Pearson Chi-squared Test Statistics Research Articles

Adherence to Clinical Guidelines for Dose Finding and Monitoring Methylphenidate Use: A Medical Record Audit in Child and Adolescent Mental Health Care and Pediatric Settings.

Background: Guideline adherence is important to ensure optimal and safe use of methylphenidate for children and adolescents with attention-deficit/hyperactivity disorder (ADHD). We investigated adherence to Dutch guidelines regarding dosing and monitoring of methylphenidate in child and adolescent mental health care and pediatric treatment settings. Methods: Five hundred six medical records of children and adolescents were investigated in 2015 and 2016. We assessed adherence to the following guideline recommendations: (1) at least four visits during the dose-finding phase; (2) monitoring thereafter at least every 6 months; (3) measuring height and weight at least annually; and (4) the use of validated questionnaires to assess treatment response. Pearson's chi-squared test statistics were used to examine differences between settings. Results: Only a small portion of patients had at least four visits during the dose-finding phase (5.1% in the first 4 weeks to 12.4% in the first 6 weeks). Also, less than half of the patients (48.4%) were seen at least every 6 months. Height was recorded at least annually in 42.0% of patients, weight in 44.9%, and both recorded in a growth chart in 19.5%. Questionnaires to assess treatment response were only used in 2.3% of all visits. When comparing both settings, more patients in the pediatric settings were seen every 6 months, although height and weight were recorded more often in the mental health care setting. Conclusion: Overall, guideline adherence was low. Training of clinicians and adding guideline recommendations to electronic medical records templates may improve adherence. Additionally, we should aim to close the gap between guidelines and clinical practice by looking critically at the feasibility of guidelines.

Effect of Educational Intervention Program on Self-Efficacy of Individuals with Type 2 Diabetes Mellitus in South-East, Nigeria.

Diabetes Mellitus is a chronic disease, which requires a level of confidence among the sufferers in its management. This study investigated the effect of an educational intervention program on self-efficacy (SE) in diabetic individuals with type 2 diabetes mellitus in South-East, Nigeria. The study was a quasi-experimental controlled study, comprising 382 individuals with type 2 DM selected, and assigned to intervention (IG) and control groups (CG). The instrument for data collection was the Stanford Chronic Disease Self-Efficacy Scale (SCDS). Pretest data were collected, and thereafter education on diabetes management was given to the IG group. The IG was followed up for 6 months. At the end of 6 months, post-test data were collected using the same instrument. Data were analyzed using Pearson Chi-square test statistics. A P-value less than .05 alpha level was considered significant. There was no statistically significant difference between the two groups before intervention. However, after 6 months of intervention, a significant proportion of participants' scores in IG moved from low to either moderate, or high SE in almost all the SE domains (P < .05. There was an improvement in most domains of self-efficacy in the intervention group after 6 months of educational intervention.

Prevalence and risk factors for motorcycle accidents presenting to the Korle-Bu Teaching Hospital, Ghana

Introduction: Road traffic injury is of great public health concern, as it stands as the eighth leading cause of death globally with the most affected being the youth aged 15-29 years. It is projected that by 2030, road traffic deaths will become the fifth leading cause of death globally unless urgent action is taken to avert it. Lack of employment has forced many youths in Sub-Saharan Africa into the commercial use of motorcycles known commonly as “Okada” business. Unfortunately, most of these riders do not undertake any formal and structured training or are licensed to be riders. Motorcyclists are among the most vulnerable road users in Ghana; they contribute to about 34% of all road traffic injuries. There is a progressive increase in motorcycle-related accidents as a result of the rise in the use of these bikes for commercial transport despite it being illegal. Statistics from the National Road Safety Commission of Ghana indicate that in 2020 road traffic crashes involving motorcycles killed 1,056 and injured 4,684 commuters. The primary aim of this study is to identify the prevalence and the associated risk factors for motorcycle accident cases and to assess the knowledge of motorcyclists’ on-road regulations on admission at the Korle-Bu Teaching Hospital, a national referral point for all emergency cases in Ghana. Methods: The study employed a cross-sectional design that used a structured questionnaire to collect data from 200 respondents using a systematic sampling technique for enrolment. Pearson's Chi-square test statistics were used to estimate the association between helmet usage and respondents’ knowledge level of road regulations on selected socio-demographic characteristics. Results: The results revealed that demographic characteristics such as age (p &lt;0.001), marital status (p&lt;0.001), education (p=0.035) and occupation (p=0.002) have shown a strong association with helmet use. Further analysis conducted on respondents’ knowledge level of road regulations showed a statistically significant association with their education (p &lt;0.001), religion (p=0.001) and occupation (p=0.006). Less than 40% of the participants reported insuring their motors and having valid national health insurance. The accident victims attributed the cause of their situation to non-adherence to road signs and overspeeding. Conclusion: Motorcycle-related accidents could be reduced through law enforcement agencies, continuous mass education using all media available and helmet use. Also, there should be a collaboration between all road sectors and road users to find a solution to the current motor accident trend.

