ACA App
Annals of Cardiac Anaesthesia Annals of Cardiac Anaesthesia Annals of Cardiac Anaesthesia
Home | About us | Editorial Board | Search | Ahead of print | Current Issue | Archives | Submission | Subscribe | Advertise | Contact | Login 
Users online: 932 Small font size Default font size Increase font size Print this article Email this article Bookmark this page
 


 

 
     
    Advanced search
 

 
 
     
  
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Email Alert *
    Add to My List *
* Registration required (free)  


    Abstract
   Introduction
    Measures of Freq...
    Measures of Cent...
    Computation of M...
    Measures of Disp...
    Computation of M...
    Coefficient of V...
   Conclusions
    References
    Article Figures
    Article Tables

 Article Access Statistics
    Viewed26543    
    Printed152    
    Emailed0    
    PDF Downloaded4996    
    Comments [Add]    
    Cited by others 135    

Recommend this journal

 


 
Table of Contents
ORIGINAL ARTICLE  
Year : 2019  |  Volume : 22  |  Issue : 1  |  Page : 67-72
Descriptive statistics and normality tests for statistical data


1 Department of Biostatistics and Health Informatics, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
2 Department of Haematology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
3 Department of Microbiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
4 Department of Neuro-Otology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Click here for correspondence address and email

Date of Web Publication14-Jan-2019
 

   Abstract 


Descriptive statistics are an important part of biomedical research which is used to describe the basic features of the data in the study. They provide simple summaries about the sample and the measures. Measures of the central tendency and dispersion are used to describe the quantitative data. For the continuous data, test of the normality is an important step for deciding the measures of central tendency and statistical methods for data analysis. When our data follow normal distribution, parametric tests otherwise nonparametric methods are used to compare the groups. There are different methods used to test the normality of data, including numerical and visual methods, and each method has its own advantages and disadvantages. In the present study, we have discussed the summary measures and methods used to test the normality of the data.

Keywords: Biomedical research, descriptive statistics, numerical and visual methods, test of normality

How to cite this article:
Mishra P, Pandey CM, Singh U, Gupta A, Sahu C, Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth 2019;22:67-72

How to cite this URL:
Mishra P, Pandey CM, Singh U, Gupta A, Sahu C, Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth [serial online] 2019 [cited 2021 Dec 3];22:67-72. Available from: https://www.annals.in/text.asp?2019/22/1/67/250184





   Introduction Top


A data set is a collection of the data of individual cases or subjects. Usually, it is meaningless to present such data individually because that will not produce any important conclusions. In place of individual case presentation, we present summary statistics of our data set with or without analytical form which can be easily absorbable for the audience. Statistics which is a science of collection, analysis, presentation, and interpretation of the data, have two main branches, are descriptive statistics and inferential statistics.[1]

Summary measures or summary statistics or descriptive statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Descriptive statistics are the kind of information presented in just a few words to describe the basic features of the data in a study such as the mean and standard deviation (SD).[2],[3] The another is inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors and sampling variation). In inferential statistics, most predictions are for the future and generalizations about a population by studying a smaller sample.[2],[4] To draw the inference from the study participants in terms of different groups, etc., statistical methods are used. These statistical methods have some assumptions including normality of the continuous data. There are different methods used to test the normality of data, including numerical and visual methods, and each method has its own advantages and disadvantages.[5] Descriptive statistics and inferential statistics both are employed in scientific analysis of data and are equally important in the statistics. In the present study, we have discussed the summary measures to describe the data and methods used to test the normality of the data. To understand the descriptive statistics and test of the normality of the data, an example [Table 1] with a data set of 15 patients whose mean arterial pressure (MAP) was measured are given below. Further examples related to the measures of central tendency, dispersion, and tests of normality are discussed based on the above data.
Table 1: Distribution of mean arterial pressure (mmHg) as per sex

Click here to view


Descriptive Statistics

There are three major types of descriptive statistics: Measures of frequency (frequency, percent), measures of central tendency (mean, median and mode), and measures of dispersion or variation (variance, SD, standard error, quartile, interquartile range, percentile, range, and coefficient of variation [CV]) provide simple summaries about the sample and the measures. A measure of frequency is usually used for the categorical data while others are used for quantitative data.


   Measures of Frequency Top


Frequency statistics simply count the number of times that in each variable occurs, such as the number of males and females within the sample or population. Frequency analysis is an important area of statistics that deals with the number of occurrences (frequency) and percentage. For example, according to [Table 1], out of the 15 patients, frequency of the males and females were 8 (53.3%) and 7 (46.7%), respectively.


   Measures of Central Tendency Top


Data are commonly describe the observations in a measure of central tendency, which is also called measures of central location, is used to find out the representative value of a data set. The mean, median, and mode are three types of measures of central tendency. Measures of central tendency give us one value (mean or median) for the distribution and this value represents the entire distribution. To make comparisons between two or more groups, representative values of these distributions are compared. It helps in further statistical analysis because many techniques of statistical analysis such as measures of dispersion, skewness, correlation, t-test, and ANOVA test are calculated using value of measures of central tendency. That is why measures of central tendency are also called as measures of the first order. A representative value (measures of central tendency) is considered good when it was calculated using all observations and not affected by extreme values because these values are used to calculate for further measures.


   Computation of Measures of Central Tendency Top


Mean

Mean is the mathematical average value of a set of data. Mean can be calculated using summation of the observations divided by number of observations. It is the most popular measure and very easy to calculate. It is a unique value for one group, that is, there is only one answer, which is useful when comparing between the groups. In the computation of mean, all the observations are used.[2],[5] One disadvantage with mean is that it is affected by extreme values (outliers). For example, according to [Table 2], mean MAP of the patients was 97.47 indicated that average MAP of the patients was 97.47 mmHg.
Table 2: Descriptive statistics of the mean arterial pressure (mmHg)

Click here to view


Median

The median is defined as the middle most observation if data are arranged either in increasing or decreasing order of magnitude. Thus, it is one of the observations, which occupies the central place in the distribution (data). This is also called positional average. Extreme values (outliers) do not affect the median. It is unique, that is, there is only one median of one data set which is useful when comparing between the groups. There is one disadvantage of median over mean that it is not as popular as mean.[6] For example, according to [Table 2], median MAP of the patients was 95 mmHg indicated that 50% observations of the data are either less than or equal to the 95 mmHg and rest of the 50% observations are either equal or greater than 95 mmHg.

Mode

Mode is a value that occurs most frequently in a set of observation, that is, the observation, which has maximum frequency is called mode. In a data set, it is possible to have multiple modes or no mode exists. Due to the possibility of the multiple modes for one data set, it is not used to compare between the groups. For example, according to [Table 2], maximum repeated value is 116 mmHg (2 times) rest are repeated one time only, mode of the data is 116 mmHg.


   Measures of Dispersion Top


Measures of dispersion is another measure used to show how spread out (variation) in a data set also called measures of variation. It is quantitatively degree of variation or dispersion of values in a population or in a sample. More specifically, it is showing lack of representation of measures of central tendency usually for mean/median. These are indices that give us an idea about homogeneity or heterogeneity of the data.[2],[6]

Common measures

Variance, SD, standard error, quartile, interquartile range, percentile, range, and CV.


   Computation of Measures of Dispersion Top


Standard deviation and variance

The SD is a measure of how spread out values is from its mean value. Its symbol is σ (the Greek letter sigma) or s. It is called SD because we have taken a standard value (mean) to measures the dispersion. Where xi is individual value, x̄ is mean value. If sample size is <30, we use “n-1” in denominator, for sample size ≥30, use “n” in denominator. The variance (s2) is defined as the average of the squared difference from the mean. It is equal to the square of the SD (s).



For example, in the above, SD is 11.01 mmHg When n<30 which showed that approximate average deviation between mean value and individual values is 11.01. Similarly, variance is 121.22 [i.e., (11.01)2], which showed that average square deviation between mean value and individual values is 121.22 [Table 2].

Standard error

Standard error is the approximate difference between sample mean and population mean. When we draw the many samples from same population with same sample size through random sampling technique, then SD among the sample means is called standard error. If sample SD and sample size are given, we can calculate standard error for this sample, by using the formula.

Standard error = sample SD/√sample size.

