Sample Standard Deviation Research Articles

A meta-analysis study on the effectiveness of a cooperative learning model on vocational high school students’ mathematics learning outcomes

Cooperative learning takes place when students work together in small groups to help each other comprehend a subject. This meta-analysis aims to determine the impact of cooperative learning on vocational high school students’ mathematics learning results compared to those that pertain to conventional learning. Data from 22 research studies, including the sample size, standard deviation, and mean for both the experimental and control groups and other information, were collected using descriptive analysis. The data analysis technique used meta-analysis on forest plots, with analytical techniques including heterogeneity testing, effect size calculation, summary effect calculation using a random-effects model, and identification of publication bias. The results showed that the effectiveness of cooperative learning was 0.89, which gave a medium effect on the mathematics learning outcomes of vocational high school students. The cooperative learning model also provides a measure of higher effectiveness in grade 11 than grade 10 and learning at a sample size of 1-30 students compared to more than 30 students. The effectiveness of cooperative learning on mathematics learning outcomes in the medium effect category, on the other hand, is dependent on grade level variables and sample size. This meta-analysis research provides information to teachers in Indonesia to implement cooperative learning based on the level and number of students in the class to improve the mathematics learning outcomes of vocational high school students.

정규모집단에서 다섯수치요약의 피셔 정보행렬에 관한 연구

In the meta-analysis, most individual research provide information in the form of {sample mean, sample standard deviation; sample size}. This is the basis for the estimate of the parameter, its standard error, confidence interval, and hypothesis test. However, in some cases, only ‘five-number summary’ information {minimum, first quartile, median, third quartile, maximum; sample size} is given. In this case, for maximum utilization of meta-information, the work of unifying information must be preceded. In most cases, it is transformed in the form {sample mean, sample standard deviation; sample size}. Several previous studies have already suggested several methods of estimating the population mean and population standard deviation from the five-number summary under the assumption of a normal population. In performance comparison studies of these methods, Lee(2022) recently proposed the maximum likelihood estimation method and showed the superiority of it, but studies on the characteristics of the standard error of the estimates were not presented. Therefore, in this study, in order to estimate the standard error of the maximum likelihood estimate(mle) based on the five-number summary, the log-likelihood function, the score function, and the Fisher information matrix(FIM) are first derived algebraically under the assumption of a normal population, and using these we create a function that computes them easily in R. Through a simulation using these, the characteristics of the standard error of the mle are identified. In addition, after estimating the FIM of the five-number summary, it was compared with the FIM of sufficient statistics and the relative efficiency was examined.

Frayer model: a strategy to improve the science vocabulary of grade 9 students of bagbag national high school

PURPOSE This study aimed to investigate the effectiveness of Frayer Model a strategy to improve the Science vocabulary of Grade 9 students of Bagbag National High School in Rosario Cavite. DESIGN/ METHODOLOGY/ APPROACH This study used Quasi – Experimental method utilizing the two-group, control group and treatment group, pre-test- post-test design. The chosen participants were sixty (60) students of Grade 9 students of Bagbag National High School. The assessment of the result was conducted through a posttest given to the participants only. T-test is used for two sample means, average means and standard deviation were used to compare the results of the pre-test and post-test. FINDINGS There was a significant difference between the post-test results of the two groups. It means that experimental group has higher test result in Science after being exposed to Frayer Model than the control group. Thus the experimental group performed better than the control group. And also there were a significant difference between the gain scores of the control group and experimental group. The gained score of experimental group were greater than the control group which means that experimental group under Frayer Model was effective. Research Limitation and Implication: Frayer Model focused only on enhancing science vocabulary through the use of graphic organizers and its impact on the achievement level of the student on the Science subject. The Researcher of the study recommends some ways in modifying the use of Frayer Model, depending on the topic needed. The whole study focused on the competencies of the second quarter. Originality/ Value: The study focused on how the Science achievement level learning outcome assessment result can be increased through Frayer Model.

Three-stage minimum risk point estimation with termination defined via Gini’s mean difference

In this article, we revisit the classic inference problem of minimum risk point estimation for an unknown normal mean when the variance also remains unknown. We propose an alternative three-stage sampling procedure with termination defined via Gini’s mean difference rather than the traditional sample standard deviation. A number of asymptotic properties are investigated both theoretically and empirically. An extensive set of simulations is conducted to demonstrate the remarkable performance of the new procedure. For practical purposes, we also include illustrations using real data sets on the number of days marigold seeds need to flower.

Tracking of Gymnast's Limb Movement Trajectory Based on MEMS Inertial Sensor.

