The Persian developmental sentence scoring as a clinical ...mjiri.iums.ac.ir/article-1-4036-en.pdf · of morphosyntax in children Nahid Jalilevand1, Mohammad Kamali*2, Yahya Modarresi3,

Original Articlehttp://mjiri.iums.ac.ir Medical Journal of the Islamic Republic of Iran (MJIRI)

Iran University of Medical Sciences

____________________________________________________________________________________________________________________1. PhD, Assistant Professor, Department of Speech and Language Pathology, School of Rehabilitation Sciences, Iran University of Medical Sci-ences, Tehran, Iran. [email protected]. (Corresponding author) PhD, Associate Professor, Department of Rehabilitation Management, School of Rehabilitation Sciences, Iran Uni-versity of Medical Sciences, Tehran, Iran. [email protected]. PhD, Professor, Department of Linguistics, Institute for Humanities and Cultural Studies, Tehran, Iran. [email protected]. PhD, Assistant Professor, Department of Speech Therapy, Faculty of Rehabilitation, Isfahan University of Medical Sciences, Isfahan, [email protected]

The Persian developmental sentence scoring as a clinical measureof morphosyntax in children

Nahid Jalilevand1, Mohammad Kamali*2, Yahya Modarresi3, Yalda Kazemi4

Received: 24 May 2016 Accepted: 23 July 2016 Published: 31 October 2016

AbstractBackground: Developmental Sentence Scoring (DSS) was developed as a numerical measurement and a clini-

cal method based on the morphosyntactic acquisition in the English language. The aim of this study was to de-velop a new numerical tool similar to DSS to assess the morphosyntactic abilities in Persian-speaking children.

Methods: In this cross-sectional and comparative study, the language samples of 115 typically developingPersian-speaking children aged 30 - 65 months were audio recorded during the free play and picture descriptionsessions. The Persian Developmental Sentence Score (PDSS) and the Mean Length of Utterance (MLU) werecalculated. Pearson correlation and one – way Analysis of variance (ANOVA) were used for data analysis.

Results: The correlation between PDSS and MLU in morphemes (convergent validity) was significant with acorrelation coefficient of 0.97 (p< 0.001). The value Cronbach's Alpha (= 0.79) in the grammatical categoriesand the split-half coefficient (0.86) indicated acceptable internal consistency reliability.

Conclusion: The PDSS could be used as a reliable numerical measurement to estimate the syntactic develop-ment in Persian-speaking children.

Keywords: Language Measurement, Validity, Reliability, Typically Developing Children, Persian Language.

Cite this article as: Jalilevand N, Kamali M, Modarresi Y, Kazemi Y. The Persian developmental sentence scoring as a clinical measureof morphosyntax in children. Med J Islam Repub Iran 2016 (31 October). Vol. 30:435.

IntroductionIn a natural setting, typically developing

children usually tend to speak about theirneeds, ideas and feelings, or ask their par-ents about their environment. Children’slanguage samples provide informationabout their language abilities, which can besystematically elicited in a natural conver-sation by a speech-language pathologist.Three numerical measures that have oftenbeen used for analyzing language samplesare Mean Length of Utterance (MLU) (1),Developmental Sentence Score (DSS) (2,3)and Index of Productive Syntax (IPSyn)(4).

MLU is a common and valid tool used byresearchers and clinicians to measure syn-tax (5). Rice et al. concluded that the MLU,

as an index of general language develop-ment, is valid and reliable (6). On the otherhand, some limitations of MLU were re-ported by some researchers. Miller andChapman reported that different normalchildren have different MLUs at the sameage (7). Klee and Fitzgerald believed thatalthough there is a high correlation betweenMLU and chronological age in typicallydeveloping children, MLU is a valid indexof development until approximately 3.0morphemes in Brown's stage II (8). Theysuggested that MLU is a gross indicator ofgrammatical development (8).

Lee presented a standard method knownas the DSS (3). She used this quantitativemethod to evaluate standard grammaticalfeatures, including eight grammatical cate-

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http://mjiri.iums.ac.ir/article-1-4036-en.html

The Persian developmental sentence scoring

2 Med J Islam Repub Iran 2016 (31 October). Vol. 30:435.http://mjiri.iums.ac.ir

gories: Indefinite pronouns, personal pro-nouns, main verbs, secondary verbs, nega-tives, conjunctions, interrogative reversals,and WH-questions in the spontaneousspeech of 200 typically developing childrenaged 2- to 6 years 11 months. DSS was cal-culated, using a sample of 50 complete sen-tences. To calculate the DSS, the sum ofthe scores from at least 50 complete sen-tences of speech sample was divided by 50(3). The validity and reliability of the DSSwere determined by Koenigsknecht (9). Hesuggested that a significant difference be-tween the DSS of different age groupsproved its validity, and concluded that thevalidity and reliability of DSS stronglysupport its usefulness as a measure of syn-tax development in children (9).