For example, according to [Table 2], standard error is 2.84 mmHg, which showed that average mean difference between sample means and population mean is 2.84 mmHg [Table 2].

Quartiles and interquartile range

The quartiles are the three points that divide the data set into four equal groups, each group comprising a quarter of the data, for a set of data values which are arranged in either ascending or descending order. Q1, Q2, and Q3 are represent the first, second, and third quartile's value.[7]

For ith Quartile = [i * (n + 1)/4]th observation, where i = 1, 2, 3.

For example, in the above, first quartile (Q1) = (n + 1)/4= (15 + 1)/4 = 4th observation from initial = 88 mmHg (i.e., first 25% number of observations of the data are either ≤88 and rest 75% observations are either ≥88), Q2 (also called median) = [2* (n + 1)/4] = 8th observation from initial = 95 mmHg, that is, first 50% number of observations of the data are either less or equal to the 95 and rest 50% observations are either ≥95, and similarly Q3 = [3* (n + 1)/4] = 12th observation from initial = 107 mmHg, i.e., indicated that first 75% number of observations of the data are either ≤107 and rest 25% observations are either ≥107. The interquartile range (IQR) is a measure of variability, also called the midspread or middle 50%, which is a measure of statistical dispersion, being equal to the difference between 75th (Q3 or third quartile) and 25th (Q1 or first quartile) percentiles. For example, in the above example, three quartiles, that is, Q1, Q2, and Q3 are 88, 95, and 107, respectively. As the first and third quartile in the data is 88 and 107. Hence, IQR of the data is 19 mmHg (also can write like: 88–107) [Table 2].



Percentile

The percentiles are the 99 points that divide the data set into 100 equal groups, each group comprising a 1% of the data, for a set of data values which are arranged in either ascending or descending order. About 25% percentile is the first quartile, 50% percentile is the second quartile also called median value, while 75% percentile is the third quartile of the data.

For ith percentile = [i * (n + 1)/100]th observation, where i = 1, 2, 3.,99.

Example: In the above, 10th percentile = [10* (n + 1)/100] =1.6th observation from initial which is fall between the first and second observation from the initial = 1st observation + 0.6* (difference between the second and first observation) = 83.20 mmHg, which indicated that 10% of the data are either ≤83.20 and rest 90% observations are either ≥83.20.




   Coefficient of Variation Top


Interpretation of SD without considering the magnitude of mean of the sample or population may be misleading. To overcome this problem, CV gives an idea. CV gives the result in terms of ratio of SD with respect to its mean value, which expressed in %. CV = 100 × (SD/mean). For example, in the above, coefficient of the variation is 11.3% which indicated that SD is 11.3% of its mean value [i.e., 100* (11.01/97.47)] [Table 2].

Range

Difference between largest and smallest observation is called range. If A and B are smallest and largest observations in a data set, then the range (R) is equal to the difference of largest and smallest observation,th at is, R = A−B.

For example, in the above, minimum and maximum observation in the data is 82 mmHg and 116 mmHg. Hence, the range of the data is 34 mmHg (also can write like: 82–116) [Table 2].

Descriptive statistics can be calculated in the statistical software “SPSS” (analyze → descriptive statistics → frequencies or descriptives.

Normality of data and testing

The standard normal distribution is the most important continuous probability distribution has a bell-shaped density curve described by its mean and SD and extreme values in the data set have no significant impact on the mean value. If a continuous data is follow normal distribution then 68.2%, 95.4%, and 99.7% observations are lie between mean ± 1 SD, mean ± 2 SD, and mean ± 3 SD, respectively.[2],[4]

Why to test the normality of data

Various statistical methods used for data analysis make assumptions about normality, including correlation, regression, t-tests, and analysis of variance. Central limit theorem states that when sample size has 100 or more observations, violation of the normality is not a major issue.[5],[8] Although for meaningful conclusions, assumption of the normality should be followed irrespective of the sample size. If a continuous data follow normal distribution, then we present this data in mean value. Further, this mean value is used to compare between/among the groups to calculate the significance level (P value). If our data are not normally distributed, resultant mean is not a representative value of our data. A wrong selection of the representative value of a data set and further calculated significance level using this representative value might give wrong interpretation.[9] That is why, first we test the normality of the data, then we decide whether mean is applicable as representative value of the data or not. If applicable, then means are compared using parametric test otherwise medians are used to compare the groups, using nonparametric methods.

Methods used for test of normality of data

An assessment of the normality of data is a prerequisite for many statistical tests because normal data is an underlying assumption in parametric testing. There are two main methods of assessing normality: Graphical and numerical (including statistical tests).[3],[4] Statistical tests have the advantage of making an objective judgment of normality but have the disadvantage of sometimes not being sensitive enough at low sample sizes or overly sensitive to large sample sizes. Graphical interpretation has the advantage of allowing good judgment to assess normality in situations when numerical tests might be over or undersensitive. Although normality assessment using graphical methods need a great deal of the experience to avoid the wrong interpretations. If we do not have a good experience, it is the best to rely on the numerical methods.[10] There are various methods available to test the normality of the continuous data, out of them, most popular methods are Shapiro–Wilk test, Kolmogorov–Smirnov test, skewness, kurtosis, histogram, box plot, P–P Plot, Q–Q Plot, and mean with SD. The two well-known tests of normality, namely, the Kolmogorov–Smirnov test and the Shapiro–Wilk test are most widely used methods to test the normality of the data. Normality tests can be conducted in the statistical software “SPSS” (analyze → descriptive statistics → explore → plots → normality plots with tests).

The Shapiro–Wilk test is more appropriate method for small sample sizes (<50 samples) although it can also be handling on larger sample size while Kolmogorov–Smirnov test is used for n ≥50. For both of the above tests, null hypothesis states that data are taken from normal distributed population. When P > 0.05, null hypothesis accepted and data are called as normally distributed. Skewness is a measure of symmetry, or more precisely, the lack of symmetry of the normal distribution. Kurtosis is a measure of the peakedness of a distribution. The original kurtosis value is sometimes called kurtosis (proper). Most of the statistical packages such as SPSS provide “excess” kurtosis (also called kurtosis [excess]) obtained by subtracting 3 from the kurtosis (proper). A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. If mean, median, and mode of a distribution coincide, then it is called a symmetric distribution, that is, skewness = 0, kurtosis (excess) = 0. A distribution is called approximate normal if skewness or kurtosis (excess) of the data are between − 1 and + 1. Although this is a less reliable method in the small-to-moderate sample size (i.e., n <300) because it can not adjust the standard error (as the sample size increases, the standard error decreases). To overcome this problem, a z-test is applied for normality test using skewness and kurtosis. A Z score could be obtained by dividing the skewness values or excess kurtosis value by their standard errors. For small sample size (n <50), z value ± 1.96 are sufficient to establish normality of the data.[8] However, medium-sized samples (50≤ n <300), at absolute z-value ± 3.29, conclude the distribution of the sample is normal.[11] For sample size >300, normality of the data is depend on the histograms and the absolute values of skewness and kurtosis. Either an absolute skewness value ≤2 or an absolute kurtosis (excess) ≤4 may be used as reference values for determining considerable normality.[11] A histogram is an estimate of the probability distribution of a continuous variable. If the graph is approximately bell-shaped and symmetric about the mean, we can assume normally distributed data[12],[13] [Figure 1]. In statistics, a Q–Q plot is a scatterplot created by plotting two sets of quantiles (observed and expected) against one another. For normally distributed data, observed data are approximate to the expected data, that is, they are statistically equal [Figure 2]. A P–P plot (probability–probability plot or percent–percent plot) is a graphical technique for assessing how closely two data sets (observed and expected) agree. It forms an approximate straight line when data are normally distributed. Departures from this straight line indicate departures from normality [Figure 3]. Box plot is another way to assess the normality of the data. It shows the median as a horizontal line inside the box and the IQR (range between the first and third quartile) as the length of the box. The whiskers (line extending from the top and bottom of the box) represent the minimum and maximum values when they are within 1.5 times the IQR from either end of the box (i.e., Q1 − 1.5* IQR and Q3 + 1.5* IQR). Scores >1.5 times and 3 times the IQR are out of the box plot and are considered as outliers and extreme outliers, respectively. A box plot that is symmetric with the median line at approximately the center of the box and with symmetric whiskers indicate that the data may have come from a normal distribution. In case many outliers are present in our data set, either outliers are need to remove or data should treat as nonnormally distributed[8],[13],[14] [Figure 4]. Another method of normality of the data is relative value of the SD with respect to mean. If SD is less than half mean (i.e., CV <50%), data are considered normal.[15] This is the quick method to test the normality. However this method should only be used when our sample size is at least 50.
Figure 1: Histogram showing the distribution of the mean arterial pressure