Pearson's goodness-of-fit tests for sparse distributions

Pearson's chi-squared test is widely used to test the goodness of fit between categorical data and a given discrete distribution function. When the number of sets of the categorical data, say k, is a fixed integer, Pearson's chi-squared test statistic converges in distribution to a chi-squared distribution with k−1 degrees of freedom when the sample size n goes to infinity. In real applications, the number k often changes with n and may be even much larger than n. By using the martingale techniques, we prove that Pearson's chi-squared test statistic converges to the normal under quite general conditions. We also propose a new test statistic which is more powerful than chi-squared test statistic based on our simulation study. A real application to lottery data is provided to illustrate our methodology.

Tecnoestrés docente y percepción de la calidad de servicio en una universidad privada de Lima

&lt;p&gt;El presente estudio tiene como objetivo conocer la asociación existente entre el tecnoestrés docente y la percepción de la calidad de servicio al interior de una universidad peruana. Las perturbaciones psicofisiológicas que podrían estar provocando los medios tecnológicos al entrar en contacto con los trabajadores son analizadas como parte del impacto de las nuevas tecnologías en los espacios laborales. En tal sentido, se llevó a cabo una investigación cuantitativa y no experimental en la que se recolectaron datos mediante la aplicación de dos instrumentos: el Cuestionario Red (2004) el cual contiene 16 ítems vinculado al tecnoestrés y el Modelo ServQual con 22 ítems el cual midió la percepción de la calidad de servicio. Ambos instrumentos pasaron la prueba de confiabilidad a través del coeficiente de Alfa de Cronbach obteniendo un valor de ,891 y ,911, respectivamente. A través de un muestreo probabilístico se consideró como muestra a 154 docentes. Para el análisis estadístico descriptivo e inferencial, se generaron tablas de contingencia, utilizando el estadístico de prueba Chi-Cuadrado de Pearson, a partir del cual se concluye que existe asociación entre el Tecnoestrés docente y la percepción de la calidad de servicio (&lt;em&gt;X&lt;sup&gt;2&lt;/sup&gt; &lt;/em&gt;(6) = 85,731 y la Sig. Asintótica (bilateral) o p_valor = 0.000 &amp;lt; 0.05). Asimismo, se concluye que existe asociación entre el Tecnoestrés docente y las percepciones de cada de las dimensiones teóricas de la calidad de servicio: elementos tangibles (&lt;em&gt;X&lt;sup&gt;2&lt;/sup&gt; &lt;/em&gt;(6) = 112,055 y la Sig. Asintótica (bilateral) o p_valor = 0.000 &amp;lt; 0.05), fiabilidad (&lt;em&gt;X&lt;sup&gt;2&lt;/sup&gt; &lt;/em&gt;(6) = 63,859 y la Sig. Asintótica (bilateral) o p_valor = 0.000 &amp;lt; 0.05), capacidad de respuesta (&lt;em&gt;X&lt;sup&gt;2&lt;/sup&gt; &lt;/em&gt;(6) = 35,580 y la Sig. Asintótica (bilateral) o p_valor = 0.000 &amp;lt; 0.05), seguridad (&lt;em&gt;X&lt;sup&gt;2&lt;/sup&gt; &lt;/em&gt;(6)&lt;em&gt; &lt;/em&gt;= 42,838 y la Sig. Asintótica (bilateral) o p_valor = 0.000 &amp;lt; 0,05) y la empatía (&lt;em&gt;X&lt;sup&gt;2&lt;/sup&gt; &lt;/em&gt;(6)&lt;em&gt; &lt;/em&gt;= 23,633, con seis grados de libertad (6) y la Sig. Asintótica (bilateral) o p_valor = 0.000 &amp;lt; 0.05).&lt;/p&gt;

Effects of health-related quality of life on health service utilisation in patients with colorectal neoplasms.

