distances for each case and model.
After having determined multiple regression mod-els for each ELAS situation, normative data can be calculated by subtracting the predicted score from the observed score, and dividing this difference score by the standard deviation of the residuals (Z¼ (observed score – predicted score)/SDresiduals). The obtained
Z-score can be transformed into a T-Z-score or percentile rank for more straightforward clinical interpretation.
Based on percentile ranks for each participant and ELAS situation, the Number of Low Attention Situations (NLAS) was calculated by counting the number of situations in which a participant scored lower than the 10th percentile. The NLAS was shown to be an important variable indicating the pervasive-ness of impairments of attention across situations (Groen et al., 2019). Correlations between the NLAS and ARS scores were computed in order to determine the strength of association between the NLAS and self-reported ADHD symptom severity.
Results
Assumption check for regression analyses
A visual check for homoscedasticity revealed the ten-dency for a slight change in variance of residuals with higher predicted scores (i.e., heteroscedasticity). Therefore, predicted scores of models with continuous predictor variables were ordered and divided into three groups with equal range of scores. Standard deviations of residuals were calculated separately for each group of predicted scores. For models with cat-egorical predictors (e.g., gender), we indicated the standard deviation of the residuals for each category (e.g., for females and males).
Histograms and normal probability plots demon-strated a roughly normal distribution of residuals for each model. For some of the ELAS situations, a slight left skewness was observed, probably because of ceil-ing effects for high scores (i.e., scores close to 100%). This skewness has been accepted since it has been stressed that non-normality of the residuals has little to no consequences for regression-based norming, because the regression model is usually robust against a violation of the normality assumption for samples larger than 50 observations (Casson & Farmer, 2014) and also because norm statistics such as percentiles are often distribution-free (Oosterhuis,2017).
The occurrence of multicollinearity was checked by means of Variance Inflation Factors (VIFs), which should not exceed 10 (Belsley, Kuh, & Welsch, 1980;
van der Elst et al., 2005). The VIFs of the predictors in regression models with more than one predictor variable were all 1.043, which indicated that collin-earity between predictors did not seem to occur and affect the analyses.
Finally, no cases were considered to be influential and needed to be removed from the data set, as indi-cated by maximum values for Cook’s distance per model ranging from 0.0062 to 0.0714.
Multiple linear regression models
Significant regression models were found for eight of
the nine ELAS situations, including Reading,
F(2,1518)¼ 24.226, p< .001; Movie, F(2,1680) ¼ 16.573, p< .001; Lecture, F(1,1234)¼ 67.590, p< .001; Conversation, F(2,1542) ¼ 4.224, p ¼ .015; Assignment, F(2,1430)¼ 34.899, p < .001; Cooking, F(1,1492)¼ 30.146, p < .001; Cleaning up, F(2,1598) ¼ 8.776, p < .001; and Driving, F(1,1497) ¼ 19.228, p< .001, with the exception of Activity for which no model could be estimated. Regression coefficients for each model are presented in Table 2. Standard devia-tions of the residuals for each model are presented in
Table 3. Normative data can be calculated based on predicted scores (obtained from regression models), observed scores, and the standard deviation of the respective residuals (Z¼ (observed score – predicted score)/SDresiduals). For Activity, the mean scale score of
Table 2. Summary of multiple linear regression models for eight of the nine ELAS situations.