Click here to view
Figure 2: Normal Q–Q Plot showing correlation between observed and expected values of the mean arterial pressure

Click here to view
Figure 3: Normal P–P Plot showing correlation between observed and expected cumulative probability of the mean arterial pressure

Click here to view
Figure 4: Boxplot showing distribution of the mean arterial pressure

Click here to view


For example in [Table 1], data of MAP of the 15 patients are given. Normality of the above data was assessed. Result showed that data were normally distributed as skewness (0.398) and kurtosis (−0.825) individually were within ±1. Critical ratio (Z value) of the skewness (0.686) and kurtosis (−0.737) were within ±1.96, also evident to normally distributed. Similarly, Shapiro–Wilk test (P = 0.454) and Kolmogorov–Smirnov test (P = 0.200) were statistically insignificant, that is, data were considered normally distributed. As sample size is <50, we have to take Shapiro–Wilk test result and Kolmogorov–Smirnov test result must be avoided, although both methods indicated that data were normally distributed. As SD of the MAP was less than half mean value (11.01 <48.73), data were considered normally distributed, although due to sample size <50, we should avoid this method because it should use when our sample size is at least 50 [Table 2] and [Table 3].
Table 3: Skewness, kurtosis, and normality tests for mean arterial pressure (mmHg)

Click here to view



   Conclusions Top


Descriptive statistics are a statistical method to summarizing data in a valid and meaningful way. A good and appropriate measure is important not only for data but also for statistical methods used for hypothesis testing. For continuous data, testing of normality is very important because based on the normality status, measures of central tendency, dispersion, and selection of parametric/nonparametric test are decided. Although there are various methods for normality testing but for small sample size (n <50), Shapiro–Wilk test should be used as it has more power to detect the nonnormality and this is the most popular and widely used method. When our sample size (n) is at least 50, any other methods (Kolmogorov–Smirnov test, skewness, kurtosis, z value of the skewness and kurtosis, histogram, box plot, P–P Plot, Q–Q Plot, and SD with respect to mean) can be used to test of the normality of continuous data.

Acknowledgment

The authors would like to express their deep and sincere gratitude to Dr. Prabhat Tiwari, Professor, Department of Anaesthesiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, for his critical comments and useful suggestions that was very much useful to improve the quality of this manuscript.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Lund Research Ltd. Descriptive and Inferential Statistics. Available from: http://www.statistics.laerd.com. [Last accessed on 2018 Aug 02].  Back to cited text no. 1
    
2.
Sundaram KR, Dwivedi SN, Sreenivas V. Medical Statistics: Principles and Methods. 2nd ed. New Delhi: Wolters Kluwer India; 2014.  Back to cited text no. 2
    
3.
Bland M. An Introduction to Medical Statistics. 4th ed. Oxford: Oxford University Press; 2015.  Back to cited text no. 3
    
4.
Campbell MJ, Machin D, Walters SJ. Medical Statistics: A text book for the health sciences, 4th ed. Chichester: John Wiley & Sons, Ltd.; 2007.  Back to cited text no. 4
    
5.
Altman DG, Bland JM. Statistics notes: The normal distribution. BMJ 1995;310:298.  Back to cited text no. 5
    
6.
Altman DG. Practical Statistics for Medical Research Chapman and Hall/CRC Texts in Statistical Science. London: CRC Press; 1999.  Back to cited text no. 6
    
7.
Indrayan A, Sarmukaddam SB. Medical Bio-Statistics. New York: Marcel Dekker Inc.; 2000.  Back to cited text no. 7
    
8.
Ghasemi A, Zahediasl S. Normality tests for statistical analysis: A guide for non-statisticians. Int J Endocrinol Metab 2012;10:486-9.  Back to cited text no. 8
    
9.
Indrayan A, Satyanarayana L. Essentials of biostatistics. Indian Pediatr 1999;36:1127-34.  Back to cited text no. 9
    
10.
Lund Research Ltd. Testing for Normality using SPSS Statistics. Available from: http://www.statistics.laerd.com. [Last accessed 2018 Aug 02].  Back to cited text no. 10
    
11.
Kim HY. Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restor Dent Endod 2013;38:52-4.  Back to cited text no. 11
    
12.
Armitage P, Berry G. Statistical Methods in Medical Research. 2nd ed. London: Blackwell Scientific Publications; 1987.  Back to cited text no. 12
    
13.
Barton B, Peat J. Medical Statistics: A Guide to SPSS, Data Analysis and Clinical Appraisal. 2nd ed. Sydney: Wiley Blackwell, BMJ Books; 2014.  Back to cited text no. 13
    
14.
Baghban AA, Younespour S, Jambarsang S, Yousefi M, Zayeri F, Jalilian FA. How to test normality distribution for a variable: A real example and a simulation study. J Paramed Sci 2013;4:73-7.  Back to cited text no. 14
    
15.
Jeyaseelan L. Short Training Course Materials on Fundamentals of Biostatistics, Principles of Epidemiology and SPSS. CMC Vellore: Biostatistics Resource and Training Center (BRTC); 2007.  Back to cited text no. 15
    

Top
Correspondence Address:
Anshul Gupta
Department of Haematology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow - 226 014, Uttar Pradesh
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/aca.ACA_157_18

Rights and Permissions


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]