In order to track the limb movement trajectory of gymnasts, a method based on MEMS inertial sensor is proposed. The system mainly collects the acceleration and angular velocity data of 11 positions during gymnastics by constructing sensor network. Based on the two kinds of preprocessed data, the parameters such as sample mean, standard deviation, information entropy, and mean square error are calculated as classification features, the support vector machine (SVM) classification model is established, and the movements of six kinds of gymnastics are effectively recognized. The experimental results show that when the human body is doing gymnastics, the measured three-axis acceleration values are between -0.5 g~2.2 g, -1 g~2.8 g, and -1.8 g~1 g, respectively, and the static error range accounts for only 1.6%~2% of the actual measured data range. Therefore, it is considered that such static error has little effect on the accuracy of data feature extraction and action recognition, which can be ignored. It is proved that MEMS inertial sensor can effectively track the movement trajectory of gymnasts' limbs.

Situation Analysis of Digital Literacy of Public-School Teachers in Punjab

The current study aimed at investigating the situation of digital literacy of public school teachers in Punjab. A self-prepared questionnaire was developed to examine the digital literacy of teachers in Punjab. The study focused on situation analysis of digital literacy of teachers that is helpful; to improving digital skills of teachers and analyzing the importance of digitalization and it's raising perspectives during COVID-19. The research study was carried out in 36 districts of Punjab, with 1000 teachers from the school education department participating as respondents. This was quantitative research. The results were analyzed using SPSS by independent sample t-test, ANOVA test, frequencies, percentage, means,and standard deviation. The opinion of respondents shows significant results regarding the importance of digital literacy for teachers and its rising perspectives. Through this situation analysis, the outcomes have also indicated that teachers should have good digital abilities, as the present situation of COVID-19 has changed the whole scenario of the world and digitalized education. So, it is a dire need to polish teachers' digital literacy skills in order to get effective outcomes in the realm of education.

An improved estimation method of gear fatigue strength based on sample expansion and standard deviation correction

The conventional data processing of gear fatigue staircase test is based on the Dixon-Mood method. Extreme estimates might appear under small-sample condition. To solve the aforementioned limitation, this paper proposes an improved estimated method based on standard deviation correction and sample expansion. Meanwhile, the bending fatigue test is carried out for validation. Results show that the relative error of fatigue strength between the Dixon-Mood method and the proposed method is only 0.51% with 22 samples. As the sample number reduces to 12, the maximum errors of the fatigue strength of the Dixon-Mood method and the proposed method are 12.70% and 5.32%, respectively. To control the maximum error within 10%, the sample number required by the Dixon-Mood method is 14, while the proposed method is 8. The proposed method provides a more effective gear fatigue estimation.

Unbiased Estimation of the Standard Deviation for Non-Normal Populations

The bias of the sample standard deviation as an estimator of the population standard deviation, for a simple random sample of size N from a Normal population, is well documented. Exact and approximate bias corrections appear in the literature for this case. However, there has been less discussion of the downward bias of this estimator for non-Normal populations. The appropriate bias correction depends on the kurtosis of the population distribution. We derive and illustrate an approximation for this bias, to O(N^( -1) ), for several common distributions.

Usage a Blended Learning Model to Promote the Career of Thai Massage for Health for Adult Students

This research aimed to study the implementation of a blended learning model for career promotion of Thai massage for health for adult students. The research samples comprised 34 adult students of Traditional Medical Clinic at Sirindhorn College of Public Health, Chonburi, selected by purposive sampling. The research tools were 1) a blended learning model for career promotion of Thai massage for health for adult students and 2) tools to study the outcome of implementing a blended learning model, including (1) a test on Thai massage for health, (2) an evaluation form on Thai massage for health skills, (3) an observation form of learners’ participation in learning activities through the learning model, and (4) a satisfaction assessment form. The data were analyzed using dependent sample t-test, frequency, percentage, and standard deviation. The results of the research were as follows: 1) the comparison of knowledge assessment in Thai massage for health of the learners showed that the mean of the post-test score after the learning activities were 0.05 percent significantly higher than that of the pre-test score, 2) the evaluation of Thai massage practice skills showed that learners can practice at the most correct level (90% and above) and at the correct level (80-90% and above), 3) the observation of learners participating in the activities showed that the average of the students' participation was at a high level, and 4) the sample group showed that the overall level of satisfaction was at a high level. The participants viewed that learning through the model can be further applied and promote a career at a high level.