Various studies illustrated that DSS pro-vides valuable information for the clinicalsetting. Hux et al. found that DSS is themost common standard and analytic meth-od used by American speech-languagePathologists (10).

Toronto developed the DevelopmentalAssessment of Spanish Grammar (DASG),which is similar to the DSS. The DASGevaluates grammatical performance ofSpanish-speaking children in spontaneousspeech. The six grammatical categoriesemployed were as follows: Indefinite pro-nouns and noun modifiers, personal pro-nouns, primary verbs, secondary verbs,conjunctions and interrogative words. TheDASG average score was reported on 128Spanish-speaking children between the ag-es of 3 and 6 years 11 months. Toronto re-ported that the DASG can discriminate be-tween one-year age levels (11).

The Developmental Sentence Score forJapanese (DSSJ) is the newest method toevaluate the syntax based on DSS in a non-English language (12). Miyata et al. devel-oped DSSJ and reported the overall DSSJin 84 typically developing children in ageintervals of 2 years 8 months to 5 years 2months. They collected 100 sentences dur-ing child-adult conversation and free play.A statistical analysis revealed that DSSJand MLU were highly correlated. They in-

troduced DSSJ as a valuable tool to studylanguage acquisition (12).

Scarborough developed the Index of Pro-ductive Syntax (IPSyn) as a grammaticalmeasure (4). IPSyn is based upon the de-velopmental stage of 56 subcategories ofnoun phrase, verb phrase, questions, nega-tions and sentence structure. She studiedthe speech samples of 15 children at theages of 24, 30, 36, 42 and 48 months longi-tudinally, and gathered 75 speech samples.The comparison between the mean of theIndex of Productive Syntax and MLU ateach age demonstrated the reliability of theIndex of Productive Syntax (4).

Gathering spontaneous language samplesallows multiple analyses of speech and lan-guage aspects. To analyze language sam-ples, Persian speech-language pathologists(SLPs) usually use the MLU as a tool tomeasure grammatical development.Jalilevand et al. conducted a longitudinalstudy on two Persian-speaking children (agirl and a boy) aged 12–60 months. Thespontaneous speech of these two childrenwas recorded and analyzed. The MLU in-creased with the age of the children. Thevariation slope in the MLU was steeperaround 24–42 months (13). Kazemi et al.examined the MLU in 171 Persian-speaking children, aged 2.5–5.5, who livedin Esfahan. The results of this study indi-cated that the rate of variation was not highafter 3–3.5 years, possibly due to slow syn-tactic growth after this age (14).

The Persian SLPs cannot use the DSS toanalyze sentences in language samples, be-cause the grammatical structure and mor-phosyntactic items in the Persian languageare not similar to the English language. Forexample, Persian is a pro-drop languagewith canonical SOV word order (15), butEnglish is not. The aim of this study was todevelop a numerical measurement to ana-lyze Persian sentences based on the Devel-opmental Sentence Scoring model in Eng-lish (3), and to evaluate its psychometricproperties.

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N. Jalilevand, et al.

3Med J Islam Repub Iran 2016 (31 October). Vol. 30:435. http://mjiri.iums.ac.ir

MethodsThis descriptive-analytic cross-sectional

study was conducted in two steps. Thefirst step was constructing the Persian de-velopmental sentence scoring chart. Weneeded to collect the morphosyntactic itemsand their acquisition time in typically de-veloping Persian speaking children. Somestudies have examined the morphosyntacticacquisition in three Persian-speaking chil-dren, who had been investigated longitudi-nally (13,16-18). The acquisition of somegrammatical items were reported in theseliteratures (13,16-18), which were selectedand grouped in eight Persian grammaticalcategories: Pronouns, question words,prepositions and conjunctions, verb mor-phology, modal and compound verbs, andgrammatical morphemes, sentence typesand sentence structures based on the Per-sian grammar sources (15,19-21). Accord-ing to the time range acquisition, thegrammatical subcategories were groupedinto six levels based on the child’s age (orweighted from 1 to 6). Then the PDSSchart was created with nine columns andsix rows. The eight columns demonstrateeight grammatical categories, and the sixrows indicate subcategories, and one col-umn represents the sentence point. Eachcategory was divided to some grammaticalitems (subcategories). For example, thefirst row in the question word category col-umn has four items: /ku/ and /koĴa/(where), / či/ (what), /ki/ (who), each itemwas weighted 1.