This article has been cited by
1 Zeolite increases grain yield and potassium balance in paddy fields
Yinghao Li, Guimin Xia, Qi Wu, Wei Chen, Wenhua Lin, Zhongxiao Zhang, Yinglong Chen, Taotao Chen, Kadambot H.M. Siddique, Daocai Chi
Geoderma. 2022; 405: 115397
[Pubmed] | [DOI]
2 Testing the reliability and effectiveness of a new tool for assessing urban blue spaces: The BlueHealth environmental assessment tool (BEAT)
Himansu Sekhar Mishra, Simon Bell, James Grellier, Mathew P. White
Health & Place. 2021; 68: 102526
[Pubmed] | [DOI]
3 Moderating effects of personal innovativeness and driving experience on factors influencing adoption of BEVs in Malaysia: An integrated SEM–BSEM approach
Hamed Khazaei, Mohammad Ali Tareq
Heliyon. 2021; 7(9): e08072
[Pubmed] | [DOI]
4 The motivations of visiting upscale restaurants during the COVID-19 pandemic: The role of risk perception and trust in government
Bekir Bora Dedeoglu, Erhan Bogan
International Journal of Hospitality Management. 2021; 95: 102905
[Pubmed] | [DOI]
5 Development and validation of a predictive model of exclusive breastfeeding at hospital discharge: Retrospective cohort study
Ana Ballesta-Castillejos, Juan Gómez-Salgado, Julián Rodríguez-Almagro, Antonio Hernández-Martínez
International Journal of Nursing Studies. 2021; 117: 103898
[Pubmed] | [DOI]
6 Comment on gallbladder polyps: Correlation of size and clinicopathologic characteristics based on updated definitions
Sami Akbulut, Tevfik Tolga Sahin
International Journal of Surgery Case Reports. 2021; 83: 105947
[Pubmed] | [DOI]
7 Evidence of declining trees resilience under long term heavy metal stress combined with climate change heating
Constantin Nechita, Andreea Maria Iordache, Karel Lemr, Tom Levanic, Tomas Pluhacek
Journal of Cleaner Production. 2021; 317: 128428
[Pubmed] | [DOI]
8 Effect of customer involvement on co-creation of services: A moderated mediation model
Millissa F.Y. Cheung, W.M. To
Journal of Retailing and Consumer Services. 2021; 63: 102660
[Pubmed] | [DOI]
9 Item analysis of multiple choice questions: A quality assurance test for an assessment tool
Dharmendra Kumar, Raksha Jaipurkar, Atul Shekhar, Gaurav Sikri, V. Srinivas
Medical Journal Armed Forces India. 2021; 77: S85
[Pubmed] | [DOI]
10 Letter to the editor “Effect of a brief progressive resistance training program in hospital porters on pain, work ability and physical function”
Prachi Maheshwari, Shilpasree Saha, Hina Vaish
Musculoskeletal Science and Practice. 2021; 51: 102263
[Pubmed] | [DOI]
11 Chest CT severity score and radiological patterns as predictors of disease severity, ICU admission, and viral positivity in COVID-19 patients
Ioannis Bellos, Kyriaki Tavernaraki, Konstantinos Stefanidis, Olympia Michalopoulou, Giota Lourida, Eleni Korompoki, Ioanna Thanou, Loukas Thanos, Angelos Pefanis, Aikaterini Argyraki
Respiratory Investigation. 2021; 59(4): 436
[Pubmed] | [DOI]
12 Kidney biopsy findings in vancomycin-induced acute kidney injury: a pooled analysis
Ioannis Bellos, Vasilios Pergialiotis, Despina N. Perrea
International Urology and Nephrology. 2021;
[Pubmed] | [DOI]
13 Industrial growth and CO2 emissions in Vietnam: the key role of financial development and fossil fuel consumption
Kishwar Ali, Satar Bakhsh, Saif Ullah, Atta Ullah, Sami Ullah
Environmental Science and Pollution Research. 2021; 28(6): 7515
[Pubmed] | [DOI]
14 Jaws of Platynereis dumerilii: Miniature Biogenic Structures with Hardness Properties Similar to Those of Crystalline Metals
Luis Zelaya-Lainez, Giuseppe Balduzzi, Olaf Lahayne, Kyojiro N. Ikeda, Florian Raible, Christopher Herzig, Winfried Nischkauer, Andreas Limbeck, Christian Hellmich
JOM. 2021; 73(8): 2390
[Pubmed] | [DOI]
15 Basics of Statistical Comparisons
Amir Maroof Khan, Ashish Goel
Indian Pediatrics. 2021; 58(10): 987
[Pubmed] | [DOI]
16 Drunkorexia behaviors and motives, eating attitudes and mental health in Lebanese alcohol drinkers: a path analysis model
Diana Malaeb, Dora Bianchi, Sara Pompili, Jana Berro, Fiorenzo Laghi, Vanessa Azzi, Marwan Akel, Sahar Obeid, Souheil Hallit
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity. 2021;
[Pubmed] | [DOI]
17 Political marketing and social media influence on young voters in Ghana
Justice Boateng Dankwah, Kobby Mensah
SN Social Sciences. 2021; 1(6)
[Pubmed] | [DOI]
18 Statistical Methods in Experimental Pathology
Douglas A. Mata, Danny A. Milner
The American Journal of Pathology. 2021; 191(5): 784
[Pubmed] | [DOI]
19 Hydrological alteration induced changes on macrophyte community composition in sub-tropical floodplain wetlands of Nepal
Tika Regmi, Deep Narayan Shah, Tanya M. Doody, Susan M. Cuddy, Ram Devi Tachamo Shah
Aquatic Botany. 2021; 173: 103413
[Pubmed] | [DOI]
20 Work-related stress, psychophysiological strain, and recovery among on-site construction personnel
Janet M. Nwaogu, Albert P.C. Chan
Automation in Construction. 2021; 125: 103629
[Pubmed] | [DOI]
21 Promoting critical thinking in an online, project-based course
Catalina Cortázar, Miguel Nussbaum, Jorge Harcha, Danilo Alvares, Felipe López, Julián Goñi, Verónica Cabezas
Computers in Human Behavior. 2021; 119: 106705
[Pubmed] | [DOI]
22 Study of cobalt doping control via various routes in thoroughbred horses
Young Beom Kwak, Jundong Yu, Eo Jin Im, Bok Son Jeong, Hye Hyun Yoo
Drug Testing and Analysis. 2021;
[Pubmed] | [DOI]
23 Obesity and long-term outcomes after incident stroke: a prospective population-based cohort study
Ralph K. Akyea, Wolfram Doehner, Barbara Iyen, Stephen F. Weng, Nadeem Qureshi, George Ntaios
Journal of Cachexia, Sarcopenia and Muscle. 2021;
[Pubmed] | [DOI]
24 Statistics decrypted—a comprehensive review and smartphone-assisted five-step approach for good statistical practice
Laura C. Guglielmetti, Fabio Faber-Castell, Lukas Fink, Raphael N. Vuille-dit-Bille
Langenbeck's Archives of Surgery. 2021;
[Pubmed] | [DOI]
25 New evidence from hyperspectral imaging analysis on the effect of photobiomodulation therapy on normal skin oxygenation
Mihaela Antonina Calin, Adrian Macovei, Roxana Savastru, Adriana Sarah Nica, Sorin Viorel Parasca
Lasers in Medical Science. 2021;
[Pubmed] | [DOI]
26 Measurement Properties of the Work Ability Score in Sick-Listed Workers with Chronic Musculoskeletal Pain
M. Stienstra, M. J. A. Edelaar, B. Fritz, M. F. Reneman
Journal of Occupational Rehabilitation. 2021;
[Pubmed] | [DOI]
27 Energy consumption prediction by using machine learning for smart building: Case study in Malaysia
Mel Keytingan M. Shapi, Nor Azuana Ramli, Lilik J. Awalin
Developments in the Built Environment. 2021; 5: 100037
[Pubmed] | [DOI]
28 Maternal and perinatal outcomes in pregnant women infected by SARS-CoV-2: A meta-analysis
Ioannis Bellos, Aakash Pandita, Raffaella Panza
European Journal of Obstetrics & Gynecology and Reproductive Biology. 2021; 256: 194
[Pubmed] | [DOI]
29 Drivers of food waste reduction behaviour in the household context
Saman Attiq, Muhammad Danish Habib, Puneet Kaur, Muhammad Junaid Shahid Hasni, Amandeep Dhir