Penalty Function Optimization in Dual Response Surfaces Based on Decision Maker’s Preference and Its Application to Real Data

The dual response surface methodology is a widely used technique in industrial engineering for simultaneously optimizing both the process mean and process standard deviation functions of the response variables. Many optimization techniques have been proposed to optimize the two fitted response surface functions that include the penalty function method (PM). The PM method has been shown to be more efficient than some existing methods. However, the drawback of the PM method is that it does not have a specific rule for determining the penalty constant; thus, in practice, practitioners will find this method difficult since it depends on subjective judgments. Moreover, in most dual response optimization methods, the sample mean and sample standard deviation of the response often use non-outlier-resistant estimators. The ordinary least squares (OLS) method is also usually used to estimate the parameters of the process mean and process standard deviation functions. Nevertheless, not many statistics practitioners are aware that the OLS procedure and the classical sample mean and sample standard deviation are easily influenced by the presence of outliers. Alternatively, instead of using those classical methods, we propose using a high breakdown and highly efficient robust MM-mean, robust MM-standard deviation, and robust MM regression estimators to overcome these shortcomings. We also propose a new optimization technique that incorporates a systematic method to determine the penalty constant. We call this method the penalty function method based on the decision maker’s (DM) preference structure in obtaining the penalty constant, denoted as PMDM. The performance of our proposed method is investigated by a Monte Carlo simulation study and real examples that employ symmetrical factorial design of experiments (DOE). The results signify that our proposed PMDM method is the most efficient method compared to the other commonly used methods in this study.

Retention and impairment of neurocognitive functions in mild cognitive impairment and Alzheimer's disease with a comprehensive neuropsychological test.

AimMATRICS Consensus Cognitive Battery was developed by the National Institute of Mental Health to establish acceptance criteria for measuring cognitive changes in schizophrenia and can be used to assess cognitive functions in other psychiatric disorders. We used a Japanese version of MATRICS Consensus Cognitive Battery to explore the changes in multiple cognitive functions in patients with mild cognitive impairment and mild Alzheimer's disease.MethodsWe administered the Japanese version of MATRICS Consensus Cognitive Battery to 11 patients with mild cognitive impairment (MCI), 11 patients with Alzheimer's disease, and 27 healthy controls. All Japanese versions of MATRICS Consensus Cognitive Battery domain scores were converted to t‐scores using sample means and standard deviations and were compared for significant performance differences among healthy control, MCI, and mild Alzheimer's disease groups.ResultsCompared with healthy controls, patients with MCI and mild Alzheimer's disease demonstrated the same degree of impairment to processing speed, verbal learning, and visual learning. Reasoning and problem‐solving showed significant impairments only in mild Alzheimer's disease. Verbal and visual abilities in working memory showed different performances in the MCI and mild Alzheimer's disease groups, with the Alzheimer's disease group demonstrating significantly more deficits in these domains. No significant difference was found among the groups in attention/vigilance and social cognition.ConclusionsThe Japanese version of MATRICS Consensus Cognitive Battery can be used to elucidate the characteristics of cognitive dysfunction of normal aging, MCI, and mild dementia in clinical practice.

The differences in physical fitness tests between males and females in the age group (12-15) years

This study aims to identify the results of students (males and females) in the King Abdullah II Award for Physical Fitness in the age group (12-15) years, as well as to the differences in these results according to the (age group, gender) variables. To achieve this, the researcher used the descriptive approach on a sample of (2410) male and female students, who achieved the first three places in the King Abdullah II Award for Physical Fitness for the year 2017/2018, and they were purposive selected. Furthermore, the study dealt with the following tests: push up test, running test (1609) m, the flexibility test, the shuttle run test (4 * 10) m and the sit-up test. In addition, to treat the study sample data, means, standard deviations, T test, Two-Way ANOVA and BONFERRONI test were used. The results of the study showed the male superiority in all the results of the King Abdullah II Award for Physical Fitness in the age group (12-15) years, with the exception of the flexibility test, which came in favor of females, as well as the superiority of students at the age of (15) years in push up, flexibility and shuttle run tests. While students at the age of (14) years excelled in the endurance test (1609). The researcher recommends the necessity of employing the media and social communication in spreading the culture of practicing sporting activities on a regular basis for both sexes.

The Learning Cycle 7-E Model Application Effect on Mathematics Critical Thinking Skills (MCTS) of Junior High School Students

This study aimed to check the differences in MCTS between students using the 7-E Learning Cycle and students using the direct learning model. An experimental type was used with a quasi-experimental approach. The sampling technique used is purposive sampling. A normality test was carried out to see the study results. A homogeneity test was conducted using the sample's standard deviation and class average. A t-test was performed to determine the differences between the two classes. Results showed differences in MCTS between students learning the 7-E Learning Cycle model and direct learning with the t-count value of 4.83, the average value of the practical class is 88 .04. The average value of the control class is 71.32. The results of this study are one of the results that show that the MCTS of students can be improved with appropriate and supportive learning models.