Data Collection and Statistical AnalysisOne hundred fifteen monolingual Persian-

speaking children (55 girls and 60 boys),whose parents signed the parental consentform and replied to the Age & Stage Ques-tionnaire (ASQ), were selected from 18kindergartens managed under the supervi-sion of the Welfare Organization in Tehran(22). The children’s ASQ scores on com-munication, fine motor, gross motor, per-sonal, social and problem solving skillswere normal. Each domain was scored sep-arately, and the scores were compared to

the screening cut-off score of each domain(22). The children had no history of neuro-logical problems, seizures, brain damage,or any other disorder and no symptoms ofmovement delay. The children were divid-ed into six age groups of six month inter-vals.

This study was approved by the EthicsCommittee of Iran University of MedicalSciences. The participants were free towithdraw at any stage of the study.

Sampling and InstrumentsLanguage sampling was done via natural

conversations in free play and picture de-scription context (2,12,23-25). One exam-iner collected the language samples in allthe children in an appropriate (with mini-mum noise and enough light) room at eachkindergarten. A free play session was pro-vided for each child, and their conversa-tions were recorded to be used as thechild’s language sample. One of the toyswas a dollhouse with furniture and includeda bedroom set, a dining room set, a bath-room and a restroom. Four small dolls (10centimeters tall) were used as well. Twosets of dolls’ clothes, an orderly and com-plete set and a messy and incomplete setprovided more stimulant settings for thechild’s language elicitation. Other toys in-cluded kitchen utensils, balls, animals, carsand toy soldiers and guns. The descriptiontask included 30 colourful pictures (20×25cm), representing daily family activitieswith the mother, father and children athome, at the park, at the doctor's office, atthe birthday party and at the beach. Duringthe conversation stage, the children wereencouraged to describe the pictures. Lan-guage samples of 20–30-minute conversa-tions between children and the examinerwere recorded, using a digital voice record-er (Kingston-DVR-902) that was kept in acontainer to avoid distracting the child. Thesampling conversation started with 15minutes of free play, followed by a 15-minute of picture description.

The language samples of children, whowere reluctant to communicate, (30 to 35-

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month-old children with less than 50 intel-ligible utterances and of 36-month and old-er children with less than 100 intelligibleutterances) were excluded from the sampleset.

We randomly selected 15 children fromthe 115 participants to examine test–retestreliability, with an interval of 10 days totwo weeks (25). Eliciting speech sampleswas done again through free play conversa-tion and picture description.

Transcription, Coding Reliability andScoring

The language samples were orthograph-ically transcribed based on the transcriptionconventions (Persian Transcription Con-vention Protocol: PTCP), considering theutterance and morpheme segmentation cri-teria (26). Lexical morphemes, functionalmorphemes, inflectional morphemes andclitics (19,21) were segmentated based onthe Persian-adapted instruction of the Sys-tematic Analysis of Language Transcripts(SALT) software (26,27). The criteria toconsider an accepted utterance for analysisare as follows:

The full intelligible utterances were ac-cepted. The repeated utterances, one-wordutterances and utterances without a verbwere excluded. Each part of speech in thelanguage sample was divided into morethan an utterance with greater than twoseconds pause, or terminal intonation ris-ing. A maximum of 50 consecutive sen-tences from each setting of free play andpicture description sessions were tran-scribed to meet the criterion of 100 sen-tences. Each sentence in the language sam-ple was checked for the grammatical itemslisted in the PDSS table and coded based onthe coding rules of the SALT. Every sen-tence that was produced by all the sentencerules of an adult standard sentence wasscored with a sentence point.

The Traditional method of calculatingMLU is to divide the total number of mor-phemes or words by the total number ofutterances (1,5,24). The following Persianutterance is segmented to four morphemes

and two words:

Utterancebæstæni = o xord-i?Ice cream= OM eat.PAST-2SG.SUDid you eat ice cream?

