Sleep problems characterized by later bedtimes, insomnia and excessive daytime sleepiness occur in different populations. Sleep problems are highly prevalent, affecting approximately 20–30% of children, and they influence multiple domains of child and family functioning as proven studies using different methods. Sleep problems are connected with many psychiatric disorders, including ADHD, autism, cognitive functioning problems, and behavior problems et al. There is an old important issue in the psychopathology field as to whether the latent construct is dimensional or taxometric. This study examined the latent structure of the sleep problems measured by the Childrens Sleep Habits Questionnaire (CSHQ). Participants We took the cluster-stratified sampling procedure, and chose 4 districts in Shenzhen. In each district, a primary school was chosen, and in each school, one class was chosen in each grade. 950 parents of the selected children participated in the paper-and-pencil survey during parents’ meetings held at the schools between late February and early March in 2011. Finally, we had 912 questionnaires available. The sample population consisted of 912 children aged 6–14 years inclusive, including 495 boys (54.3%), 413 girls (45.3%) and 4 missing values about gender (0.4%), with a mean age of 8.9 years (SD = 1.9). The children were comprised of first through sixth grade and they came from schools in Shenzhen City, which is located in the south of China mainland. The number and the portion of each grade are as follows: the first grade 235 (25.8%), the second grade 135 (14.8%), the third grade 155 (17.0%), the fourth grade 133 (14.6%), the fifth grade 136 (14.9%), and the sixth grade 118 (12.9%). There were 170 missing values in total, and we took the expectation maximization (EM) method to replace them. Measures The CSHQ is a sleep questionnaire for children designed for screening a wide range of sleep problems among school age children. According to recall of a recent typical week, parents rated the occurrence of 33 sleep habits or sleep problems on a 3-point scale:3 = usually (5–7 times/week),2 = sometimes (2–4 times/week),1 = rarely (0–1 times/week). The questionnaire yields a total score and eight subscale scores: Bedtime Resistance, Sleep Onset Delay, Sleep Duration, Sleep Anxiety, Night Wakings, Parasomnias, Sleep Disordered Breathing and Daytime Sleepiness. The higher the score, the more likely to have sleep problems. A total score above 41 is considered to have a serious sleep problem. The CSHQ has demonstrated adequate reliability (coefficient alpha 0.68–0.78, testC¨retest reliability 0.62–0.79) and validity. In this study, we took the Chinese language version of the CSHQ. Reliability (coefficient alpha 0.71) and validity (content and construct) of the Chinese version of CSHQ are good. In order to identify the latent structure of CSHQ, we used three independent taxometric procedures: MAMBAC (mean above minus below a cut), MAXEIG (maximum eigenvalue) and L-Mode (latent-mode factor analysis). We applied three taxometric procedures to analysis our data and used Ruscio’s taxometric R program(http://www.tcnj.edu/∼ruscio/taxometrics.html) to get taxometric plots and ran all calculations. Sleep problems were measured by three main factors£ – we called F1–F3 (bad sleep habit⧹problems about sleep discords⧹problems about sleep time and daynapping). We took three taxometric analysis procedures (MAMBAC⧹MEIGN⧹LMODE) to apply in the latent structure analysis of the CSHQ. Three indicators were used in the taxometric analysis procedures, which have enough wide range and proper Skewness and Kurtosis. The correlations between indicators were around 0.3. Most of the Cohen’s d values except one are larger than 1.25SD, indicating the indicators deemed appropriate for taxometric analysis. The present study indicates that the latent structure of the CSHQ is dimensional. It aids in better understanding of the latent structure of CSHQ-measured sleep problems among Chinese school-aged children. The authors thank the lovely children and their parents and teachers. We also appreciate the efforts of Rui Ma (Nanshan Xuefu Primary School, Shenzhen, China), Lin Lin and her three classmates (Shenzhen University, China) for their help with data collection and entry.