Career Development Anxiety and Mental Health Regulation of University Music Teachers

Mental health essentially refers to a state of mind. It means that an individual can keep a good or normal state in behavior. It is not only related to the overall development of the individual body and mind but also affects the construction of university teachers. This paper mainly studies the career development anxiety of music teachers in colleges and universities in China, analyzes its causes, and puts forward corresponding countermeasures and suggestions to alleviate the pressure and promote the physical and mental harmony of music teachers and the common growth of a good social atmosphere. The purpose is to help music teachers in colleges and universities better manage their mental health. This paper collects the data of occupational stress and mental health of 164 music teachers in many colleges and universities through the method of a questionnaire survey and processes the data with the analysis formula of sample variance and standard deviation. The analysis shows that a considerable number of the respondents have career development anxiety.

The Effect of Categorizing a Continuous Independent Variable on Explained Variance

Researchers, Particularly those in behavioral sciences, often categorize a continuous independent variable, in their research work. This is done for the main purpose of applying analysis of variance in testing their hypotheses. Such categorization is accompanied by loss of information which is reflected in a change in proportion of the variance in the dependent variable, accounted for, by the independent variable. To provide an empirical backing to this claim, a validated “teachers variables and the attainment of the goals of the Universal basic education scheme”, was administered on a random sample of 640 secondary school teachers in Ogoja Education zone of Cross River State,Nigeria. The resulting data were analyzed using simple and multiple linear regressions. The independent variables were then categorized using sample mean and standard deviation. Analysis of variance was then applied. The proportion of the variance accounted for by the independent variables, individually and collectively were then compared using Fishers Z-transformation test. The results show that differences in explained variances do exist in favour of linear regression analysis, though not significant. The implication of the findings in research design is discussed.

Second-order asymptotics for comparing treatment means from purely sequential estimation strategies under possible outlying observations

We consider two independent populations, and with all four parameters assumed unknown, and In this paper, our goal is to revisit sequential minimum risk point estimation (MRPE) problems for the parameter say) under the squared error loss (SEL) plus linear cost of sampling in our quest for comparing two treatments. We explicitly consider two separate scenarios: (i) but is assumed unknown, and (ii) are unknown and unequal. In defining the relevant purely sequential stopping rules, customarily one incorporates multiples of sample standard deviations to set up requisite boundary crossing conditions. We set out to replace the multiples of sample standard deviations used in defining requisite boundary crossing conditions with Gini’s mean difference (GMD), mean absolute deviation (MAD), along with a number of combinations of the sample standard deviations, the GMD’s, and the MAD’s. We prove that the ensuing body of such newly developed purely sequential MRPE strategies have associated second-order regret expansions and that the GMD-based as well as the MAD-based strategies are better equipped to withstand occurrences of possible outlying observations. Our theory and methodologies are amply supported by both large-scale simulations and the illustrations with real data.

Graph convolutional neural network applied to the prediction of normal boiling point

In this article, we describe training and validation of a machine learning model for the prediction of organic compound normal boiling points. Data are drawn from the experimental literature as captured in the NIST Thermodynamics Research Center (TRC) SOURCE Data Archival System. The machine learning model is based on a graph neural network approach, a methodology that has proven powerful when applied to a variety of chemical problems. Model input is extracted from a 2D sketch of the molecule, making the methodology suitable for rapid prediction of normal boiling points in a wide variety of scenarios. Our final model predicts normal boiling points within 6 K (corresponding to a mean absolute percent error of 1.32%) with sample standard deviation less than 8 K. Additionally, we found that our model robustly identifies errors in the input data set during the model training phase, thereby further motivating the utility of systematic data exploration approaches for data-related efforts.

