Construct validity of modified time-interval analysis in measuring stuttering and trained speaking patterns

Journal of Fluency Disorders 37 (2012) 42–53

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Journal of Fluency Disorders

Construct validity of modified time-interval analysis in measuring stuttering and trained speaking patterns Anke Alpermann a,b,∗ , Walter Huber a , Ulrich Natke c , Klaus Willmes a a b c

RWTH Aachen University, Germany Zuyd University, The Netherlands Neuss, Germany

a r t i c l e

i n f o

Article history: Received 16 March 2011 Received in revised form 14 November 2011 Accepted 14 November 2011 Available online 23 November 2011 Keywords: Stuttering Construct validity Modified time-interval analysis

a b s t r a c t Purpose: The aim of the present study was to examine the construct validity of modified time-interval analysis. This measure allows judgments on stuttered and fluent speech as well as trained speaking patterns and might be valuable for outcome studies. Method: Construct validity was investigated in an intervention study with two treatment groups (24 clients received stuttering modification treatment, 30 clients received fluency modification treatment) and a control group (38 stuttering adults). All participants were interviewed during surprise phone calls before and after treatment; the speech samples were analyzed by means of modified time-interval analysis and stuttering frequency counts. Results: The outcomes confirmed prior hypotheses for the most part. First, the amount of trained speaking patterns after therapy was significantly higher in both treatment groups than in the control group. Secondly, longitudinal changes in the treatment groups met prior expectations based on differing treatment goals and exceeded the changes in the control group. Modified time-interval analysis was sufficiently sensitive to detect changes of speech fluency, but underestimated spontaneous fluent speech when trained speaking patterns were applied. Conclusion: The present study supports construct validity of modified time-interval analysis in measuring stuttering and trained speaking patterns, but also reveals a lack of accuracy. Educational objectives: At the end of this activity the reader will be able to (a) explain different forms of validity in relation to the use of modified time-interval analysis, (b) evaluate whether construct validity of modified time-interval analysis has been supported by the outcomes of an intervention study and (c) describe the usefulness and limitations of modified time-interval analysis for future research. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Stuttering is a disorder of speech fluency that affects approximately 1% of the world’s population. Throughout its course, stuttering usually develops in complexity and severity and can become a socially disabling condition for adults (Craig, 2000). Stuttering in adulthood can lead to social phobia, elevated levels of distress and negative mood states and has a significant negative impact on quality of life (Craig, Blumgart, & Tran, 2009; Cummins, 2010; Iverach et al., 2009). Available treatment interventions for adolescents and adults are numerous and include among others behavioral intervention as well as the use of assistive devices (e.g., DAF), and pharmaceutical agents (Bloodstein & Bernstein Ratner, 2008). Among the

∗ Corresponding author at: Kapitelbuschweg 12, 22527 Hamburg, Germany. Tel.: +49 40 57206962. E-mail addresses: [email protected], [email protected] (A. Alpermann). 0094-730X/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jfludis.2011.11.006

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behavioral approaches fluency modification and stuttering modification are the most common (Conture & Curlee, 2007). Although both approaches address stuttering, they differ in their treatment goals. Fluency modification aims for controlled fluency by use of a novel speaking pattern and for spontaneous fluency (Guitar & McCauley, 2010). However, therapists prefer the use of controlled fluency as this has the benefit of facilitating transfer and maintenance. Stuttering modification also seeks to achieve controlled fluency (e.g., use of preparatory sets), but includes acceptable stuttering (e.g., stuttering without secondary behaviors, use of pull-outs or cancellations) and spontaneous fluency in their treatment goals (Guitar, 2006). According to Guitar (2006), the results of both approaches are quite similar in that they both involve a modified style of speaking (or controlled fluency). A substantial body of literature has demonstrated that behavioral treatment of stuttering adults can be effective, resulting in a significant decrease in stuttering frequency (Andrews, Guitar, & Howie, 1980; Bothe, Davidow, Bramlett, & Ingham, 2006; Herder, Howard, Nye, & Vanryckeghem, 2006). In almost all of these studies stuttering frequency counts, expressed by the percentage of stuttered syllables or words, have been used to demonstrate changes in fluency after treatment. Despite its popularity, the reliability of stuttering frequency counts have often been questioned because of reports of low interjudge agreement (Curlee, 1981; Ingham & Cordes, 1992; Kully & Boberg, 1988). As an alternative, Cordes, Ingham, Frank, and Ingham (1992) introduced the time-interval analysis. This analysis does not focus on individual stuttering events but on the occurrence of a stuttering event within a defined time interval. Meanwhile, satisfactory interjudge and intrajudge agreement could be demonstrated for both time-interval analysis (Cordes & Ingham, 1994a, 1994b, 1995, 1996; Cordes et al., 1992; Einarsdóttir & Ingham, 2008; Ingham, Cordes, & Finn, 1993; Ingham, Cordes, & Gow, 1993) and syllable-based stuttering frequency counts (Cordes & Ingham, 1994a, 1994b; Yaruss, 1998). Regardless of the basic unit over which stuttering frequency is averaged (syllables, words or time intervals), it is interesting to note that the use of trained speaking patterns (which results in controlled fluency) is not considered in stutter count measures. As argued earlier, judges might be inclined to count most fluency modification techniques as fluent syllables, whereas stuttering modification techniques, such as a pull-out, might rather be scored as stuttered syllables (Alpermann, Huber, Natke, & Willmes, 2010). For this reason, Natke (2005a) suggested a modification of time-interval analysis, called modified time-interval analysis, by adding the category “trained speaking patterns” to the existing categories “fluent” and “stuttered”. Such, spontaneously fluent speech could be measured under the category “fluent” while the use of controlled fluency could be measured by the category “trained speaking pattern”. A modified version of time-interval analysis might be valuable as an additional measure in outcome studies and it might be useful for exploring the relationship between discontinuation of trained speaking patterns and (long-term) post-treatment relapse. Modified time-interval analysis relies on the assumption that trained speaking patterns can be identified and distinguished from spontaneous fluency in a reliable way. Indeed, several authors showed that post-treatment speech deviates from spontaneous fluency in its naturalness and that this difference in naturalness can be measured reliably (Franken, Boves, Peters, & Webster, 1995; Martin, Haroldson, & Triden, 1984; Onslow, Hayes, Hutchins, & Newman, 1992; Runyan, Bell, & Prosek, 1990; Teshima, Langevin, Hagler, & Kully, 2010). These results were corroborated by findings that the speech of stuttering adults changes after treatment in terms of acoustic parameters such as reduced variability of vowel duration (Onslow, van Doorn, & Newman, 1992; Packman, Onslow, & van Doorn, 1994). More specifically, Onslow and O’Brian (1998) showed that experienced clinicians can judge the presence of prolonged speech (gentle onsets, soft contacts, and continuous vocalization) in different adults who stutter with high intrajudge (94.7%) and interjudge agreement (98.2%). For a stuttering modification approach, Eichstädt, Watt, and Gierson (1998) demonstrated substantial intrajudge ( = 0.74) and interjudge ( = 0.69) agreement in the measurement of stuttering modification techniques (prolongation, pull-out, cancellation). Besides, Alpermann et al. (2010) investigated interjudge and intrajudge agreement of modified time-interval analysis for German stuttering specialists. It was found that, overall, interjudge and intrajudge agreement met the typical requirement of 80% and was comparable to the level of agreement among American fluency specialists only on stuttered and fluent speech (Cordes & Ingham, 1995). In a follow-up study the results could be extended by demonstrating that inexperienced clinicians can also make reliable and accurate judgments with modified time-interval analysis after having received training. As described above, modified time-interval analysis is a promising tool for measuring stuttered and fluent speech as well as the use of trained speaking patterns (controlled fluency) reliably. As the three categories (fluent, stuttered, trained speaking patterns) are evaluated at the same time, this measure has the advantage of being easy and quick in execution. However, the co-occurrence of the three categories within one time interval is problematic. Even for a person who stutters frequently, there will always be some spontaneously fluent syllables or words within a time interval. Similarly, stuttering could co-occur with one or more trained speaking patterns within one time interval. Furthermore, the three categories might not be distinguishable at all, e.g., a pull-out might be considered as stuttering or a very natural and unobtrusive use of prolonged speech might sound like spontaneous fluency. Acknowledging these threats to the validity of modified time-interval analysis, we designed this study to investigate the validity of this measure. In general, three basic types of validity can be distinguished (Schiavetti & Metz, 2006). Content validity reflects how well a measure samples the intended behavior or characteristic to be measured. Criterion validity refers to the degree to which a measure correlates with a known indicator of the behavior or characteristic it is supposed to measure. Finally, construct validity reflects the ability of an instrument to measure an abstract concept, or construct (Portney & Watkins, 2009), in this case the ability of modified time-interval analysis to measure trained speaking patterns. According to Messick (1989), construct validity also subsumes other types of validity, such as criterion-related validity and content validity (p. 17), and