Food Quality and Preference. 2021; 94: 104300
[Pubmed] | [DOI]
30 Microbial dynamics in rearing trials of Hermetia illucens larvae fed coffee silverskin and microalgae
Andrea Osimani, Ilario Ferrocino, Maria Rita Corvaglia, Andrea Roncolini, Vesna Milanovic, Cristiana Garofalo, Lucia Aquilanti, Paola Riolo, Sara Ruschioni, Elham Jamshidi, Nunzio Isidoro, Matteo Zarantoniello, Luca Cocolin, Ike Olivotto, Francesca Clementi
Food Research International. 2021; 140: 110028
[Pubmed] | [DOI]
31 Development of shiny dashboard application for “genome-wide association study on analysis of SNPs injected in Homo sapiens genome (snips-HsG)”
Balamurugan Sivaprakasam, Prasanna Sadagopan
Gene Reports. 2021; 23: 101033
[Pubmed] | [DOI]
32 Características y tendencias de la mortalidad por cáncer de ojo y anexos en Chile
Blas Vargas
Revista Médica Clínica Las Condes. 2021; 32(4): 511
[Pubmed] | [DOI]
33 Predicting the physical activity of new parents who participated in a physical activity intervention
Ryan E. Rhodes, Mark R. Beauchamp, Alison Quinlan, Danielle Symons Downs, Darren E.R. Warburton, Chris M. Blanchard
Social Science & Medicine. 2021; 284: 114221
[Pubmed] | [DOI]
34 Changes in young adult substance use during COVID-19 as a function of ACEs, depression, prior substance use and resilience
Katelyn F. Romm, Brooke Patterson, Natalie D. Crawford, Heather Posner, Carly D. West, DeEnna Wedding, Kimberly Horn, Carla J. Berg
Substance Abuse. 2021; : 1
[Pubmed] | [DOI]
35 Letter to Editor ‘Effect of moving cupping therapy on hip and knee range of movement and knee flexion power: a preliminary investigation’
Diksha Bains, Adarsh Kumar Srivastav
Journal of Manual & Manipulative Therapy. 2021; 29(3): 196
[Pubmed] | [DOI]
36 Analysing Gray Cast Iron Data using a New Shapiro-Wilks test for Normality under Indeterminacy
Muhammad Aslam
International Journal of Cast Metals Research. 2021; 34(1): 1
[Pubmed] | [DOI]
37 Trends in COSI responses associated with age and degree of hearing loss
Richard Windle
International Journal of Audiology. 2021; : 1
[Pubmed] | [DOI]
38 Dehydration and Rapid Weight Gain Between Weigh-in and Competition in Judo Athletes: The Differences between Women and Men
Bayram Ceylan, Sukru Serdar Balci
Research in Sports Medicine. 2021; : 1
[Pubmed] | [DOI]
39 Cogstate Brief Battery: Cognition and the feigning of cognitive impairment in chronic pain
Tamar Lupu, Yoram Braw, Yaron Sacher, Motti Ratmansky
Applied Neuropsychology: Adult. 2021; : 1
[Pubmed] | [DOI]
40 Understanding mediators and moderators of the effect of customer satisfaction on loyalty
Lovemore Chikazhe, Charles Makanyeza, Blessing Chigunhah, Morteza Akbari
Cogent Business & Management. 2021; 8(1): 1922127
[Pubmed] | [DOI]
41 DNAku Consumers Profile: One of The First Direct to Customer Genetics Testing in Indonesia
Deby Erina Parung, Kians Azizatikarna, Dian Amirulloh, Erlin Listiyaningsih, Bharuno Mahesworo, Arif Budiarto, Simon, Bens Pardamean
IOP Conference Series: Earth and Environmental Science. 2021; 794(1): 012117
[Pubmed] | [DOI]
42 Optimization of the rapid carbapenem inactivation method for use with AmpC hyperproducers
Madalina Maria Muntean, Andrei-Alexandru Muntean, François Guerin, Vincent Cattoir, Elodie Creton, Garance Cotellon, Saoussen Oueslati, Mircea Ioan Popa, Delphine Girlich, Bogdan I. Iorga, Rémy A. Bonnin, Thierry Naas
Journal of Antimicrobial Chemotherapy. 2021; 76(9): 2294
[Pubmed] | [DOI]
43 Treating top management team conflicts through employee voice for reducing intentions to quit: moderating role of union instrumentality
Naveed Iqbal Chaudhry, Muhammad Azam Roomi, Marium Eugien, Javed Iqbal Chaudhry
International Journal of Conflict Management. 2021; ahead-of-p(ahead-of-p)
[Pubmed] | [DOI]
44 Synchronous online flip learning with formative gamification quiz: instruction during COVID-19
Zamzami Zainuddin, Ratna Farida, Cut Muftia Keumala, Rudi Kurniawan, Hadi Iskandar
Interactive Technology and Smart Education. 2021; ahead-of-p(ahead-of-p)
[Pubmed] | [DOI]
45 Entrepreneurial alertness and social entrepreneurial venture creation: the mediating role of personal initiative
Isa Nsereko, Juma Wasswa Balunywa, Lawrence Musiitwa Kyazze, Hamidah Babirye Nsereko, Jamidah Nakato
Journal of Enterprising Communities: People and Places in the Global Economy. 2021; ahead-of-p(ahead-of-p)
[Pubmed] | [DOI]
46 Conditional resource and social entrepreneurial action: the mediating role of social entrepreneurial intent
Isa Nsereko
Journal of Entrepreneurship in Emerging Economies. 2021; 13(5): 1057
[Pubmed] | [DOI]
47 Radiographic assessment of carpal conformation in the horse: Technique development and validation of the consistency of measurements
Timothy A. O. Olusa, Sa'ad M. Y. Ismail, Christina M. Murray, Helen M. S. Davies
Anatomia, Histologia, Embryologia. 2021; 50(2): 284
[Pubmed] | [DOI]
48 Development of a novel risk score for the prediction of critical illness amongst COVID-19 patients
Ioannis Bellos, Panagiota Lourida, Aikaterini Argyraki, Eleni Korompoki, Christina Zirou, Ioanna Kokkinaki, Angelos Pefanis
International Journal of Clinical Practice. 2021; 75(4)
[Pubmed] | [DOI]
49 Nomophobia and temperaments in Lebanon: Results of a national study
Clara Rahme, Rabih Hallit, Marwan Akel, Clarissa Chalhoub, Maria Hachem, Souheil Hallit, Sahar Obeid
Perspectives in Psychiatric Care. 2021;
[Pubmed] | [DOI]
50 Exploring the Elements Influencing the Behavioral Adoption of E-Commerce by Chinese Small and Medium Enterprises (SMEs)
Isaac Kofi Mensah, Rui Wang, Lin Gui, Jinxuan Wang
Information Development. 2021; : 0266666921
[Pubmed] | [DOI]
51 Height Prediction Using the Knee Height Measurement Among Indonesian Children
Fernando Rumapea, Eddy Fadlyana, Meita Dhamayanti, Rodman Tarigan, Rahmayani Rahmayani, Kusnandi Rusmil
Food and Nutrition Bulletin. 2021; 42(2): 247
[Pubmed] | [DOI]
52 Caregiver burden of families of children with corrosive esophageal injuries
Nazife G Özer Özlü, Fatma Vural
Journal of Child Health Care. 2021; : 1367493521
[Pubmed] | [DOI]
53 Clinical and genetic factors associated with anxiety and depression in breast cancer patients: a cross-sectional study
Aline HAJJ, Roula HACHEM, Rita KHOURY, Souheil HALLIT, Bashar ElJEBBAWI, Fady NASR, Fadi EL KARAK, Georges CHAHINE, Joseph KATTAN, Lydia RABBAA KHABBAZ
BMC Cancer. 2021; 21(1)
[Pubmed] | [DOI]
54 Restrained eating in Lebanese adolescents: scale validation and correlates
Tracy Boulos Nakhoul, Anthony Mina, Michel Soufia, Sahar Obeid, Souheil Hallit
BMC Pediatrics. 2021; 21(1)
[Pubmed] | [DOI]
55 Sleep disorders in a sample of Lebanese children: the role of parental mental health and child nutrition and activity
Elsa Sfeir, Chadia Haddad, Marwan Akel, Souheil Hallit, Sahar Obeid
BMC Pediatrics. 2021; 21(1)
[Pubmed] | [DOI]
56 Premenstrual dysphoric disorder and childhood maltreatment, adulthood stressful life events and depression among Lebanese university students: a structural equation modeling approach
Yorgo Younes, Souheil Hallit, Sahar Obeid
BMC Psychiatry. 2021; 21(1)
