Bivariate Analysis

Sandilands, Debra

doi:10.1007/978-94-007-0753-5_222

Cited by 8 publications

(3 citation statements)

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“…Bivariate analysis involves examining two variables to uncover potential connections between them. Such analyses are commonly featured in studies related to quality of life [12].…”

Section: Methodsmentioning

confidence: 99%

Relationship between age, occupation, education, and parity with the chronic energy deficiency among pregnant women

Auranissa,

Zuhairini,

Nurdiawan

2024

IJPHS

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Maternal and child health quality depends on maternal nutrition during pregnancy. Chronic energy deficiency (CED), a malnutrition condition common in pregnant women, increases the risk of the mother experiencing postpartum hemorrhage and the fetus experiencing intrauterine growth restriction (IUGR). The prevalence of CED risk among pregnant women in Sukabumi City, West Java, Indonesia is unstable and has not significantly decreased. This study aimed to determine the relationship between age, occupation, education, and parity with the risk of CED among pregnant women in Sukabumi City. An analytical observational study with a case-control approach using cluster sampling was conducted on 126 pregnant women with and without CED risk, respectively. The population for this study was pregnant women who checked at the primary health center in Sukabumi City in 2021. Data were collected from maternity health record and analyzed using Chi-square. The results showed that age had a significant relationship (p=0.000). In contrast, occupation (p=0.162), education (p=0.127), and parity (p=0.313) did not have a significant relationship with the risk of CED among pregnant women in Sukabumi City. Age was associated with the risk of CED among pregnant women in Sukabumi City.

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“…Bivariate analysis involves examining two variables to uncover potential connections between them. Such analyses are commonly featured in studies related to quality of life [12].…”

Section: Methodsmentioning

confidence: 99%

Relationship between age, occupation, education, and parity with the chronic energy deficiency among pregnant women

Auranissa,

Zuhairini,

Nurdiawan

2024

IJPHS

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“…For processing the answers, statistical data analysis was employed, using quantitative methods, based on IBM-SPSS Statistics v.28 software, Chi square, frequency descriptive statistic and graph function (relationship map) as well as bivariate analysis. Bivariate analyses, a nonparametric test, helps to identify a statistical association between two variables, and the degree of association and the prediction of each other influences (Sandilands, 2014). As the independent and dependent variables were categorical, the study applied the Spearman correlation (rs).…”

Section: Methods and Research Designmentioning

confidence: 99%

Perceptions of Generation Y on Social Aspects Involving Sustainable Tourism in Romanian Destinations

PÎRGHIE¹,

Matei²,

Cocoş³

2021

JETA

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Sustainable tourism destination management requires the simultaneous and multidirectional pursuit of interactions. One of these are tourists, which are structured differently, based on socioeconomic and psycho-formative criteria. Taking into account the general features of Generation Y, the study aims at analyzing their perception regarding social aspects demanded in sustainable tourism destinations in the case of Romania. The research is focuses on the survey method, which relies on a semi-structured questionnaire applied online in 2020. The answers were modeled, using data analysis tools or graphics functions in IBM-SPSS v.28. Study findings highlight the interesting social perceptions of Y tourists in Romania (RGY), with an emphasis on their desire for conflict-free destinations, to spend time with friends or family, but also to make carefully chosen friends. The perceptions regarding the acceptance of interactions with hosts varies by their socio-demographic characteristics, depending especially on the issues that require their active involvement. These reveal a predominantly interest on professional services, largely excluding creative activities for the acquisition of local traditions. In conclusion, RGY considers that their social inputs as neutral in the tourist areas. However, more attention should be focused on the management of tourism products, in order to come up with specific offers for a highly skilled, but stressed generation, as well as on finding ways able to boost through motivating solutions the young people's interest for social dimension of the sustainable tourism development.

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“…However, since the research aims to identify the critical factors influencing the adoption possibilities, and in line with objective 2, a further analysis was run with feature selection. This multi-variate process is better than a single correlation process because it identifies the variable of casualty when the value of the independent variable changes as result of the relation that exist between them (Mayuresh, 2021;Sandilands, 2014). As can be deduced from the initial analysis, the correlation process shows a poor relationship between the variables.…”

Section: Feature Engineeringmentioning

confidence: 99%

A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria

Wusu

Alaka²,

Yusuf³

et al. 2022

SASBE

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PurposeSeveral factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.Design/methodology/approachThe research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).FindingsThe research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.Research limitations/implicationsData were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.Practical implicationsThe research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.Originality/valueThe research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

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Bivariate Analysis

Cited by 8 publications

References 0 publications

Relationship between age, occupation, education, and parity with the chronic energy deficiency among pregnant women

Relationship between age, occupation, education, and parity with the chronic energy deficiency among pregnant women

Perceptions of Generation Y on Social Aspects Involving Sustainable Tourism in Romanian Destinations

A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria

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