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must be pursued to justify the use of a test or measure. While, historically, primary emphasis in construct validation has been placed on patterns of relationships among item scores or between test scores and other measures, Messick (1989) stated, “[] probably even more illuminating of core meaning [. . .] are studies of performance differences over time, across groups and settings, and in response to experimental treatments and manipulations” (p. 17). Following Messicks considerations, the purpose of this study was to investigate the construct validity of modified time-interval analysis by means of an intervention study with two treatment groups and one control group. Whereas the first treatment group received treatment according to the fluency modification approach, the other group was treated according to the stuttering modification approach. Several commonly employed analysis procedures were used to assess construct validity of the feasibility of modified time-interval analysis. The known groups method (Portney & Watkins, 2009) implied for this study that the two treatment groups could be distinguished from the control group by the use of trained speaking patterns. Longitudinal construct validity, also referred to as sensitivity to change (Liang, 2000), included the capacity of modified time-interval analysis to measure statistically significant change (due to the interventions). In this context, effect sizes were calculated as they are currently the most accepted and widely applied indices of sensitivity to change (Igl, 2007; Streiner & Norman, 2008). Finally, syllable-based stuttering frequency counts enabled investigation of convergent and discriminant validity as dimensions of construct validity (Cordes & Ingham, 1994a). Specifically, we investigated the following hypotheses: “Known groups validity”: only after treatment, both treatment groups will use significantly more instances of trained speaking patterns than the control group. Sensitivity to change: (a) the percentage of stuttered time intervals will decrease from pre- to post-assessment significantly more in both treatment groups than in the control group. (b) The amount of trained speaking patterns will increase significantly more in the treatment groups than in the control group. (c) The percentage of spontaneously fluent time intervals will decrease significantly more in the fluency modification group than in the control group. In the stuttering modification group this percentage will increase significantly more than in the control group. Convergent and discriminant validity: (a) the percentages of fluent and stuttered time intervals of modified time-interval analysis will correlate significantly with the percentage of stuttered syllables. (b) The percentage of time intervals with trained speaking patterns will not correlate significantly with the percentage of stuttered syllables. 2. Method 2.1. Participants A total of 92 stuttering adults provided the data for investigating construct validity of modified time-interval analysis. These adults belonged to three different groups: (a) twenty-four participants (20 men and 4 women; mean age 31 years, range 16–62 years) who received a 1-year long group-therapy following a stuttering modification-approach, (b) 30 clients (22 men and 8 women; mean age 25 years, range 14–52 years) who followed a two-week group-treatment based on the fluency modification-approach, and, (c) 38 stuttering adults (22 men, 16 women; mean age 41 years, range 19–68 years) who did not receive any therapy during the present study (control group). The participants of the control group were all members of self help organizations, who had all (except of two) attended one or more stuttering therapies previously. Inclusion criteria were: a minimum age of 14 years, the completion of treatment, sufficient language skills for a conversation in German and consent to participate in the study. Participants of the control group had neither attended any stuttering therapy within the past year nor would they start a treatment during the course of the present study. The participants could not be assigned to the groups at random because they choose their treatment program independently. Initial analyses with a one-way analysis of variance (ANOVA) showed a significant difference between groups with regard to age (F(2,89) = 18.099; p < .001). Subsequent Bonferroni corrected post-hoc t-tests failed to show a significant difference between the treatment groups (p = .110), but resulted in significant differences between the stuttering modification and the control group (p = .004) and the fluency modification and the control group (p < .001) respectively. Mean stuttering frequency in the treatment groups (SM: M = 7.7%; FM: M = 9.6%) also exceeded the values of the control group (M = 4.7%), which was supported by a one-way ANOVA (F(2,89) = 6.614; p = .002). However, as Bonferroni corrected post-hoc t-tests showed, only the mean difference of stuttering frequency in the control group and the fluency modification group reached significance (p = .002), while the stuttering modification and control group (p = .116) and the two treatment groups (p = .715) did not differ from each other. 2.2. Treatment programs and procedure The treatments were given independent of this study and the first author who analyzed the data was only in contact with the participants for data collection purposes. The participants of the stuttering modification therapy received treatment in groups of 6 persons each between 2001 and 2007. The participants of the fluency modification therapy followed treatment in groups of each 6–8 persons between 2007 and 2008. Even though total treatment length differed in the treatment groups, all participants received about the same number of hours of treatment. All participants of the treatment groups were assessed before and after the main treatment phase (see Fig. 1). The control group served as a control measure for both treatment groups, so that time lags between the assessments had to be matched to those of the treatment groups (see Fig. 1).