[Pubmed] | [DOI]
57 Association between cumulative cigarette and Waterpipe smoking and symptoms of dependence in Lebanese adults
Diana Malaeb, Marwan Akel, Hala Sacre, Chadia Haddad, Sahar Obeid, Souheil Hallit, Pascale Salameh
BMC Public Health. 2021; 21(1)
[Pubmed] | [DOI]
58 Exploring safety culture in the Finnish ambulance service with Emergency Medical Services Safety Attitudes Questionnaire
Anu Venesoja, Veronica Lindström, Pasi Aronen, Maaret Castrén, Susanna Tella
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. 2021; 29(1)
[Pubmed] | [DOI]
59 Evaluation of a mosquito home system for controlling Aedes aegypti
Ahmad Mohiddin Mohd Ngesom, Anis Ahmad Razi, Nur Syahirah Azizan, Nazni Wasi Ahmad, Asmalia Md Lasim, Yanfeng Liang, David Greenhalgh, Jasmine Chia Siew Min, Mazrura Sahani, Rozita Hod, Hidayatulfathi Othman
Parasites & Vectors. 2021; 14(1)
[Pubmed] | [DOI]
60 Validation of the Arabic version of the Dusseldorf Orthorexia Scale (DOS) among Lebanese adolescents
Radoslaw Rogoza, Souheil Hallit, Michel Soufia, Friederike Barthels, Sahar Obeid
Journal of Eating Disorders. 2021; 9(1)
[Pubmed] | [DOI]
61 Phubbing and temperaments among young Lebanese adults: the mediating effect of self-esteem and emotional intelligence
Zeinab Bitar, Souheil Hallit, Wael Khansa, Sahar Obeid
BMC Psychology. 2021; 9(1)
[Pubmed] | [DOI]
62 Problematic smartphone use and affective temperaments among Lebanese young adults: scale validation and mediating role of self-esteem
Joanne Zeidan, Souheil Hallit, Marwan Akel, Ismail Louragli, Sahar Obeid
BMC Psychology. 2021; 9(1)
[Pubmed] | [DOI]
63 Cyberchondria severity and quality of life among Lebanese adults: the mediating role of fear of COVID-19, depression, anxiety, stress and obsessive–compulsive behavior—a structural equation model approach
Clara Rahme, Marwan Akel, Sahar Obeid, Souheil Hallit
BMC Psychology. 2021; 9(1)
[Pubmed] | [DOI]
64 Efficient neural spike sorting using data subdivision and unification
Masood Ul Hassan, Rakesh Veerabhadrappa, Asim Bhatti, Alexandros Iosifidis
PLOS ONE. 2021; 16(2): e0245589
[Pubmed] | [DOI]
65 Does curve pattern impact on the effects of physiotherapeutic scoliosis specific exercises on Cobb angles of participants with adolescent idiopathic scoliosis: A prospective clinical trial with two years follow-up
Yunli Fan, Michael K. T. To, Eric H. K. Yeung, Jianbin Wu, Rong He, Zhuoman Xu, Ruiwen Zhang, Guangshuo Li, Kenneth M. C. Cheung, Jason P. Y. Cheung, Alison Rushton
PLOS ONE. 2021; 16(1): e0245829
[Pubmed] | [DOI]
66 Comparison of the disinfecting effect of sodium hypochlorite aqueous solution and surfactant on hospital kitchen hygiene using adenosine triphosphate swab testing
Takashi Aoyama, Tomoko Kudo, Hans-Uwe Dahms
PLOS ONE. 2021; 16(4): e0249796
[Pubmed] | [DOI]
67 Orthorexia nervosa and disordered eating attitudes among Lebanese adults: Assessing psychometric proprieties of the ORTO-R in a population-based sample
Souheil Hallit, Anna Brytek-Matera, Sahar Obeid, Claudio Imperatori
PLOS ONE. 2021; 16(8): e0254948
[Pubmed] | [DOI]
68 The role of access to finance from different finance providers in production risks of horticulture in Indonesia
Eliana Wulandari, Miranda P. M. Meuwissen, Maman H. Karmana, Alfons G. J. M. Oude Lansink, László Vasa
PLOS ONE. 2021; 16(9): e0257812
[Pubmed] | [DOI]
69 Age- and Gender-Related Changes in Physical Function in Community-Dwelling Brazilian Adults Aged 50 to 102 Years
Hélio J. Coelho-Junior, Marco C. Uchida, Ivan O. Gonçalves, Riccardo Calvani, Bruno Rodrigues, Anna Picca, Graziano Onder, Francesco Landi, Roberto Bernabei, Emanuele Marzetti
Journal of Geriatric Physical Therapy. 2021; 44(2): E123
[Pubmed] | [DOI]
70 Greek population’s perceptions of nonpharmacological interventions towards the first wave of COVID-19 pandemic mitigation: A regressionbased association analysis
Eleni Boutsikari, Anna Christakou, Michail Elpidoforou, Ioannis Kopsidas, Nicholas Nikolovienis, Despina Kardara, Chrissoula Boutsikari, Christos Triantafyllou
Pneumon. 2021;
[Pubmed] | [DOI]
71 REVEALING THE EFFECTS OF TEACHERS STIMULATING TECHNOLOGICAL INCLUSION ON UNIVERSITY STUDENTS EDUCATIONAL GAINS: A COMPARATIVE STUDY
Mehboob Ul Hassan, Haq Nawaz, Abu Ul Hassan Faiz
Humanities & Social Sciences Reviews. 2021; 9(3): 986
[Pubmed] | [DOI]
72 The effects of feeding and starvation on antioxidant defence, fatty acid composition and lipid peroxidation in reared Oncorhynchus mykiss fry
Hatayi Zengin
Scientific Reports. 2021; 11(1)
[Pubmed] | [DOI]
73 Evaluating the interchangeability of infrared and digital devices with the traditional mercury thermometer in hospitalized pediatric patients: an observational study
Angelo Dante, Elona Gaxhja, Vittorio Masotta, Carmen La Cerra, Valeria Caponnetto, Cristina Petrucci, Loreto Lancia
Scientific Reports. 2021; 11(1)
[Pubmed] | [DOI]
74 Lightpath QoT computation in optical networks assisted by transfer learning
Ihtesham Khan, Muhammad Bilal, M. Umar Masood, Andrea D’Amico, Vittorio Curri
Journal of Optical Communications and Networking. 2021; 13(4): B72
[Pubmed] | [DOI]
75 Evaluación de marcadores antropométricos de riesgo cardiometabólico en adultos de una comunidad de la región Cañada de Oaxaca, México.
Jacob Jonatan Cruz Sánchez, Remedios Jiménez Pineda, Nelly Victoria Gutiérrez Moguel, Zaydi Anaí Acosta Chí, Citlalli Regalado Santiago, Patricia González Cano
RESPYN Revista Salud Pública y Nutrición. 2021; 20(3): 8
[Pubmed] | [DOI]
76 Determination of Polycyclic Aromatic Hydrocarbons in Batman River by Liquid-liquid and Solid-phase Extractions and the Statistical Comparison of the Two Extraction Techniques
Talha Kemal KOÇAK
International Journal of Environment and Geoinformatics. 2021; 8(4)
[Pubmed] | [DOI]
77 Psychological Effects of Heart Rate and Physical Vibration on the Operation of Construction Machines: Experimental Study
Nobuki Hashiguchi, Jianfei Cao, Yeongjoo Lim, Shinichi Kuroishi, Yasuhiro Miyazaki, Shigeo Kitahara, Shintaro Sengoku, Katsushi Matsubayashi, Kota Kodama
JMIR mHealth and uHealth. 2021; 9(9): e31637
[Pubmed] | [DOI]
78 Effects of environmental factors on health risks by using machine learning
Wangrong Ma, Maozhu Jin, Weili Zhen
Work. 2021; : 1
[Pubmed] | [DOI]
79 Age-Related Exosomal and Endogenous Expression Patterns of miR-1, miR-133a, miR-133b, and miR-206 in Skeletal Muscles
Chrystalla Mytidou, Andrie Koutsoulidou, Margarita Zachariou, Marianna Prokopi, Konstantinos Kapnisis, George M. Spyrou, Andreas Anayiotos, Leonidas A. Phylactou
Frontiers in Physiology. 2021; 12
[Pubmed] | [DOI]
80 Feasibility of Reducing and Breaking Up University Students' Sedentary Behaviour: Pilot Trial and Process Evaluation
Oscar Castro, Ineke Vergeer, Jason Bennie, Stuart J. H. Biddle
Frontiers in Psychology. 2021; 12
[Pubmed] | [DOI]
81 Bedtime Smart Phone Usage and Its Effects on Work-Related Behaviour at Workplace
Abida Ellahi, Yasir Javed, Samina Begum, Rabia Mushtaq, Mobashar Rehman, Hafiz Mudassir Rehman
Frontiers in Psychology. 2021; 12
[Pubmed] | [DOI]