This study was to assess the impact of HRQOL on health service utilisation using four different count data models. The HRQOL was measured using the Short-Form Six-Dimension instrument and the functional assessment of cancer therapy-colorectal whereas health service utilisation was measured by the number of monthly clinical consultations and the number of monthly hospitalisation. Different count data models (Poisson's regression, negative binomial regression, zero-inflated Poisson's regression and zero-inflated negative binomial regression) were used to assess the association between HRQOL and health service utilisation. A performance comparison was made between the models. Goodness-of-fit statistics (the Pearson's chi-squared test statistic, the Akaike and Bayesian information criteria) were used to determine the best-fitting model. The negative binomial model performed the best in assessing the association between HRQOL measures and health service utilisation in patients with colorectal neoplasm and thus recommended. Physical well-being of patients was negatively and significantly associated with the monthly rate of health service utilisation after controlling for patient demographics. Both physical and function well-beings of patients were negatively and significantly associated with the number of monthly hospitalisations. If the data for the condition-specific FACT-C are not available, SF-6D showed a very strong negative relationship with health service utilisation. Such models can be used to guide the allocation of clinical resources and funding for the care of colorectal cancer patients.

Caesarean Sections in Beijing, China - Results from a Descriptive Study.

Caesarean section rates are increasing dramatically in China. The predominant contributor to the increase in China was the unnecessary Caesarean section. The objective of this study was to estimate the excess economic burden caused by unnecessary Caesarean sections vs. vaginal delivery for Beijing and all China. The 33 476 obstetric medical case files from randomly selected 17 hospitals of Beijing in 2011 were taken for our descriptive approach. Based on the individual medical files and on a definition of what necessary and unnecessary Caesarean sections are, we sampled also the data for costs and length of stay for comparisons with vaginal deliveries. The Mann-Whitney U test and Pearson chi-square test statistics were used to test for significant difference in the analysis. The Caesarean section rate was 58.5% among all deliveries and that rate of unnecessary Caesarean sections was 71.5% among all the Caesarean sections but 41.8% among all deliveries in Beijing. The hospitalisation expenses of unnecessary Caesarean sections were 472 US$ higher than that of a vaginal delivery on average. The total excess economic burden caused by unnecessary Caesarean sections can be estimated to 38.97 million US$ for Beijing and to 3.29 billion US$ all over China in 2011. This is equivalent to the annual health expenditure of over 139 575 residents in Beijing and of 11 783 120 residents in China, respectively. Socio-economic factors are possible reasons for the increase of unnecessary Caesarean section in China and more analytical attention should be paid to that problem in order to propose fitting practical reactions.

Investigating the Association Structure of Two Categorical Variables

On the basis of the orthogonal decomposition of Pearson's chi-square test statistics, a new testing method is proposed to investigate the association between two categorical variables. The approximate sampling distributions of the proposed test statistics were studied by using intensive Monte Carlo simulation. Two examples are used to demonstrate the application of the proposed method.

K-모집단 동질성검정에서 피어슨검정의 오차성분 분석에 관한 연구

Traditional Pearson chi-squared test is not appropriate for the data collected by the complex sample design. When one uses the traditional Pearson chi-squared test to the complex sample categorical data, it may give wrong test results, and the error may occur not only due to the biased variance estimators but also due to the biased point estimators of cell proportions. In this study, the design based consistent Wald test statistics was derived for k-population homogeneity test, and the traditional Pearson chi-squared test statistics was partitioned into three parts according to the causes of error; the error due to the bias of variance estimator, the error due to the bias of cell proportion estimator, and the unseparated error due to the both bias of variance estimator and bias of cell proportion estimator. An analysis was conducted for empirical results of the relative size of each error component to the Pearson chi-squared test statistics. The second year data from the fourth Korean national health and nutrition examination survey (KNHANES, IV-2) was used for the analysis. The empirical results show that the relative size of error from the bias of variance estimator was relatively larger than the size of error from the bias of cell proportion estimator, but its degrees were different variable by variable.

Analysis of ϕ-divergence for loglinear models with constraints under product-multinomial sampling

The loglinear model under product-multinomial sampling with constraints is considered. The asymptotic expansion and normality of the restricted minimum ϕ-divergence estimator (RMϕDE) which is a generalization of the maximum likelihood estimator is presented. Then various statistics based on ϕ-divergence and RMϕDE are used to test various hypothesis test problems under the model considered. These statistics contain the classical loglikelihood ratio test statistics and Pearson chi-squared test statistics. In the last section, a simulation study is implemented.