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Fig. 1. Assessment occasions for the treatment and control groups.

To assess changes in fluency, we obtained speech samples for every participant by telephone calls. For all assessments, the first author contacted the participants according to the assessment schedule and had a conversation of at least 10 min about the profession and/or education, hobbies or therapy of the participant. All conversations were recorded on a computer using a voice recorder (Tiptel AG, Ratingen). Afterward, the first author processed all speech samples that were available in wav-format with sound processing software. To this end, the speech of the assessor and non-speech related noise (e.g., coughing) were removed from the sample; pauses, that were clearly due to thinking, were shortened to 0.5 s. 2.3. Speech measures and reliability 2.3.1. Modified time-interval analysis For modified time-interval analysis, all speech samples for each speaker (pre-post for the treatment groups, pre-postpost2 for the control group) were merged into one continuous speech sample. Subsequently, this speech sample could be analyzed by means of time-interval software (Natke, 2005b). This software divides a wav-file of any length into time intervals (4 s for this study) and presents these time intervals in a randomized order. Each time interval is followed by a silent pause (4 s for this study), in which the preceding time interval can be judged by choosing one of three categories: stuttered, (spontaneously) fluent or trained speaking pattern(s) (= controlled fluency). Each category has been previously defined to ensure consistent judgments (for a detailed overview see Alpermann et al., 2010). After having chosen a category by clicking the corresponding box, the software presents the next time interval. If no judgment is given within the 4 s of the silent pause, the software continues with the next time interval and marks the past time interval as “not judged”. At the end of the speech sample, the software automatically generates a text-file with codes for the judgment results and calculates percentages for the proportion of the three different categories. All judges listened to the speech samples through headphones (Philips, SHP 8900). 2.4. Stuttering frequency We obtained stuttering frequency, as expressed by the percentage of stuttered syllables (%SS), by counting on-line (in realtime) the number of fluent and stuttered syllables within each running speech sample (pre, post, post2). The same definitions as described above were used to identify stuttered syllables. Afterward, we calculated the percentage of stuttering frequency by dividing the number of stuttered syllables in the sample by the total number of spoken syllables. 2.5. Reliability Each one third of the data were analyzed by the first author and two trained undergraduate students of speech-language pathology who were trained by the first author (for a description of the training, see Boberg & Kully, 1994). Individual data were randomly assigned to the judges with stratification for therapy, meaning that one judge evaluated all two or three measurements (pre, post, post2) for each selected participant and completed all analyses per speaker. Inter- and intrajudge reliability was calculated for about one third of the data with the intra-class correlation coefficient (ICC; Wirtz & Caspar, 2002). Intrajudge reliability was above 0.95 for two judges and above 0.91 for one judge on both modified time-interval analysis and stuttering frequency counts, which indicates relatively high consistency for each judge. Interjudge reliability was at or above 0.90 for both measures and indicates satisfactory agreement between the judges. Overall, the intrajudge and interjudge reliability scores correspond to the scores obtained by Alpermann et al. (2010).

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Table 1 Mean (M), standard deviation (SD) and median (Mdn) for all groups at all assessment occasions and for stuttering frequency (%SS) and modified time-interval analysis (mTIA). Measure

Pre M

%SS 7.7 SMa 9.6 FMb 4.3 CGc mTIA fluent 37.4 SM FM 41.8 58.2 CG mTIA stuttered 61.7 SM 56.5 FM 40.3 CG mTIA trained speaking patterns 0.8 SM 1.6 FM 1.3 CG

Post SD

Mdn

Post2

M

SD

Mdn

M

SD

Mdn

nad 1.7 4.0

na 2.3 4.3

na 0.9 2.1

3.5 na 4.3

4.3 na 3.4

1.7 na 3.1

6.0 7.7 4.6

6.3 8.1 2.7

24.8 27.9 27.0

34.5 46.8 63.3

na 26.3 60.7

na 28.2 26.0

an 10.5 63.7

55.5 na 60.1

25.2 na 23.3

54.7 na 64.0

24.7 28.8 27.2

65.6 52.1 33.1

na 8.1 38.0

na 9.8 26.3

na 5.3 34.9

35.0 na 38.0

23.4 na 23.5

26.6 na 33.2

1.5 2.4 6.7

0.0 0.6 0.0

na 65.5 1.2

na 34.1 5.1

na 80.3 0.0

9.3 na 1.8

13.9 na 7.5

2.6 na 0.0

a

SM refers to the stuttering modification group. FM refers to the fluency modification group. c CG refers to the control group. d na = non applicable; the stuttering modification group was only assessed at pre and post2 and the fluency modification group was only assessed at pre and post. b