Segmentationbæstæni= 1 morpheme, 1 word;= o = 1 morpheme;xord-i= 2 morphemes, 1 word.

In this study, the MLUs of the languagesamples were calculated by the SALT. ThePDSS was calculated from the total sen-tence scores of the language sample anddividing it by the total number of the sen-tences. To calculate the total sentencescores, we used the PDSS codes that weresorted by SALT.

To examine the inter-rater point-to-pointagreement reliability, the percent agree-ment was calculated. The first 10 minutesof 20% of the language samples were re-transcribed by a trained transcriber (28).The agreements and disagreements werecalculated for the utterances, morphemes,and coding.

Total agreement / total agreement + totaldisagreement × 100, was used as the formu-la for percentage agreement calculation(29). This calculation indicated 94%, 96%and 93% inter-rater agreement for utteranc-es, morphemes, and coding, respectively.The values demonstrate an acceptable levelof agreement (11).

Statistical AnalysisStatistical analysis was carried out, using

the SPSS Software (Version 17). The meanand standard deviations for age, the MLUin morpheme, the MLU in word and thePDSS were calculated for every participant.A Kolmogorov-Smirnov test at p>0.05 wasconducted, which indicated that the datawere normally distributed; and hence, par-ametric testing was conducted. A one-wayAnalysis of Variance (ANOVA) was per-formed to evaluate the effect of age groupsas an independent variable, and the PDSS

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as dependent variables. Post hoc compari-sons were done by the Tukey HSD test. Tostudy the correlation between the totalscore of the PDSS with the total score ofthe MLUs (Convergent Validity) and thetotal score of the PDSS and age (age dis-criminative validity), Pearson’s coefficientswere calculated. The internal consistency ofthe scale was calculated through theCronbach's alpha coefficient, which wascalculated for the PDSS total score.Cronbach's alpha reliability coefficientnormally ranges between 0 and 1. The test-retest reliability (Temporal reliability, inter-rater reliability) of the PDSS was evaluatedby the repeatability coefficient of relativeor Intraclass Correlation Coefficient (ICC)at p<0.05. The Standard Error of Measure-ment (SEM) was also calculated (SEM=SD. √ 1-rtest-retest).

ResultsThe mean and standard deviations of the

children’s ages are shown in Table 1. Themean and standard deviations of the PDSSand MLUs of the six age groups are shownin Table 2. Figure 1 displays the meanPDSS per age group. A significant positivecorrelation coefficient between age andMLUs and PDSS is shown in Table 2. Fig-ure 2 shows the relationship between MLUin morphemes and PDSS. The correlationcoefficient r between age and PDSS was

0.69 (p<0.001). The correlation coefficientr between age and MLU in morphemes andMLU in words was 0.68 (p<0.001) and0.67 (p<0.001), respectively. The correla-tion between the PDSS and the MLU inmorphemes was significant where the cor-relation coefficient r was 0.97 (p<0.001);and the correlation between the PDSS andthe MLU in words was significant wherethe correlation coefficient r was 0.95(p<0.001) as well. A one-way ANOVA wasconducted to compare the effect of agegroups as an independent variable andPDSS as a dependent variable. There was astatistically significant difference amongage group means (F (5,109)= 24.691,p<0.001). Post hoc comparisons, using theTukey HSD test indicated that the PDSSmean for the 30-35 months age group(mean= 9.2, SD= 1.2) were significantlydifferent from the other age groups (Ap-pendix A shows the significant results inTukey HSD test).

Cronbach's Alpha was 0.79 for thegrammatical categories, and Spearman-Brown Coefficient split-half (internal con-sistency) was 0.86. The correlation betweenthe grammatical categories (items) and thePDSS (item-total) was calculated to test thescale homogeneity. There was a significantcorrelation between the grammatical cate-gories and the PDSS (p<0.05), except forthe category of question words. To examine

Table 1. Age groups, Gender and the Age Means and Standard Deviations of the Participants (n=115)AgeTotal

ParticipantsGenderAge Groups

In month Mean (SD)BoysGirls32 (1.6)

39.4(1.6)44.6(1.6)51.4 (1.1)56.5(1.7)62.5 (1.7)

202018201918

101010101010

101081098

30-3536-4142-4748-5354-5960-65

47.4(10.4)1156055Total

Table 2. The Mean and Standard Deviation of MLUm, MLUw and PDSS in Six Age Groups in a Month (n=115)60-6553-5948-5242-4736-4130-35Age Groups8.891.12