Maternal and Fetal Outcome among Women having Pre Eclampsia with Hyperuricemia

Objective: To find out the frequency of some common maternal and fetal outcomes among women having pre eclampsia with hyperuricemia. Study design: Cross sectional study. Place and duration of study: Department of Obstetrics and Gynaecology, Hayatabad Medical Complex, Peshawar from April 2019 to Oct 2019. Patients and methods: A total of 161 women with singleton pregnancy irrespective of gravity and party, in age group ranging from 20-35 years having pre eclampsia with hyperuricemia were included in consective manner. All patients signed informed written consent for study and its outcome. Detailed history taking and examination were performed and standard lab investigations were sent. All patients were followed up till 40th day of delivery for maternal (caesarean section, eclampsia and maternal death) and fetal outcome [intrauterine fetal death, (small for gestational age) SGA and APGAR score]. Data was analyzed using SPSS version 20. Results: Maternal complications in patients of pre eclampsia with hyperuricemia were eclampsia 15.5%, caesarean section 18% and death 19.3%. Fetal complications included SGA 11.8%, intrauterine death 16.8% and APGAR score at 5 minutes 24.2%. The mean age and standard deviation of sample were 28.4 + 4.8 years, mean gravidity (2.7±1.1), mean parity (2.4±0.9) and mean BMI (24.37±2.4 kg/m2). Conclusion: Hyperuricemia in pre-eclampsia is a poor prognostic factor with increased maternal and fetal deaths, worsening of pre eclampsia to eclampsia and low APGAR scores of fetus. Key words: Pre eclampsia, hyperuricemia, maternal death, caesarean section, small for gestational age, APGAR score

An aerosol classification scheme for global simulations using the K-means machine learning method

Abstract. The K-means machine learning algorithm is applied to climatological data of seven aerosol properties from a global aerosol simulation using EMAC-MADE3. The aim is to partition the aerosol properties across the global atmosphere in specific aerosol regimes; this is done mainly for evaluation purposes. K-means is an unsupervised machine learning method with the advantage that an a priori definition of the aerosol classes is not required. Using K-means, we are able to quantitatively define global aerosol regimes, so-called aerosol clusters, and explain their internal properties and their location and extension. This analysis shows that aerosol regimes in the lower troposphere are strongly influenced by emissions. Key drivers of the clusters' internal properties and spatial distribution are, for instance, pollutants from biomass burning and biogenic sources, mineral dust, anthropogenic pollution, and corresponding mixtures. Several continental clusters propagate into oceanic regions as a result of long-range transport of air masses. The identified oceanic regimes show a higher degree of pollution in the Northern Hemisphere than over the southern oceans. With increasing altitude, the aerosol regimes propagate from emission-induced clusters in the lower troposphere to roughly zonally distributed regimes in the middle troposphere and in the tropopause region. Notably, three polluted clusters identified over Africa, India, and eastern China cover the whole atmospheric column from the lower troposphere to the tropopause region. The results of this analysis need to be interpreted taking the limitations and strengths of global aerosol models into consideration. On the one hand, global aerosol simulations cannot estimate small-scale and localized processes due to the coarse resolution. On the other hand, they capture the spatial pattern of aerosol properties on the global scale, implying that the clustering results could provide useful insights for aerosol research. To estimate the uncertainties inherent in the applied clustering method, two sensitivity tests have been conducted (i) to investigate how various data scaling procedures could affect the K-means classification and (ii) to compare K-means with another unsupervised classification algorithm (HAC, i.e. hierarchical agglomerative clustering). The results show that the standardization based on sample mean and standard deviation is the most appropriate standardization method for this study, as it keeps the underlying distribution of the raw data set and retains the information of outliers. The two clustering algorithms provide similar classification results, supporting the robustness of our conclusions. The classification procedures presented in this study have a markedly wide application potential for future model-based aerosol studies.

Croatian Use of The Hospital Survey on Patient Safety Culture

Introduction. Patient safety during hospitalisation is one of the biggest concerns for hospitals worldwide and one obligation of all medical professionals is to create a safe environment for patients and prevent accidents. Approximately 50% of adverse events can be prevented with a systematic approach. Aim. This study aimed to examine the underlying dimensions and psychometric properties of the Hospital Survey on Patient Safety Culture questionnaire in Croatian hospital settings, and to compare the results with the original questionnaire from the United States of America. Methods. The sample consisted of 438 nurses from four Croatian university hospitals. All participants signed an informed consent document, and the questionnaires were provided to the nurses in their units by the head nurses of their departments. Data analysis was performed using IBM SPSS 25.0. Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) indicator were performed with Cronbach’s alpha test and sample standard deviation. Results. All factors explain the total of 59% of variance of the measured questionnaire. Additionally, the reliability of the entire questionnaire was determined by using the internal consistency coefficient (Cronbach’s alpha) on the Hospital Survey on Patient Safety Culture with 12 of 0.88 particles, which is high internal consistency reliability. Our results show that the particles that make up the questionnaire are very homogeneous according to their object of measurement. Conclusion. The results of our study found that the survey can be applied to Croatian settings and used in hospitals. We also believe that more research on this topic is needed and is crucial for improving patient safety in hospitals. It is necessary to emphasize continuous education of nurses regarding patient safety in hospitals.