2.6. Data analyses The data analyses included cross-sectional as well as longitudinal comparisons for the control group and both treatment groups. Whereas post-assessments of the fluency modification group were compared to the second assessment of the control group, post-assessments of the stuttering modification group were compared to the third assessment of the control group (see Fig. 1). Four dependent variables were evaluated in the data analyses: the percentage of stuttered syllables and the three categories of modified time-interval analysis (percentage of fluent and stuttered time intervals as well as time intervals with trained speaking patterns). After entering all data into SPSS 16.0, arc sine transformation was performed for all percentages in order to approximate a normal distribution and stabilize the variances (Klauer, 1987). For “known groups analysis”, group differences at pre- and post(2)-assessments were analyzed with one-way ANOVAs for the percentage of trained speaking patterns. To explore sensitivity to change we used mixed designs. In a repeated measures two-factor ANOVA, with Assessment Occasion (pre versus post(2)) as within-subject factor and Group as between-subject factor (stuttering modification respectively fluency modification versus control group), we examined main and interaction effects for modified time-interval analysis and frequency of stuttering. We included analyses of stuttering frequency to compare the effects found by modified time-interval analysis. Further examinations regarding sensitivity to change included comparisons of the mean change score (changes of mean scores before and after intervention = pre-post difference) of each treatment group with the control group by means of an independent t-test. Then, we calculated standard effect sizes (d ) with confidence intervals, separately for the changes of the fluency modification group, proportional to the control group, and the changes of the stuttering modification group, proportional to the control group. In this study, the effect size index was the ratio of the difference of the mean change scores of one treatment (M(treatment) 2–1) and the control group (M(control) 2–1) divided by the pooled standard deviation of the change scores (SD(change scores) pooled). For interpretation of the effect sizes, we used Cohen’s classification, implying that effect sizes of d = 0.2 were considered as small, effect sizes of d = 0.5 as medium and effect sizes of d = 0.8 and above as large (Cohen, 1977). Note that effect sizes were not reported to indicate effectiveness of the treatments but to estimate sensitivity to change. For assessment of convergent and discriminant validity we calculated Pearson product-moment correlation coefficients between percentage of stuttered syllables and the results with categories of modified time-interval analysis for all groups and all assessment occasions. 3. Results 3.1. Known groups validity “Known groups analysis” for the percentage of trained speaking patterns showed only minor group differences before treatment (see Table 1). This was supported by the one-way ANOVA, which failed to reveal a significant difference between the three groups (F(2,89) = 2.09, p = .130). At post-assessment, the number of trained speaking patterns increased in both treatment groups, whereas the percentage of trained speaking patterns in the control group remained about the same

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Fig. 2. Mean within-group changes in modified time-interval analysis; SM refers to the stuttering modification group; CG refers to the control group.

(see Table 1). Because of the different number of assessments two one-way ANOVAs were performed. A comparison of the percentage of trained speaking patterns used by the stuttering modification group at the post-assessment revealed a significant difference (F(1,60) = 16.26, p < .001). When the percentage of trained speaking patterns in the post-assessment of the fluency modification group was compared with that in the second assessment of the control group the difference was also statistically significant (F(1,66) = 173.2, p < .001). 3.2. Sensitivity to change 3.2.1. Longitudinal changes in dependent variables for treatment and control group(s) First, three repeated-measures ANOVAs were calculated for changes in the stuttering modification group and the control group. For these ANOVAs, the percentages of fluent time intervals, stuttered time intervals and time intervals with trained speaking patterns were each regarded as dependent variables, whereas Group (stuttering modification versus control group) and Assessment Occasion (before treatment versus after treatment) were considered independent variables. For the percentage of fluent time intervals, the ANOVA revealed a significant main effect for Group (F(1,60) = 4.508, p = .038), a significant effect for Assessment Occasion (F(1,60) = 18.974, p < .001), and a significant main effect for the interaction of Group × Assessment Occasion (F(1,60) = 13.419, p = .001). Thus, although the groups differed already at the pre-assessment and the control group showed increased fluency towards the post-assessment, the significant interaction effect indicates that improvements in fluency in the stuttering modification group were clearly bigger than the changes in the control group (see Fig. 2). For the percentage of stuttered time intervals, the ANOVA showed no significant main effect for Group (F(1,60) = 2.526, p = .117), but a significant main effect for Assessment Occasion (F(1,60) = 34.007, p < .001) and the interaction of Group × Assessment Occasion (F(1,60) = 25.539, p < .001). In this case, the interaction effect indicated that the percentage of stuttered time intervals in the stuttering modification group decreased significantly more than in the control group. Finally, for the percentage of time intervals with trained speaking patterns, a significant main effect was found for Group (F(1,60) = 9.024, p = .004), Assessment Occasion (F(1,60) = 28.007, p < .001), and the interaction of Group × Assessment Occasion (F(1,60) = 21.217, p < .001). Again, the interaction effect indicated a significant increase in trained speaking patterns for the stuttering modification group from pre- to post-treatment. The results were supported by the pattern of change of the percentage of stuttered syllables. The 2 × 2 ANOVA failed to reveal a significant main effect for Group (F(1,60) = 0.910, p = .344) but showed significant main effects for Assessment Occasion (F(1,60) = 21.640, p < .001) and the interaction of Group × Assessment Occasion (F(1,60) = 29.113, p < .001). Three repeated-measures ANOVAs were also executed for changes in the fluency modification group in comparison to the control group. The between-subject factor (Group) comprised the fluency modification versus the control group whereas the within-subject factor (Assessment Occasion) consisted of the two levels pre- and post2-assessment. For the percentage of fluent time intervals, the ANOVA showed a significant main effect for Group (F(1,66) = 20.179, p < .001) and significant main effects for Assessment Occasion (F(1,66) = 7.871, p = .007) and the interaction of Group × Assessment Occasion (F(1,66) = 12.044, p = .001). As can be seen in Table 2 and Fig. 3, these effects, which exceeded the changes in the control group, implied a distinct decrease in the percentage of fluent time intervals in the fluency modification group. For the stuttered time intervals, the following effects were observed: no significant main effect for Group (F(1,66) = 1.784, p = .084)

Fig. 3. Mean within-group changes in modified time-interval analysis; FM refers to the fluency modification group; CG refers to the control group.

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Table 2 T-values and effect sizes for the score change of the stuttering modification group in comparison to the control group. Measure

t (df)

Standardized effect size (d )a d

Modified time-interval analysis % fluent intervals % stuttered intervals % trained speaking patterns Frequency of stuttering (%SS) a b c * **

3.21* (33.66) −4.43** (31.62) 2.87* (23.32) −4.28** (60)

CIb Differencec

Lower

Upper

0.54

0.45

0.65

−1.37

−1.47

−1.33

0.14

1.31

1.26

1.32

0.06

−1.13

−1.15

−1.12

0.03

0.2

Interpretation of d : 0.2–0.49 = small effect; 0.5–0.79 = medium effect; >0.8 = large effect. CI = confidence interval. Difference refers to the range of the confidence interval, meaning the difference between the upper and lower limit. p < .05. p < .001.