8.941.41

7.890.9

7.640.79

7.061.03

5.91.1

MeanSD

MLUm

5.670.85

5.580.94

4.930.5

4.740.65

4.270.59

3.70.6

MeanSD

MLUw

13.071.27

13.211.76

11.951.1

11.611.03

10.831.3

9.21.2

MeanSD

PDSS

The Mean Length of Utterance in Morphemes (MLUm), Mean Length of Utterance in Words (MLUw), and Persian DevelopmentalSentence Scoring (PDSS)

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test–retest reliability, language sample withan interval of 10 days to 2 weeks was elic-ited from 15. The summary of item statis-tics are as follows: The mean (variance)and range were 12.06 (0.94) and 0.43, re-spectively. It was determined that the intra-class correlation coefficient (ICC) was 0.77with 95% confidence interval (0.46-0.91),and the SEM was 0.96.

DiscussionThe aim of this study was to develop

PDSS as a numerical measurement to esti-mate the syntactic development in Persian-speaking children and evaluate its psycho-metric features. The results revealed ac-ceptable psychometric properties. We com-pared the statistical features of this studywith those reported for DSS, DASG, andDSSJ. The strong correlation between

Fig. 1. Mean PDSS per Age Group (in 6 - Month intervals)

Fig. 2. The Relationship between PDSS and MLU in Morphemes

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MLU in morphemes and words with PDSSscore suggested good convergent validity.Koenigsknecht reported a strong correlation(r=0.87) between MLU and DSS of com-plete sentences (6). Similarly, Miyata et al.found a strong correlation between DSSJand the mean level of utterance in mor-phemes (r=0.94).

Cronbach’s alpha (α) is the most commonmeasure for assessing reliability and deter-mining internal consistency (35). The sig-nificant and high correlation between itemsand the significant correlation between thetwo sets of items (split-half) indicate ac-ceptable reliability. Koenigsknecht exam-ined the overall internal consistency ofDSS, using the coefficient α, (0.71), and thesplit-half coefficient (0.73), and demon-strated a positive correlation between indi-vidual grammatical categories and overallDSS scores, which supported its validity(6).

The significant correlation betweengrammatical subcategory scores and PDSS(item-total correlation) confirmed the inter-nal consistency reliability and constructvalidity of the PDSS. The relationship be-tween age and PDSS supports and confirmsthe age discriminative validity. A relation-ship also exists between age and DSS (6),DASG (14), and DSSJ (15).

Koenigsknecht concluded that significantdifferences that exist between the agegroups is an indicator of DSS validity (6).Torento believed that increasing DASGscores with age can indicate the instru-ment’s validity (14). Miyata et al. found astrong correlation between average DSSJscores and age (15), but reported that theoldest age group (5; 2) scored slightly low-er than the 4;8 age group (15). In this study,the coefficient of the correlation indicated astrong correlation with PDSS and age. Inaddition, a statistically significant differ-ence was detected in the means of differentage groups with average PDSS, but posthoc comparisons indicated no statisticallysignificant differences between the groupsaged 60–65 and 54–59 months. The aver-age PDSS scores of the group aged 54–59

months was slightly more than that of theoldest group. Therefore, we can concludethat PDSS could not reflect morphosyntaxdevelopment of Persian-speaking childrenin 6-month intervals, as Lee reported DSSin 1-year interval age groups (4).

A significant positive correlation coeffi-cient confirmed the relationship betweenage and grammatical subcategories, but allcoefficients were not equal. The verb mor-phology, grammatical morpheme, sentencestructure, and preposition and conjunctionscores had the highest correlation with age.The sentence structure scores were relatedto the complexity of sentences; therefore,increasing the sentence structure scores al-so increased the complexity of sentences. Asignificant positive correlation coefficientalso confirmed the relationship betweenPDSS and the verb morphology, grammati-cal morpheme, sentence structure, andpreposition and conjunction scores. Similarto Koenigsknecht’s report, our study foundsignificant overall differences in four of theeight DSS grammatical category scores:Indefinite pronouns and noun modifiers,personal pronouns, main verbs, and con-junctions (6). In addition, Miyata et al. re-ported that verb final inflection, verb mid-dle inflection, conjunctions, copula, case,and other particles contributed to changesin the DSSJ score (15).