82 Prediction of Solid Waste Generation Rates in Urban Region of Laos Using Socio-Demographic and Economic Parameters with a Multi Linear Regression Approach
Kanchan Popli, Chunkyoo Park, Sang-Min Han, Seungdo Kim
Sustainability. 2021; 13(6): 3038
[Pubmed] | [DOI]
83 Adult Education: A Sustainable Model for the Reduction of Psychosocial and Educational Risks Caused by COVID-19
Manuel-Jesús Perea-Rodríguez, Juan-Agustín Morón-Marchena, María-Carmen Muñoz-Díaz, David Cobos-Sanchiz
Sustainability. 2021; 13(9): 5264
[Pubmed] | [DOI]
84 Shadow of Your Former Self: Exploring Project Leaders’ Post-Failure Behaviors (Resilience, Self-Esteem and Self-Efficacy) in High-Tech Startup Projects
Umer Zaman, Laura Florez-Perez, Pablo Farías, Saba Abbasi, Muddasar Ghani Khwaja, Tri Indra Wijaksana
Sustainability. 2021; 13(22): 12868
[Pubmed] | [DOI]
85 Can intelligent agents improve data quality in online questiosnnaires? A pilot study
Arne Söderström, Adrian Shatte, Matthew Fuller-Tyszkiewicz
Behavior Research Methods. 2021; 53(5): 2238
[Pubmed] | [DOI]
86 Cover Crop Residue Effects on Soil and Corn Performance in Ex-Nickel Mining Soils
Sitti Leomo, Syamsu Alam, Enal Afrianto, La Ode Jamil, Muhidin .
Pakistan Journal of Biological Sciences. 2021; 24(8): 888
[Pubmed] | [DOI]
87 Cost and Treatment Characteristics of Sport-Related Knee Injuries Managed by Athletic Trainers: A Report From the Athletic Training Practice-Based Research Network
Kenneth C. Lam, Ashley N. Marshall, Cailee E. Welch Bacon, Tamara C. Valovich McLeod
Journal of Athletic Training. 2021; 56(8): 922
[Pubmed] | [DOI]
88 In-depth investigation of turn-around time of full blood count tests requested from a clinical haematology outpatient department in Cape Town, South Africa
Leonard Mutema, Zivanai Chapanduka, Fungai Musaigwa, Nomusa Mashigo
African Journal of Laboratory Medicine. 2021; 10(1)
[Pubmed] | [DOI]
89 Impact of Scientific Calculators in Mathematics among Low- Achieving Students in a Secondary School in Kajang, Selangor
Fatimah Salihah Radzuan, Nurzatulshima Kamarudin, Mas Nida Md Khambari, Nurazidawati Mohamad Arsad
Pertanika Journal of Science and Technology. 2021; 29(S1)
[Pubmed] | [DOI]
90 Exploring the Gaze Behavior of Tennis Players with Different Skill Levels When Receiving Serves through Eye Movement Information
Yen-Nan Lin, Jun Wang, Yu Su, I-Lin Wang
Applied Sciences. 2021; 11(19): 8794
[Pubmed] | [DOI]
91 Processing Cycle Efficiency to Monitor the Performance of an Intelligent Tube Preparation System for Phlebotomy Services
Ming-Feng Wu, Jen-Ying Li, Yu-Hsuan Lin, Wei-Chang Huang, Chi-Chih He, Jiunn-Min Wang
International Journal of Environmental Research and Public Health. 2021; 18(17): 9386
[Pubmed] | [DOI]
92 Short-Term Effectiveness of the Youth Gambling Prevention Program “Who Really Wins?”—Results from the First National Implementation
Dora Dodig Hundric, Sabina Mandic, Neven Ricijas
International Journal of Environmental Research and Public Health. 2021; 18(19): 10100
[Pubmed] | [DOI]
93 How Does Food Addiction Relate to Obesity? Patterns of Psychological Distress, Eating Behaviors and Physical Activity in a Sample of Lebanese Adults: The MATEO Study
Anna Brytek-Matera, Sahar Obeid, Marwan Akel, Souheil Hallit
International Journal of Environmental Research and Public Health. 2021; 18(20): 10979
[Pubmed] | [DOI]
94 Association between Self-Reported and Accelerometer-Based Estimates of Physical Activity in Portuguese Older Adults
Célia Domingos, Nadine Correia Santos, José Miguel Pêgo
Sensors. 2021; 21(7): 2258
[Pubmed] | [DOI]
95 Evaluating Starting a Business Indicators Innovation in the World
Antoine Niyungeko
Management & Economics Research Journal. 2021;
[Pubmed] | [DOI]
96 Protection against Brain histopathological damage in experimental Cerebral malaria models after exposure to hyperbaric oxigent
Prawesty Diah Utami, Usman Hadi, Yoes Prijatna Dachlan, Guritno Suryokusumo, R. Loeki Enggar Fitri, Varidianto Yudo
Research Journal of Pharmacy and Technology. 2021; : 3833
[Pubmed] | [DOI]
97 The effects of “Fangcang, Huoshenshan, and Leishenshan” hospitals and environmental factors on the mortality of COVID-19
Yuwen Cai, Tianlun Huang, Xin Liu, Gaosi Xu
PeerJ. 2020; 8: e9578
[Pubmed] | [DOI]
98 Adoption of Road Water Harvesting Practices and Their Impacts: Evidence from a Semi-Arid Region of Ethiopia
Kebede Manjur Gebru, Kifle Woldearegay, Frank van Steenbergen, Aregawi Beyene, Letty Fajardo Vera, Kidane Tesfay Gebreegziabher, Taye Alemayhu
Sustainability. 2020; 12(21): 8914
[Pubmed] | [DOI]
99 Assessment of the Natural Radioactivity of Polish and Foreign Granites Used for Road and Lapidary Constructions in Poland
Tomasz Drzymala, Aneta Lukaszek-Chmielewska, Sylwia Lewicka, Joanna Stec, Barbara Piotrowska, Krzysztof Isajenko, Pawel Lipinski
Materials. 2020; 13(12): 2824
[Pubmed] | [DOI]
100 Application of Ecological Indices using Macroinvertebrate Assemblages in Relation to Aquaculture Activities in Rawang Sub-basin, Selangor River, Malaysia
Nadeesha Dilani Hettige, Rohasliney Binti Hashim, Ahmad Bin Abas Kutty, Nor Rohaizah Binti Jamil, Zulfa Hanan Binti Ash’aari
Pertanika Journal of Science and Technology. 2020; 28(S2)
[Pubmed] | [DOI]
101 The impact of social distancing policy on small and medium-sized enterprises (SMEs) in Indonesia
Muhtar Lutfi, Pricylia Chintya Dewi Buntuang, Yoberth Kornelius, Erdiyansyah, Bakri Hasanuddin
Problems and Perspectives in Management. 2020; 18(3): 492
[Pubmed] | [DOI]
102 Selection of Immature Cat Oocytes with Brilliant Cresyl Blue Stain Improves In Vitro Embryo Production during Non-Breeding Season
Anna Rita Piras, Federica Ariu, Maria-Teresa Zedda, Maria-Teresa Paramio, Luisa Bogliolo
Animals. 2020; 10(9): 1496
[Pubmed] | [DOI]
103 The Effect of a Physiotherapy Intervention on Thoracolumbar Posture in Horses
Amy Shakeshaft, Gillian Tabor
Animals. 2020; 10(11): 1977
[Pubmed] | [DOI]
104 Impact of Alcohol on Occupational Health and Safety in the Construction Industry at Workplaces with Scaffoldings
Marek Sawicki, Mariusz Szóstak
Applied Sciences. 2020; 10(19): 6690
[Pubmed] | [DOI]
105 Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network
Panpan Chen, Chengcheng Liu, Ting Feng, Yong Li, Dean Ta
Applied Sciences. 2020; 10(22): 8089
[Pubmed] | [DOI]
106 SPOR BILIMLERI ALANINDA YAYINLANAN MAKALELERDE KULLANILAN ISTATISTIKSEL YÖNTEMLERIN INCELENMESI
Yetkin Utku KAMUK
Ankara Üniversitesi Beden Egitimi ve Spor Yüksekokulu SPORMETRE Beden Egitimi ve Spor Bilimleri Dergisi. 2020; 18(3): 73
[Pubmed] | [DOI]
107 An interdisciplinary intensive outpatient pain program is associated with improved patient activation and key outcomes
Barbara K Bujak, Christine E Blake, Paul F Beattie, Shana Harrington, Courtney M Monroe, David Wilkie, Mary E Earwood
Pain Management. 2020; 10(5): 307
[Pubmed] | [DOI]
108 Role of Virtual Reality in Balance Training in Patients with Spinal Cord Injury: A Prospective Comparative Pre–Post Study
Shilpasree Saha
Asian Spine Journal. 2020; 14(2): 264
[Pubmed] | [DOI]