and significant main effects for Assessment Occasion (F(1,66) = 105.227, p < .001) and the interaction of Group × Assessment Occasion (F(1,66) = 91.116, p < .001). The interaction effect revealed that stuttering had only decreased significantly in the treatment group. Finally, the increase in the percentage of trained speaking patterns in the fluency modification group exceeded the changes in the control group. Significant main effects were found for Group (F(1,66) = 152.522, p < .001), Assessment Occasion (F(1,66) = 157.049, p < .001), as well as for the interaction of Group × Assessment Occasion (F(1,66) = 153.962, p < .001). Once more, the interaction effect indicated greater increase in trained speaking patterns for the treatment group. The 2 × 2 ANOVA of the percentage of stuttered syllables did not show a significant main effect for Group (F(1,66) = 0.594, p = .444). More importantly, a significant main effect was found for Assessment Occasion (F(1,66) = 93.867, p < .001) and the interaction of Group × Assessment Occasion was significant (F(1,66) = 82.397, p < .001). Thus, in agreement with the results of modified time-interval analysis, the percentage of stuttered syllables decreased significantly more in the fluency modification group than in the control group. In sum, changes in both treatment groups significantly exceeded those of the control group for all dependent variables. Significant reduction in the amount of stuttering was detected by both the stuttering frequency measure and modified time-interval analysis. 3.2.2. Comparison of score changes and effect sizes In accordance with the analyses of variance, both score changes of modified time-interval analysis and stuttering frequency were significantly higher in the stuttering modification group than in the control group (see Table 2). Once again, the decrease in stuttering was captured by both stuttering frequency and modified time-interval analysis to a comparable degree (d > 0.8). Furthermore, modified time-interval analysis seemed to be very sensitive to changes in the number of trained speaking patterns (large effect) but less sensitive to changes in the number of fluent time intervals (medium effect, Cohen, 1977). Comparison of score changes between the fluency modification and control groups revealed significant differences that also favored the treatment group (see Table 3). The reduction of stuttering and the increase of trained speaking patterns, measured by modified time-interval analysis, as well as the reduction in stuttering frequency significantly exceeded the changes in the control group. Moreover, the reduction of spontaneous fluency after treatment differed significantly from changes in the control group. Comparisons of the effect sizes of the stuttered time intervals and the percentage of stuttered syllables both revealed large sensitivity to change (Cohen, 1977). An even larger effect was found for the increase in trained speaking patterns. In contrast, the decrease in spontaneous fluent time intervals involved only a small effect. Strikingly consistent for all effect sizes in both treatment approaches, the confidence intervals of the effect sizes in modified time-interval analysis were wider than those of the frequency measures, indicating that modified time-interval analysis was less precise. 3.3. Convergent and discriminant validity Correlations between frequency of stuttering and modified time-interval analysis were calculated for all groups and assessment occasions (see Table 4). In all cases, a significant correlation between the percentage of stuttered syllables and stuttered time intervals was found, contributing to convergent validity. With one exception in the fluency modification group at post-assessment, the percentage of fluent time intervals also correlated rather highly with stuttering frequency. Note that the negative sign of the correlations implies that few fluent time intervals were consistent with the high frequency

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Table 3 T-values and effect sizes for the score change of the fluency modification group in comparison to the control group. Measure

Standardized effect size (d )a

t (df)

d

Modified time-interval analysis % fluent intervals % stuttered intervals % trained speaking patterns Frequency of stuttering (%SS) a b c * **

2.69* (30.91) 8.63* (32.29) −10.29** (29.14) 6.14** (30.45)

CIb Lower

Upper

Differencec

−0.49

−0.62

−0.38

0.24

−2.75

−2.85

−2.72

0.13

4.00

3.88

4.01

0.13

−2.10

−2.12

−2.10

0.02

Interpretation of d : 0.2–0.49 = small effect; 0.5–0.79 = medium effect; >0.8 = large effect. CI = confidence interval. Difference refers to the range of the confidence interval, meaning the difference between the upper and lower limit. p < .05. p < .001.

of stuttering and many fluent time intervals were consistent with low stuttering frequency. Moreover, as hypothesized, no significant correlations could be found between stuttering frequency and the percentage of trained speaking patterns. 4. Discussion 4.1. Interpretation of main findings based on hypotheses The purpose of this study was to investigate the construct validity of modified time-interval analysis. Results confirmed the validity of modified time-interval analysis as these scores were most clearly consistent with our hypotheses. 4.1.1. Known groups validity Before treatment, the percentage of trained speaking patterns did not differ significantly between the three groups. After treatment, however, both treatment groups used significantly more trained speaking patterns than the control group. This confirmed our assumption that after treatment, the treatment groups can be discriminated from the control group by the number of used trained speaking patterns. 4.1.2. Sensitivity to change The expected significant decrease in stuttering immediately after treatment (Hypothesis 2a) was confirmed by the outcomes of modified time-interval analysis. Interestingly, the effect sizes of modified time-interval analysis were not only comparable to those of the frequency counts but also to the mean effect sizes found in the treatment outcome literature (Andrews, Guitar, & Howie, 1980; Herder et al., 2006). In the fluency modification group, the large effect size for the score change in comparison to the control group (d = −2.75) not only reflects a drastic reduction but also reveals that stuttering is not a desired outcome in fluency modification. In the stuttering modification group, the effect size for the score change was surprisingly large (d = −1.37), considering that acceptable stuttering is one of the treatment goals. Only some clients seem to opt for a manner of speaking that involves acceptable stuttering as the high standard deviation of the post-treatment percentage of stuttered time intervals suggests (M = 35.0%; SD = 25.4%). Additionally, the neglect of stuttering moments in time intervals where a trained speaking pattern was applied (these intervals were classified as “trained speaking pattern”) might have led to an overestimation of the reduction of stuttering at post-treatment assessments. This theory is supported Table 4 Pearson-correlations of frequency of stuttering (%SS) and the modified time-interval analysis (mTIA). Variables

%SS – mTIA fluent %SS – mTIA stuttered %SS – mTIA trained speaking patterns a b c **

Post/post2

Pre SMa

FMb

CGc

SM

FM

CG (post)

CG (post2)

−.819** .821** −.027

−.831** .824** −.192

−.825** .842** −.099

−.682** .785** −.084

−.083 .686** −.126

−.869** .880** −.099

−.877** .872** −.030

SM refers to the stuttering modification group. FM refers to the fluency modification group. CG refers to the control group. Correlation is significant at the 0.01 level (2-tailed).