Effect of Bias on the Pearson Chi-squared Test for Two Population Homogeneity Test

Categorical data collected based on complex sample design is not proper for the standard Pearson multinomial-based chi-squared test because the observations are not independent and identically distributed. This study investigates effects of bias of point estimator of population proportion and its variance estimator to the standard Pearson chi-squared test statistics when the sample is collected based on complex sampling scheme. This study examines the effect under two population homogeneity test. The standard Pearson test statistic can be partitioned into two parts; the first part is the weighted sum of <TEX>${\chi}^2_1$</TEX> with eigenvalues of design matrix as their weights, and the additional second part which is added due to the biases of the point estimator and its variance estimator. Our empirical analysis shows that even though the bias of point estimator is small, Pearson test statistic is very much inflated due to underestimate the variance of point estimator. In the connection of design-based variance estimator and its design matrix, the bigger the average of eigenvalues of design matrix is, the larger relative size of which the first component part to Pearson test statistic is taking.

Penalized power-divergence test statistics for testing linear-by-linear association

Two different families of penalized power-divergence test statistics have been defined to find alternatives to the likelihood ratio and the Pearson chi-squared test statistics for testing linear- by-linear association in two-way contingency tables with empty cell(s). The exact size and power properties of these test statistics and the ordinary power-divergence test statistics have been studied based on an extensive designed simulation study. Different penalized test statistics have been proposed depending on their exact size and power performances.

Breastfeeding Self-Efficacy and the Use of Prescription Medication: A Pilot Study

Objective. To examine the association of self-efficacy, perception of milk production, and lactating women's use of medication prescribed to increase breast milk in a cohort of 18–40-year-old mothers over six months. Methods. Mothers (n = 76) attending community clinics completed the Breastfeeding Self-Efficacy Scale and the Humenick/Hill Lactation Scale, a measure of perceived milk production, three times. Results. Domperidone, a dopamine antagonist, was used by 28% of participants. On average, those using domperidone had lower self-efficacy scores than those not using it (P < 0.05) and were more likely to have used formula (Pearson chi-square test statistic = 6.87, df = 1, P < 0.05). Breastfeeding self efficacy and perception of milk production were positively correlated. Conclusion. Breastfeeding assessment conducted prior to prescription of galactogogues is recommended for mothers and healthy term babies. Following Baby-Friendly hospital protocols and increasing self-efficacy for lactating women may be most effective in sustaining breastfeeding. Risks and benefits of various galactogogues are discussed.

A new association test based on Chi-square partition for case-control GWA studies

In case-control genetic association studies, the robust procedure, Pearson's Chi-square test, is commonly used for testing association between disease status and genetic markers. However, this test does not take the possible trend of relative risks, which are due to genotype, into account. On the contrary, although Cochran-Armitage trend test with optimal scores is more powerful; it is usually difficult to assign the correct scores in advance since the true genetic model is rarely known in practice. If the unknown underlying genetic models are misspecified, the trend test may lose power dramatically. Therefore, it is desirable to find a powerful yet robust statistical test for genome-wide association studies. In this paper, we propose a new test based on the partition of Pearson's Chi-square test statistic. The new test utilizes the information of the monotonic (increasing or decreasing) trend of relative risks and therefore in general is more powerful than the Chi-square test; furthermore, it reserves the robustness. Using simulated and real single nucleotide polymorphism data, we compare the performance of the proposed test with existing methods.

An improvement to TF-IDF: Term Distribution based Term Weight Algorithm

In the process of document formalization, term weight algorithm plays an important role. It greatly interferes the precision and recall results of the natural language processing(NLP) systems. Currently, TF-IDF term weight algorithm is widely applied into language models to build NLP Systems. Since term frequency is not the only discriminator which is necessary to be considered in term weight ing and make each weight suitable to indicate the term’s importance, we are motivated to investigate other statistical characteristics of terms and found an important discriminator: term distribution. Furthermore, we found that , in a single document, a term with higher frequency and close to hypo-dispersion distribution usually contains much semantic information and should be given higher weight. One the other hand, in a document collection, the term with higher frequency and hypo-dispersion distribution usually contains less information. Based on this hypothesis, by leveraging the Pearson Chi-square Test Statistic, a Term Distribution based Local Term Weight Algorithm and Global Term Weight Algorithm are put forward respectively in this paper. Also, the experiment results at the end of this paper approve the reliability and efficiency of the algorithm s .