We observed poor correlation coefficientscores between age and question words.However, there were no significant correla-tion coefficient scores between questionwords and PDSS. This result was similar tothat reported by Koenigsknecht (6) and To-ronto (14).

In this study, the ICC value was 0.77 andindicated excellent inter-rater reliability(36). Koenigsknecht used the repeated ap-plications of the DSS sampling procedureto examine temporal stability. He comparedfour measures during two weeks period andfound that the mean DSS score for 10 par-ticipants increased progressively from firstsampling to forth sampling (9). Torontoreported no differences in scoring betweenchildren tested by different examiners (11).

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The result showed that by increasingMLUs, PDSS score also increases. Thus,the number of morphemes and words couldincrease PDSS score, which was similar tothe MLUm and MLUw. The number ofmorphemes or words can add to the lengthof the utterance and increase the MLUmand MLUw, respectively. However, sen-tences with an equal number of morphemesmay have different developmental sentencescores. According to Klee et al., MLU maynot be a sensitive measure of any linguisticconstruct other than utterance length itself(8). Thus, the differences between sentenc-es with an equal number of morphemesshould be measured by PDSS because theweight or scores of morphemes or gram-matical subcategories are not equal in themethod of sentence scoring. Therefore,PDSS could determine the developmentalvalue of sentences with different grammati-cal subcategories. The subcategories withhigh scores in a sentence could increase itstotal PDSS score. Thus, we could comparePersian-speaking children not only in termsof the total PDSS score, but also in gram-matical subcategory scores. Consequently,the investigators and SLPs could analyzethe grammatical subcategories by employ-ing Persian-speaking children in a study,and by using PDSS as a clinical measure ofmorphosyntax.

An important limitation of this study wasthat we could not have parent–elicited con-versational samples for all participants inthe kindergartens. Thus, language samplewas elicited by an examiner.

ConclusionHere, PDSS was developed as a tool to

assess the morphosyntactic abilities of Per-sian-speaking children based on the devel-opmental hierarchy of the Persian morpho-syntactic rules, and its validity and reliabil-ity were examined. The findings confirmedthat PDSS may be used as a reliable numer-ical measurement to analyze the followingPersian grammatical categories: Verb mor-phology, modal and compound verbs,grammatical morphemes, pronouns, ques-

tion words, prepositions and conjunctions,sentence structure, and sentence type. Theinvestigators and Persian SLPs might beable to use this method as a clinical meas-ure to estimate the syntactic development inPersian-speaking children with languagedelay and compare them with typically de-veloping Persian-speaking peers before andafter speech therapy.

AcknowledgmentsThis investigation was supported by the

education grant number of 320-3246-93-9-9 granted by the Vice-Chancellor for Re-search at Iran University of Medical Sci-ences. The authors would like to thankAmir Hossain Mashreghi, Navid Samava-tian, Sahar Mafian, Tahmine Maleki,Fateme Babajani,Samane Babajani andMarziye Faraji for their contributions.

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Appendix A. The Significant Results in Tukey HSD TestAge Groups Age Groups Mean Difference Std. Error p Confidence Interval 95%

Lower Bound Upper Bound30-35 36-41 -1.61 0.41 0.003 -0.82 -0.39

42-47 -2.38 0.42 0.001 -3.63 -1.1448-53 -2.72 0.41 0.001 -3.93 -1.5154-59 -3.98 0.42 0.001 -5.21 -2.7660-65 -3.84 0.42 0.001 -5.08 -2.6

36-41 30-35 -1.61 0.41 0.003 -0.82 -0.3954-59 -2.37 0.42 0.001 -3.60 -1.1560-65 -2.23 0.42 0.001 -3.47 0.98

42-47 30-35 2.38 0.42 0.001 1.14 3.6354-59 -2.59 0.43 0.005 -2.85 - 0.3360-65 -1.45 0.44 0.016 -2.73 -0.17

48-53 30-35 2.38 0.42 0.001 1.14 3.6354-59 -2.59 0.43 0.005 -2.85 -0.33

54-59 30-35 3.98 0.42 0.001 2.76 5.2136-41 2.37 0.42 0.001 1.15 3.6042-47 1.59 0.43 0.005 0.33 2.8548-53 1.26 0.42 0.004 0. 35 2.48

60-65 30-35 3.84 0.42 0.001 2.60 5.0836-41 2.23 0.42 0.001 0.98 3.4742-47 1.45 0.44 0.016 0.17 2.73

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