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by consistently larger confidence intervals for all categories of modified time-interval analysis compared to the frequency counts (see Tables 2 and 3), suggesting that modified time-interval analysis is less precise than frequency counts. Confirming our prediction (Hypothesis 2b), the increase in trained speaking patterns exceeded in both treatment groups the changes in the control group. The large effect sizes (SM: d = 1.31, FM: d = 4.0) confirmed that modified time-interval analysis is a sensitive measure for changes in controlled fluency by the use of trained speaking patterns. Changes in spontaneous fluency were expected to differ between the treatment groups due to their differing treatment goals (Hypothesis 2c). As hypothesized, spontaneous fluency in the stuttering modification group increased. However, the effect size indicated only a medium effect (d = 0.54), possibly suggesting an underestimation of increase in spontaneous fluency when measured with modified time-interval analysis. Indeed, underestimation may be the case because time intervals are not judged “fluent” if they contain only one instance of stuttering or trained speaking pattern. In the fluency modification group, the significant decrease of the amount of spontaneous fluency compared to the control group was also expected. However, the results of modified time-interval analysis suggest that spontaneously fluent speech was, rather unexpected, still predominant in some clients (see Table 1). Possibly, the judges were, at least for some clients or parts of the speech samples, unable to distinguish between spontaneous and controlled fluency, such compromising construct validity of modified time-interval analysis. However, during the telephone interviews, the first author also gained the subjective impression that some clients were indeed able to speak spontaneously fluently without using any trained speaking pattern. This increase in spontaneous fluency without use of the learned speaking pattern is frequent during and immediately after treatment, although it’s exact cause is unknown and uninvestigated (Natke, Alpermann, Heil, Kuckenberg, & Zückner, 2010). More importantly, clients may discover that, for the time being, they can talk spontaneously fluently without the effort of monitoring their speech, and thus some may deliberately decide not to use their novel pattern of speech. Others may simply forget (in some situations) to apply their trained speaking pattern. The high standard deviation of fluent time intervals in this treatment group (SD = 28.2; range: 0.0–78.2%) supports this hypothesis of individual choices for controlled or spontaneous fluency. Consequently, the small decrease in fluent time intervals is not necessarily a sign of lack of sensitivity of modified time-interval analysis, but rather it indicates differences in the use of trained speaking patterns among the participants. 4.1.3. Convergent and discriminant validity Our last hypothesis (Hypothesis 3) implied that modified time interval analysis differs from traditional frequency counts in the category “trained speaking pattern(s)”. Consistent with this claim, we found high correlations between the percentage of stuttered syllables and the percentage of fluent as well as stuttered time intervals, suggesting that the same constructs were measured (convergent validity). Strikingly, one correlation did not reach significance; the post-treatment correlation between percentage of stuttered syllables and the percentage of fluent time intervals in the fluency modification group (see Table 4). As reported earlier, the amount of stuttering after treatment was consistently low in this group. In contrast, the standard deviation of (spontaneously) fluent time intervals was quite high because some clients chose to speak without their learned speaking pattern. Thus, the different distributions of stuttering and fluent values may have caused the correlation to not be statistically significant. Discriminant validity of the category “trained speaking patterns” was proven by the consistently low correlations between the percentage of trained speaking patterns and the frequency of stuttering for all groups and assessment occasions. 4.2. Limitations and future research While the results from the present study are encouraging, they are still preliminary and influenced by some limitations in the present design and the nature of the time-interval judgment system. Most problematic, trained speaking patterns, stuttering and spontaneously fluent speech can co-occur within one time interval, influencing the accuracy of the results as described above. There are several options of dealing with this ambiguity that should be explored in future studies. Shorter time intervals (e.g., 3 s) or word-based judgments might not rule out, but reduce the frequency of both stuttering and trained speaking patterns within a time interval. Also, the modified time-interval software could be altered by adding a fourth category “stuttering and trained speaking pattern(s)”. The difficulty to discriminate between a very natural, unobtrusive use of trained speaking patterns and spontaneously fluent speech is apparent, which is why we took a number of measures to minimize this bias in advance. Interjudge and intrajudge reliability were checked ahead of time and found to be satisfactory. Secondly, all judges were blind with regard to the assessment occasion of a time interval so that they were less influenced by any anticipation of treatment outcomes. Thirdly, the two student judges, whose judgments were quite consistent with those of the author, were blind to the group affiliation of speakers. Still, future research on changes in fluency following a range of treatment types is necessary in order to replicate the results regarding spontaneous fluency, controlled fluency and stuttering, as well as their associated effect sizes. Because effect sizes are a characteristic of both treatments and sensitivity to change, the present results cannot be fully appreciated unless outcomes with comparable measures are reported. The present results might also be biased by the selection of the control group which consisted only of members of self help organizations. Although we would not assume that this bias has systematically affected the data of the present study, future studies with the modified time-interval analysis should also include different participants in the control group.

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To ensure internal validity, it should be explored if outcomes from video recordings are consistent with results from audio-only recordings (see Alpermann et al., 2010). Another important step to substantiate validity of modified time-interval analysis might be to compare its outcomes with naturalness ratings, acoustic analyses and/or self-ratings of clients and/or clinicians on the application of trained speaking patterns. Previous research has indicated that stuttering adults are able to make relatively consistent and valid judgments of their own levels of speech monitoring (Finn & Ingham, 1994).