Notes on the Goodness-of-Fit Tests for the Ordinal Response Model

In this paper we discuss some cautionary notes in using the Pearson chi-squared test statistic for the goodness-of-fit of the ordinal response model. If a model includes continuous type explanatory variables, the resulting table from the fit of a model is not a regular one in the sense that the cell boundaries are not fixed but randomly determined by some other criteria. The chi-squared statistic from this kind of table does not have a limiting chi-square distribution in general and we need to be very cautious of the use of a chi-squared type goodness-of-fit test. We also study the limiting distribution of the chi-squared type statistic for testing the goodness-of-fit of cumulative logit models with ordinal responses. The regularity conditions necessary to the limiting distribution will be reformulated in the framework of the cumulative logit model by modifying those of Moore and Spruill (1975). Due to the complex limiting distribution, a parametric bootstrap testing procedure is a good alternative and we explained the suggested method through a practical example of an ordinal response dataset.

A Bayesian Chi‐Squared Goodness‐of‐Fit Test for Censored Data Models

We propose a Bayesian chi-squared model diagnostic for analysis of data subject to censoring. The test statistic has the form of Pearson's chi-squared test statistic and is easy to calculate from standard output of Markov chain Monte Carlo algorithms. The key innovation of this diagnostic is that it is based only on observed failure times. Because it does not rely on the imputation of failure times for observations that have been censored, we show that under heavy censoring it can have higher power for detecting model departures than a comparable test based on the complete data. In a simulation study, we show that tests based on this diagnostic exhibit comparable power and better nominal Type I error rates than a commonly used alternative test proposed by Akritas (1988, Journal of the American Statistical Association 83, 222-230). An important advantage of the proposed diagnostic is that it can be applied to a broad class of censored data models, including generalized linear models and other models with nonidentically distributed and nonadditive error structures. We illustrate the proposed model diagnostic for testing the adequacy of two parametric survival models for Space Shuttle main engine failures.

A better alternative to Wald's test-statistic for simple goodness-of-fit tests under one-stage cluster sampling

Significance levels of the popular Wald's Chi-squared statistic for simple goodness-of-fit (GOF) tests under one-stage cluster sampling are often unreliable. A large number of alternatives to Wald's GOF test with Type-I error rates more closely matching the nominal level of significance have been proposed but not yet found their way into applied statistics. Type-I error rates with Wald's test-statistic in cluster sampling from 10 actual forest cover-type maps from 5 sites and 81 sample designs are compared to the error rates of 11 alternatives. The effects of site, sampling design, evenness of cover-type class proportions, and intra-cluster correlation on Type-I error rates are quantified with logistic regressions for Wald's statistic and five promising alternatives. Our proposed second-order bias correction of Finney's [Finney, D.J., 1971. Probit Analysis, vol. 3. Cambridge University Press, p. 350] and Brier's [Brier, S.S., 1980. Analysis of contingency tables under cluster sampling. Biometrika 67, 591–596] method of moments correction of Pearson's Chi-squared test statistic emerged as the overall best alternative in this study. It was the least sensitive to design and cluster effects. Test power was investigated for the alternative simple hypothesis of equality of cover-type proportions in two site-specific maps. The proposed alternative test statistic had slightly (3%) less power than Wald's test for designs with a power of 80% or greater, yet a consistently better odds ratio of a correct test decision.

Multiple Marginal Independence Testing for Pick Any/C Variables

Many survey questions allow respondents to pick any number out of c possible categorical responses or “items”. These kinds of survey questions often use the terminology “choose all that apply” or “pick any”. Often of interest is determining if the marginal response distributions of each item differ among r different groups of respondents. Agresti and Liu (1998, 1999) call this a test for multiple marginal independence (MMI). If respondents are allowed to pick only 1 out of c responses, the hypothesis test may be performed using the Pearson chi-square test of independence. However, since respondents may pick more or less than 1 response, the test's assumptions that responses are made independently of each other is violated. Recently, a few MMI testing methods have been proposed. Loughin and Scherer (1998) propose using a bootstrap method based on a modified version of the Pearson chi-square test statistic. Agresti and Liu (1998, 1999) propose using marginal logit models, quasisymmetric loglinear models, and a few methods based on Pearson chi-square test statistics. Decady and Thomas (1999) propose using a Rao-Scott adjusted chi-squared test statistic. There has not been a full investigation of these MMI testing methods. The purpose here is to evaluate the proposed methods and propose a few new methods. Recommendations are given to guide the practitioner in choosing which MMI testing methods to use.

Pearson chi-square estimator and test for log-linear models with expected frequencies subject to linear constraints

The minimum Pearson chi-square estimator and test are defined for multinomial log-linear models with expected frequencies subject to linear constraints. The minimum Pearson chi-square estimate defined here yields predicted cell frequency estimates that, unlike the maximum likelihood estimates, minimize the popular Pearson chi-square test statistic.