109 Response to: Role of Virtual Reality in Balance Training in Patients with Spinal Cord Injury: A Prospective Comparative Pre–Post Study
Dhritiman Chakrabarti, Anupam Gupta
Asian Spine Journal. 2020; 14(2): 266
[Pubmed] | [DOI]
110 Letter to the Editor: “Sarcopenia and Back Muscle Degeneration as Risk Factors for Back Pain: A Comparative Study”
Hina Vaish
Asian Spine Journal. 2020; 14(4): 581
[Pubmed] | [DOI]
111 Response to: “Sarcopenia and Back Muscle Degeneration as Risk Factors for Back Pain: A Comparative Study”
Whoan Jeang Kim, Kap Jung Kim, Dae Geon Song, Jong Shin Lee, Kun Young Park, Jae Won Lee, Shann Haw Chang, Won Sik Choy
Asian Spine Journal. 2020; 14(4): 583
[Pubmed] | [DOI]
112 A Quality of Experience assessment of haptic and augmented reality feedback modalities in a gait analysis system
Thiago Braga Rodrigues, Ciarán Ó Catháin, Noel E. O’Connor, Niall Murray, Bijan Najafi
PLOS ONE. 2020; 15(3): e0230570
[Pubmed] | [DOI]
113 Impact of Mobile Augmented Reality System on Cognitive Behavior and Performance during Rebar Inspection Tasks
Ali Abbas, JoonOh Seo, MinKoo Kim
Journal of Computing in Civil Engineering. 2020; 34(6): 04020050
[Pubmed] | [DOI]
114 Suicidal ideation among Lebanese adolescents: scale validation, prevalence and correlates
Melissa Chahine, Pascale Salameh, Chadia Haddad, Hala Sacre, Michel Soufia, Marwan Akel, Sahar Obeid, Rabih Hallit, Souheil Hallit
BMC Psychiatry. 2020; 20(1)
[Pubmed] | [DOI]
115 Impact of Workforce Diversity Management on Employees’ Outcomes: Testing the Mediating Role of a person’s Job Match
Wenjing Li, Xuhui Wang, Md Jamirul Haque, Muhammad Noman Shafique, Muhammad Zahid Nawaz
SAGE Open. 2020; 10(1): 2158244020
[Pubmed] | [DOI]
116 Previous Intestinal Resection Is Associated with Postoperative Complications in Crohn’s Disease: A Cohort Study
Yantao Duan, Yifan Liu, Yousheng Li
Gastroenterology Research and Practice. 2020; 2020: 1
[Pubmed] | [DOI]
117 The impact of sensory activity schedule (SAS) intervention on classroom task performance in students with autism – a pilot randomised controlled trial
Caroline Jennifer Mills, Christine Chapparo, Joanne Hinitt
Advances in Autism. 2020; 6(3): 179
[Pubmed] | [DOI]
118 Associations of obstetrical characteristics and dietary intakes with iron status among pregnant women in Selangor and Kuala Lumpur
Meng Lee Tan, Yit Siew Chin, Poh Ying Lim, Salma Faeza Ahmad Fuzi
British Food Journal. 2020; 122(10): 3115
[Pubmed] | [DOI]
119 The effectiveness of instructional media based on lectora inspire towards student’s achievement
N V Saputro, Masturi, Supriyadi
Journal of Physics: Conference Series. 2020; 1567(2): 022063
[Pubmed] | [DOI]
120 Gender differences in the prevalence of vitamin D deficiency in a southern Latin American country: a pilot study
M. S. Vallejo, J. E. Blümel, E. Arteaga, S. Aedo, V. Tapia, A. Araos, C. Sciaraffia, C. Castelo-Branco
Climacteric. 2020; 23(4): 410
[Pubmed] | [DOI]
121 Infertility and perceived stress: the role of identity concern in treatment-seeking men and women
Paul Grunberg, Skye Miner, Phyllis Zelkowitz
Human Fertility. 2020; : 1
[Pubmed] | [DOI]
122 Psychological outcomes of MRSA isolation in spinal cord injury rehabilitation
Jenna L. Gillett, Jane Duff, Rebecca Eaton, Katherine Finlay
Spinal Cord Series and Cases. 2020; 6(1)
[Pubmed] | [DOI]
123 Differential microRNAs expression in acute graft-versus-host disease as potential diagnostic biomarkers
Jamshid Motaei, Marjan Yaghmaie, Hossein Pashaiefar, Seyed Asadollah Mousavi, Ardeshir Ghavamzadeh, Mohammad Ahmadvand, Mohammad Amin Kerachian
Bone Marrow Transplantation. 2020; 55(12): 2339
[Pubmed] | [DOI]
124 Prolonged bedrest reduces plasma high-density lipoprotein levels linked to markedly suppressed cholesterol efflux capacity
Athina Trakaki, Hubert Scharnagl, Markus Trieb, Michael Holzer, Helmut Hinghofer-Szalkay, Nandu Goswami, Gunther Marsche
Scientific Reports. 2020; 10(1)
[Pubmed] | [DOI]
125 Pregnancy status and thyroid function in semi-intensive-kept Marecha she-camels (Camelus dromedarius): managerial implications
Asim Faraz, Carlos Iglesias Pastrana, Annamaria Passantino, Ayman Balla Mustafa, Abdul Waheed, Nasir Ali Tauqir, Muhammad Shahid Nabeel
Tropical Animal Health and Production. 2020; 52(6): 3387
[Pubmed] | [DOI]
126 Spatial and temporal distributions of terrestrial and marine organic matter in the surface sediments of the Yangtze River estuary
Shanshan Zhang, Cui Liang, Weiwei Xian
Continental Shelf Research. 2020; 203: 104158
[Pubmed] | [DOI]
127 Ecuadorian mothers of preschool children with and without intellectual disabilities: Individual and family dimensions
Carmita E. Villavicencio, Silvia López-Larrosa
Research in Developmental Disabilities. 2020; 105: 103735
[Pubmed] | [DOI]
128 Mental Health Outcomes of the COVID-19 Pandemic and a Collapsing Economy: Perspectives from a Developing Country
Pascale SALAMEH, Aline HAJJ, Danielle A BADRO, Carla ABOU?SELWAN, Randa AOUN, Hala SACRE
Psychiatry Research. 2020; 294: 113520
[Pubmed] | [DOI]
129 Letter to the Editor regarding: Does pectoralis minor stretching provide additional benefit over an exercise program in participants with subacromial pain syndrome? A randomized controlled trial
Saumya Kothiyal, Manu Goyal
Musculoskeletal Science and Practice. 2020; 46: 102126
[Pubmed] | [DOI]
130 Letter to editor “Effectiveness of additional deep-water running for disability, lumbar pain intensity, and functional capacity in patients with chronic low back pain: A randomised controlled trial with 3-month follow-up"
Gurjant Singh, Saumya Kothiyal
Musculoskeletal Science and Practice. 2020; 50: 102227
[Pubmed] | [DOI]
131 Corticospinal activity during a single-leg stance in people with chronic ankle instability
Masafumi Terada, Kyle B. Kosik, Ryan S. McCann, Colin Drinkard, Phillip A. Gribble
Journal of Sport and Health Science. 2020;
[Pubmed] | [DOI]
132 Unleashing ß-catenin with a new anti-Alzheimer drug for bone tissue regeneration
Marianne Comeau-Gauthier, Magdalena Tarchala, Jose Luis Ramirez-Garcia Luna, Edward Harvey, Geraldine Merle
Injury. 2020; 51(11): 2449
[Pubmed] | [DOI]
133 Multiparametric qMTI Assessment and Monitoring of Normal Appearing White Matter and Classified T1 Hypointense Lesions in Relapsing-Remitting Multiple Sclerosis
M. Fooladi, N. Riyahi Alam, H. Sharini, K. Firouznia, M. Shakiba, M.H. Harirchian
IRBM. 2020; 41(3): 151
[Pubmed] | [DOI]
134 Clinical Severity as a Predictor of Nursing Workload in Pediatric Intensive Care Units: A Cross-Sectional Study
Alexandra-Stavroula Nieri, Eleni Spithouraki, Petros Galanis, Daphne Kaitelidou, Vasiliki Matziou, Margarita Giannakopoulou
Connect: The World of Critical Care Nursing. 2019; 13(4): 175
[Pubmed] | [DOI]
135 Comparison of the recruitment of transverse abdominis through drawing-in and bracing in different core stability training positions
Navid Moghadam, Maryam Selk Ghaffari, Pardis Noormohammadpour, Mohsen Rostam, Mohammad Zarei, Mersad Moosavi, Ramin Kordi
Journal of Exercise Rehabilitation. 2019; 15(6): 819
[Pubmed] | [DOI]



 

Top