Model N Predictor B SE (B) Beta t p R2(%)
Reading 1,521 Constant 55.270 1.821 30.356 <.001 Age 0.195 0.030 0.168 6.543 <.001 Education (high) 3.782 1.072 0.091 3.527 <.001 3.1 Movie 1,683 Constant 62.661 1.733 36.162 <.001 Age 0.065 0.025 0.064 2.618 .009 Gender 4.176 0.893 0.114 4.679 <.001 1.9 Lecture 1,236 Constant 49.366 1.608 30.709 <.001 Age 0.245 0.030 0.228 8.221 <.001 5.2 Conversation 1,545 Constant 60.954 1.522 40.059 <.001 Age 0.057 0.028 0.051 2.017 .044 Education (low) 9.942 4.868 0.052 2.043 .041 0.5 Assignment 1,433 Constant 55.730 1.810 30.790 <.001 Age 0.200 0.027 0.196 7.450 <.001 Gender 2.119 0.968 0.058 2.190 .029 4.7 Cooking 1,494 Constant 74.432 1.197 62.207 <.001 Age 0.122 0.022 0.141 5.491 <.001 2.0 Cleaning up 1,601 Constant 66.844 1.650 40.502 <.001 Age 0.090 0.028 0.081 3.218 .001 Education (high) 2.112 0.998 0.053 2.117 .034 1.1 Driving 1,499 Constant 74.862 1.375 54.442 <.001 Gender 3.792 0.865 0.113 4.385 <.001 1.3
Note. ELAS¼ Everyday Life Attention Scale. Variables age, gender, and education were considered as predictor variables in each of the models and were stepwise selected for the regression models.
No regression model could be estimated for Activity. For this situation, predicted value is the mean scale score of the entire sample (M¼ 70.603). APPLIED NEUROPSYCHOLOGY: ADULT 143
the entire sample (¼ predicted score) and its standard deviation are considered for norm data calculation, because self-rated attentional capacities on this situ-ation appear to be independent from age, gender, and
education. An example of the norm data
calculation of a hypothetical client is presented in the Appendix.
Number of low attention situations (NLAS) and its association to ADHD symptoms
The frequencies, percentages, and cumulative percen-tages of the Number of Low Attention Situations (NLAS) are presented in Table 4. Results indicate
that the majority of participants have no impair-ment in any situation of the ELAS (63.9%), while 90.7% of participants have impairments in two or less of the situations. Correlation analyses revealed that the NLAS is significantly associated with ARS scores, that is, the ARS total score, r¼ 0.325, p< .001, the ARS inattention score, r ¼ 0.342, p< .001, and the ARS hyperactivity/impulsivity score, r¼ 0.256, p < .001.
Discussion
The ELAS has the potential to add relevant informa-tion to the clinical evaluainforma-tion of atteninforma-tion deficits, to guide treatment planning, as well as to evaluate treatment outcome. The norm data as provided in the present study may serve at least three purposes for research and clinic, that is, assessment, treatment selection, and treatment evaluation. Regarding assess-ment, norm data can complement the diagnostic process of ADHD, as the diagnostic criteria of ADHD require pervasiveness of the symptoms across settings (American Psychiatric Association, 2013). Indeed, data analysis of this study revealed that the Number of Low Attention Situations (NLAS) of the ELAS was significantly related to ADHD symptom severity in this healthy norm population, especially with the subscale score of inattention symptoms (medium effect). The sensitivity of the ELAS to reveal attention impairments have also been shown for other psychiatric disorders, including psychotic disorders, mood and anxiety disorders, personality disorders, and mental disorders due to psychoactive substances (Groen et al., 2019). Furthermore, norma-tive evaluation of attention capacities in several tasks of daily living may inform treatment choice, for example, the type and dose of pharmacological drug treatment or psychosocial interventions that specific-ally address the situations of daily life that are expe-rienced as most impaired by the individual. Finally, normative data of the ELAS can inform the clinician as a treatment evaluation tool about the level of functioning after a treatment program has been com-pleted (e.g., in the context of a re-assessment). This might be particularly interesting as psychometric tests measuring attention have been shown to have low test–retest reliability or are highly susceptible to practice effects (Fernandez-Marcos, de la Fuente, & Santacreu, 2018). Psychometric analysis performed within a previous study revealed good test-retest reli-ability of the ELAS (Groen et al., 2019). Also, the fine-grained 11-point Likert scale of the ELAS may
Table 3. Standard deviations of the residuals for each of the eight regression models for ELAS situations.