5. Conclusions In summary, evidence from this study supports the construct validity of modified time-interval analysis. It seems that this measure allows more detailed insight into changes of fluency after different treatment approaches than counts of stuttering frequency only. However, modified time-interval analysis tends to underestimate spontaneous fluent speech and overestimates reductions in stuttering after therapy. Consequently, it would be premature to abandon frequency counts in favor of modified time-interval analysis as further research on its accuracy and generalizability is needed. Consequently, we encourage for future outcome studies of different behavioral stuttering treatments to use both measures in parallel and supplement them with clients’ self-ratings of their use of trained speaking patterns. CONTINUING EDUCATION 1. Which of the following statements is true regarding the goals of stuttering modification and fluency modification? (a) Stuttering modification and fluency modification have the same goal of stutter-free speech. (b) Controlled fluency is the most important goal for both approaches. (c) Fluency modification aims for controlled and spontaneous fluency while stuttering modification also allows acceptable stuttering. (d) Acceptable stuttering is not an acceptable goal for stuttering modification. (e) Only fluency modification makes a difference between spontaneous fluency and controlled fluency. 2. Modified time-interval analysis is a measure that allows to: (a) Evaluate the naturalness of speech. (b) Discriminate between trained speech patterns of stuttering modification and fluency modification. (c) Evaluate how accurate trained speech patterns are being applied. (d) Listen to a speech sample once and judge on three different categories. (e) Determine for a number of intervals whether they were stuttered or fluent 3. Which of the following hypotheses was established for this study? (a) In both treatment groups, the percentage of stuttered intervals decreases significantly more than in the control group. (b) After treatment, all groups use significantly more trained speech patterns. (c) In the fluency modification group, the percentage of fluent intervals increases significantly more than in the control group. (d) Before treatment, the control group uses significantly more trained speech patterns than the treatment groups. (e) The percentage of intervals with trained speech patterns correlate significantly with the percentage of stuttered syllables. 4. Construct validity of the modified time-interval analysis (a) Is not given as prior hypotheses could not be confirmed. (b) Is threatened as convergent and discriminate validity were not satisfactory. (c) Could be supported preliminary, but has to be confirmed in the future, e.g., by naturalness ratings, acoustic measures or self-ratings. (d) Could be proven as it measures the same construct as stuttering frequency counts. (e) Has to be rejected as it does not allow the measurement of trained speech patterns acquired in different treatments. 5. What kind of future research on the modified time-interval analysis should be considered? (a) Modified time-interval analysis should replace stuttering frequency counts in outcome studies. (b) A variation of interval length and/or a word-based measure with modified time-interval analysis should be addressed. (c) The reliability of modified time-interval analysis among experts on stuttering should be investigated. (d) Future research on modified time-interval analysis is unnecessary. (e) The outcomes of modified time-interval analysis should be compared to ratings of naïve listeners whether trained speech patterns were applied. Acknowledgments This work was supported by a research grant to the first author from the German association of speech-language therapy (DBL e.V.), and a dissertation grant from Zuyd University Heerlen (NL). We thank students Stephanie Fischer and Katharina Schwambach for their analyses in this study and express our gratitude to the clients who participated in this study.

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References Alpermann, A., Huber, W., Natke, U., & Willmes, K. (2010). Measurement of trained speech patterns in stuttering: Interjudge and intrajudge agreement of experts by means of modified time-interval analysis. Journal of Fluency Disorders, 35(3), 299–313. Andrews, G., Guitar, B., & Howie, P. (1980). Meta-analysis of the effect of stuttering treatment. Journal of Speech and Hearing Research, 45(3), 287–307. Bloodstein, O., & Bernstein Ratner, N. (2008). A handbook on stuttering (6th ed.). Clifton Park, NY: Thomson-Delmar. Boberg, E., & Kully, D. (1994). Long-term results of an intensive treatment program for adults and adolescents who stutter. Journal of Speech and Hearing Research, 37, 1050–1059. Bothe, A. K., Davidow, J. H., Bramlett, R. E., & Ingham, R. J. (2006). Stuttering treatment research 1970–2005: I. Systematic review incorporating trial quality assessment of behavioral, cognitive, and related approaches. American Journal of Speech Language Pathology, 15(4), 321–341. Cohen, J. (1977). Statistical power analysis for behavioral sciences (revised ed.). New York: Academic Press. Conture, E. G., & Curlee, R. (2007). Stuttering and related disorders of fluency (3rd ed.). NY: Thieme. Cordes, A. K., & Ingham, R. J. (1994a). The reliability of observational data: II. Issues in the identification and measurement of stuttering events. Journal of Speech and Hearing Research, 37(2), 279–294. Cordes, A. K., & Ingham, R. J. (1994b). Time-interval measurement of stuttering: Effects of training with highly agreed or poorly agreed exemplars. Journal of Speech and Hearing Research, 37(6), 1295–1307. Cordes, A. K., & Ingham, R. J. (1995). Judgments of stuttered and nonstuttered intervals by recognized authorities in stuttering research. Journal of Speech and Hearing Research, 38(1), 33–41. Cordes, A. K., & Ingham, R. J. (1996). Time-interval measurement of stuttering: Establishing and modifying judgment accuracy. Journal of Speech and Hearing Research, 39(2), 298–310. Cordes, A. K., Ingham, R. J., Frank, P., & Ingham, J. C. (1992). Time-interval analysis of interjudge and intrajudge agreement for stuttering event judgments. Journal of Speech and Hearing Research, 35(3), 483–494. Craig, A. R. (2000). The developmental nature and effective treatment of stuttering in children and adolescents. Journal of Developmental and Physical Disabilities, 12(3), 173–186. Craig, A., Blumgart, E., & Tran, Y. (2009). The impact of stuttering on the quality of life in adult people who stutter. Journal of Fluency Disorders, 34, 61–71. Cummins, R. A. (2010). Fluency disorders and life quality: Subjective wellbeing vs health related quality of life. Journal of Fluency Disorders, 35, 161–172. Curlee, R. F. (1981). Observer agreement on disfluency and stuttering. Journal of Speech and Hearing Research, 24, 595–600. Eichstädt, A., Watt, N., & Girson, J. (1998). Evaluation of the efficacy of a stutter modification program with particular reference to two new measures of secondary behaviors and control of stuttering. Journal of Fluency Disorders, 23, 231–246. Einarsdóttir, J., & Ingham, R. J. (2008). The effect of stuttering measurement training on judging stuttering occurrence in preschool children who stutter. Journal of Fluency Disorders, 33(3), 167–179. Finn, P., & Ingham, R. J. (1994). Stutterers’ self-ratings of how natural speech sounds and feels. Journal of Speech and Hearing Research, 37(2), 326–340. Franken, M. C., Boves, L., Peters, H. F. M., & Webster, R. L. (1995). Perceptual rating instrument for speech evaluation of stuttering treatment. Journal of Speech and Hearing Research, 38, 280–288. Guitar, B. (2006). Stuttering: An integrated approach to its nature and treatment (3rd ed.). Baltimore: Williams & Wilkins. Guitar, B., & McCauley, R. (2010). Treatment of stuttering: Established and emerging interventions. Baltimore: Williams & Wilkins. Herder, C., Howard, C., Nye, C., & Vanryckeghem, M. (2006). Effectiveness of behavioral stuttering treatment: A systematic review and meta-analysis. Contemporary Issues in Communication Science and Disorders, 33, 61–73. Igl, W. (2007). Änderungssensitivität und Responsivität von generischen Patientenfragebogen in der Rehabilitation. Doctoral dissertation. Germany: AlbertLudwigs-Universität, Freiburg i.Br. Ingham, R. J., & Cordes, A. K. (1992). Interclinic differences in stuttering-event counts. Journal of Fluency Disorders, 17(3), 171–176. Ingham, R. J., Cordes, A. K., & Finn, P. (1993). Time-interval measurement of stuttering: Systematic replication of Ingham, Cordes, and Gow (1993). Journal of Speech and Hearing Research, 36(6), 1168–1176. Ingham, R. J., Cordes, A. K., & Gow, M. L. (1993). Time-interval measurement of stuttering: Modifying interjudge agreement. Journal of Speech and Hearing Research, 36(3), 503–515. Iverach, L., Jones, M., O’Brian, S., Block, S., Lincoln, M., Harrison, E., et al. (2009). Screening for personality disorders among adults seeking speech treatment for stuttering. Journal of Fluency Disorders, 34, 173–186. Klauer, K. J. (1987). Kriteriumsorientierte tests. Göttingen: Hogrefe. Kully, D., & Boberg, E. (1988). An investigation of interclinic agreement in the identification of fluent and stuttered syllables. Journal of Fluency Disorders, 13(5), 309–318. Liang, M. H. (2000). Longitudinal construct validity: Establishment of clinical meaning in patient evaluative instruments. Medical Care, 9(Suppl. II), 84–90. Martin, R. R., Haroldson, S. K., & Triden, K. A. (1984). Stuttering and speech naturalness. Journal of Speech and Hearing Disorders, 49, 53–58. Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13–103). New York: Macmillan. Natke, U. (2005a). Stottern: Erkenntnisse, Theorien und Behandlungsmethoden (2nd ed.). Bern: Huber. Natke, U. (2005b). Time-interval software. Neuss: Natke. Natke, U., Alpermann, A., Heil, W., Kuckenberg, S., & Zückner, H. (2010). Langzeiteffekte der/long-term effects of the Intensiv-Modifikation Stottern (IMS). Sprache – Stimme – Gehör, 34, 155–164. Onslow, M., Hayes, B., Hutchins, L., & Newman, D. (1992). Speech naturalness and prolonged-speech treatments for stuttering: Further variables and data. Journal of Speech and Hearing Research, 35(2), 274–282. Onslow, M., van Doorn, J., & Newman, D. (1992). Variability of acoustic segment durations after prolonged-speech treatment for stuttering. Journal of Speech and Hearing Research, 35(3), 529–536. Onslow, M., & O’Brian, S. (1998). Reliability of clinicians’ judgments about prolonged-speech targets. Journal of Speech, Language and Hearing Research, 41(5), 969–975. Packman, A., Onslow, M., & van Doorn, J. (1994). Prolonged speech and modification of stuttering: Perceptual, acoustic, and electroglottographic data. Journal of Speech and Hearing Research, 37(4), 724–737. Portney, L. G., & Watkins, M. P. (2009). Foundations of clinical research: Applications to practice (3rd ed.). Upper Saddle River, NY: Prentice Hall Health. Runyan, C. M., Bell, J. N., & Prosek, R. A. (1990). Speech naturalness ratings of treated stutterers. Journal of Speech and Hearing Disorders, 55(3), 434–438. Schiavetti, N., & Metz, D. E. (2006). Evaluating research in communicative disorders (5th ed.). Boston: Pearson. Streiner, D. L., & Norman, G. R. (2008). Health measurement scales (4th ed.). New York, NY: Oxford University Press. Teshima, S., Langevin, M., Hagler, P., & Kully, D. (2010). Post-treatment speech naturalness of comprehensive stuttering program clients and differences in ratings among listener groups. Journal of Fluency Disorders, 35(1), 44–58. Wirtz, M., & Caspar, F. (2002). Beurteilerübereinstimmung und Beurteilerreliabilität. Göttingen: Hogrefe. Yaruss, J. S. (1998). Real-time analysis of speech fluency: Procedures and reliability training. American Journal of Speech Language Pathology, 7(2), 25–37.

Anke Alpermann was born in Mannheim, Germany. She studied speech-language pathology at the HAN University of Applied Sciences and the RWTH Aachen University. Recently she finished her doctoral studies at the Department of Neurology, Section Neurolinguistics, RWTH Aachen University and works now in a private practice specialized in stuttering treatment.

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Walter Huber was born in 1945, is a professor emeritus of neurolinguistics at the Neurology Department of the RWTH Aachen University in Germany, where he has been directing the speech/language clinic and the aphasia ward. Dr. Huber has been trained in general linguistics at the Free University in Berlin and at Harvard/MIT in Cambridge, Massachusetts. He obtained his doctoral degree (Dr. Phil.) in 1980 with a dissertation on generative syntax of German, and his medical habilitation in 1985 for his research on aphasia. Since 1991, Dr. Huber has been coordinating the newly developed study program for teaching and research logopedics. His ongoing research activities are still in the field of aphasia with recent emphasis on the study of functional reorganization by means of brain imaging. Other research interests are disorders of language development, fluency disorders, dyslexia and sign language. Dr. Huber has published three books, two standardized test batteries and more than 140 research articles. In 2000, he and his team have received the Helmut-Bauer Award for Rehabilitation from the German Neurological Society. Ulrich Natke was born in Bremen, Germany. After studying mathematics he worked as a lecturer and researcher with special interest in fluency disorders at the Institute of Experimental Psychology of the Heinrich-Heine-University Düsseldorf. In 1999 he finished his doctoral dissertation about sensorimotor control of fluent and stuttered speech. Ulrich Natke is the author of a German monograph on stuttering. As a person who stutters he is also engaged in the fields of self-help, stuttering treatment, and evaluation of treatment outcomes. Klaus Willmes was born in Arnsberg, Germany. He holds a M.Sc. degree both in mathematics and psychology from the RWTH Aachen University. In 1987 he finished his dissertation in psychology about multivariate permutation tests at the University of Trier, Germany. His habilitation was at the University of Bielefeld, Germany, in 1994 on psychometrics in neuropsychology. Since 1997 he is full professor of Neuropsychology at the Medical Faculty of the RWTH Aachen University, closely collaborating with the Section Neurolinguistics.