Model Predicted score/predictor variable SD (residuals)
Reading <63.806 21.587 63.806– 68.835 20.735 >68.835 19.557 Movie <70.645 18.929 70.645– 73.288 18.526 >73.288 16.904 Lecture <58.506 17.276 58.506– 63.239 20.371 >63.239 18.918 Conversation <70.765a 19.976 >70.765 16.831 Assignment <66.007 17.210 66.007– 70.572 18.873 >70.572 17.406 Cooking <78.988 15.271 78.988– 81.348 15.944 >81.348 14.827 Cleaning up <68.778 19.973 68.778– 71.212 19.720 >71.212 19.376 Driving Males 16.116 Females 17.356
Note. ELAS¼ Everyday Life Attention Scale. No regression model could be estimated for Activity. For this situation, the standard deviation of the mean scale score of the entire sample is considered (SD¼ 17.694).
a
The first (<66.371) and second (66.371 – 70.765) group of predicted scores were collapsed, because no case was found in the second group.
Table 4. Frequencies, percentages, and cumulative percen-tages of the Number of Low Attention Situations (n¼ 1,874).
NLAS Frequency Percentage Cumulative percentage
0 1,197 63.9 63.9 1 330 17.6 81.5 2 172 9.2 90.7 3 83 4.4 95.1 4 39 2.1 97.2 5 29 1.5 98.7 6 14 0.7 99.5 7 5 0.3 99.7 8 4 0.2 99.9 9 1 0.1 100.0
Note. NLAS¼ Number of Low Attention Situations. 144 A. B. M. FUERMAIER ET AL.
be more sensitive to detect subtle changes following treatment than other self-report scales for attention that have too narrow scales for catching subtle change (Kirshner & Guyatt, 1985). An Excel spread-sheet for easy and straightforward use of the ELAS is currently in preparation and will be freely access-ible to anyone by request from the authors. The Excel spreadsheet will perform automated scoring of scale scores and will provide T-scores and percentile ranks for a given assesse (see Appendix for an example of a hypothetical client). Further applied clinical research on the ELAS and its norms is needed in order to unfold its utility for the clinical neuropsychological assessment of attention capacities. Limitations
The present study has to be seen in the context of some limitations. First, while the majority of respond-ents (n¼ 1,874) completed and passed six items assessing the validity of their responses, a smaller pro-portion of participants (n¼ 457) did not complete any validity measures. Self-report scales, including those measuring attention, are susceptible to noncredible responses and the importance of the assessment of symptom validity has been stressed consistently in ori-ginal articles, consensus reports, and statement papers (Bush et al.,2005; Fuermaier et al., 2018; Heilbronner, Sweet, Morgan, Larrabee, & Millis, 2009; Tucha, Fuermaier, Koerts, Groen, & Thome, 2015). The absence of neurological and psychiatric disorders that may influence the self-evaluation of attention capaci-ties were determined based on self-report and were not assessed in an objective expert assessment. While it can be assumed difficult to perform objective expert assessments of large norm samples, it cannot be excluded that some of the participants of the present study did not report or were not aware of neuro-psychological conditions that may have influenced their experienced attention capacities.
Furthermore, dissemination and application of the ELAS would benefit from normative data from differ-ent cultures and in differdiffer-ent languages in order to facilitate its use in various countries. Explorative anal-yses of young adults (age 18–25 years) who completed the ELAS in German (n¼ 109), English (n ¼ 189), or Dutch (n¼ 204), suggested the presence language effects in at least a minority of the ELAS situations (data not shown). Nevertheless, more systematic research is required in order to address this issue adequately, including professional translations of the ELAS questionnaire into different languages and
acquisition of large data sets on people with native languages different than Dutch.
Conclusions
The ELAS is a reliable and sensitive tool for the assessment of attentional capacities in clinic and research. It has the potential to add relevant informa-tion to the clinical evaluainforma-tion of atteninforma-tion deficits, i.e. to guide treatment planning and to evaluate treatment outcome. Normative data of the ELAS as provided in this article are freely accessible to anyone and will facilitate its use in clinical practice and research.
ORCID
Yvonne Groen http://orcid.org/0000-0002-7242-0317
Oliver Tucha http://orcid.org/0000-0001-8427-5279
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Appendix. Norm data calculation on the basis of a hypothetical example. 1. Characteristics of the client
58-year old patient, male, higher education
Coding: Age¼ 58; Sex ¼ 2; HighEducation ¼ 1; LowEducation ¼ 0 Age: in years
Sex: 1¼ female, 2 ¼ male
HighEducation: high¼ 1, low/medium ¼ 0 LowEducation: low¼ 1, medium/high ¼ 0 2. Observed scores
Mean scale score (%) for each ELAS situation as described in material section (hypothetical example) Reading¼ 36.7; Movie ¼ 64.5; Activity ¼ 72.8; Lecture ¼ 43.4; Conversation ¼ 59.5;
Assignment¼ 67.8; Cooking ¼ 69.5; Cleaning up ¼ 56.1; Driving ¼ 47.4 3. Predicted scores
Applying regression equation (B values) as presented inTable 2for each ELAS situation
Readingpredicted¼ 55.270 þ Age0.195 þ HighEducation3.782 ¼ 55.270 þ 580.195 þ 13.782 ¼ 70.362
Moviepredicted¼ 62.661 þ Age0.065 þ Sex4.176 ¼ 62.661 þ 580.065 þ 24.176 ¼ 74.783
Activitypredicted¼ 70.603 (¼ mean scale score of the entire sample)
Lecturepredicted¼ 49.366 þ Age0.245 ¼ 49.366 þ 580.245 ¼ 63.576
Conversationpredicted¼ 60.954 þ Age0.057 þ LowEducation9.942 ¼ 60.954 þ 580.057 þ 09.942 ¼ 64.26
Assignmentpredicted¼ 55.730 þ Age0.200 þ Sex2.119 ¼ 55.730 þ 580.200 þ 22.119 ¼ 71.568
Cookingpredicted¼ 74.432 þ Age0.122 ¼ 74.432 þ 580.122 ¼ 81.508
Cleaning uppredicted¼ 66.844 þ Age0.090 þ HighEducation2.112 ¼ 66.844 þ 580.090 þ 12.112 ¼ 69.952
Drivingpredicted¼ 74.862 þ Sex3.792 ¼ 74.862 þ 23.792 ¼ 82.446
4. Z-scores
Z-score¼ (observed score – predicted score) / SDresiduals
SDresidualspresented inTable 3
ReadingZ-score¼ (36.7–70.362) / 19.557 ¼ 1.721 MovieZ-score¼ (64.5–74.783) / 16.904 ¼ 0.608 ActivityZ-score¼ (72.8–70.603) / 17.694 ¼ 0.124 LectureZ-score¼ (43.4–63.576) / 18.918 ¼ 1.067 ConversationZ-score¼ (59.5–64.26) / 19.976 ¼ 0.238 AssignmentZ-score¼ (67.8–71.568) / 17.406 ¼ 0.216 CookingZ-score¼ (69.5–81.508) / 14.827 ¼ 0.840 Cleaning upZ-score¼ (56.1–69.952) / 19.720 ¼ 0.702 DrivingZ-score¼ (47.4–82.446) / 16.116 ¼ 2.175
5. Transformation of Z-score into percentile rank Transformation tables freely available in many sources ReadingZ-score¼ 1.721 ! Readingpercentile¼ 4
MovieZ-score¼ 0.608 ! Moviepercentile¼ 27
ActivityZ-score¼ 0.124 ! Activitypercentile¼ 55
LectureZ-score¼ 1.067 ! Lecturepercentile¼ 14
ConversationZ-score¼ 0.238 ! Conversationpercentile¼ 41
AssignmentZ-score¼ 0.216 ! Assignmentpercentile¼ 41
CookingZ-score¼ 0.840 ! Cookingpercentile¼ 20
Cleaning upZ-score¼ 0.702 ! Cleaning uppercentile¼ 24
DrivingZ-score¼ 2.175 ! Drivingpercentile¼ 1
6. Number of Low Attention Situations (NLAS)
Calculate number of ELAS situations with percentile rank< 10 NLAS¼ 2 (reading and driving)
