Spss window version 20

The coded data was checked, cleaned, and entered into the epidemiological information software (Epi info version 3.5.1) to ensure data accuracy and then exported into SPSS window version 20.0 (SPSS Inc., Chicago, IL) for analysis. Descriptive statistics were manipulated for all variables. Bivariate analysis was first conducted for each potentially explanatory risk factor to see associations to select for multivariate analysis. Variables that had a P value <0.25 in bivariate analysis were run in multivariable logistic regression. Multiple logistic regressions were performed to assess the association between binary outcomes and different explanatory variables. The strength of association was interpreted using odds ratio and confidence interval. P value <0.05 was considered statistically significant in this study.

Data were entered to Epi-Info 7.0.9.7 version computer software package and exported to SPSS window version 20 for data management and analysis. Editing, cleaning, and checking for completeness and consistency was done. Descriptive analysis was done to summarise the characteristics of study participants. Both bivariate and multivariate logistic regression analysis was done to identify the risk factors associated withNTDs. Variables with P value < 0.25 during the bivariate analysis were included in the multivaribabe logistic regression model to control the effect of confounding variables and identify statistically significant associated factors. Adjusted odds ratio with 95% confidence interval was calculated and variables with P-value less than 0.05 were considered as statistically significant risk factors to NTDs. Finally, data was presented using graphs, tables and statements.

The data was collected using closed-ended questionnaires. The information received will assist us in accomplishing the study’s goal. There are two parts covered in data analysis section. In the frequency Table 1, the respondent profile is evaluated using the descriptive statics tool of analysis from SPSS Window version 20. The study’s objectives are discussed in part two. Basically, the level of awareness, benefits, and challenges associated with industry 4.0 and IoT in the construction industry. The relative importance index (RII) analysis was used to analyze them.Table 1

Highest level of educational qualification and experience

Variables	Category	Frequency	Percent (%)
Highest educational qualification	PHD	23	15.0
	Masters	26	17.0
	First degree	82	53.0
	Diploma	18	12.0
	Others	5	3.0
Years of professional experience	1–5 years	73	47.0
	6–10 years	35	20.0
	11–16 years	25	19.0
	Over 16 years	21	14.0

All data obtained were analysed using Statistical Package for the Social Science (SPSS) Window version 20. Descriptive statistic was used and results were presented in mean score for each factor. Pearson Correlation was used to test for statistically significant relationship between satisfaction score and age of the participants, One-way ANOVA test was used to identify the relationship between satisfaction score with education level and working experience and independent t-test analysed the relationship between satisfaction scores and position of the participants. The overall JSS score is classified as dissatisfied, moderate, satisfied and no opinion with total scores of 20–40, 41–60, 61–100 and 0 respectively [1 (link)]. P-value < 0.05 was considered as statistically significant.

The collected data were checked for completeness and entered into Epi Data software version 3.1 and exported to SPSS window version 20 for data processing and analysis. Descriptive statistics like percent, mean and standard deviation were done. Both bivariate and multivariable logistic regression analysis were computed to identify associated factors. Odds ratio along with 95% CI was computed to ascertain the association between independent and outcome variables. Variables that have p-value of <0.2 at bivariate analysis were included in multivariable logistic regression to control possible confounding factors. Statistical tests at p-value of <0.05 were considered as cut off point to determine statistical significance.

The data was cleaned, edited and entered into Epi data version 4.1. Then, the data was exported to SPSS window version 20 for analysis. Descriptive statistics was done by computing proportions and summary statistics. Chi-square testing were used and normality were checked. Binary logistic regression model was employed to identify associated factors. Initially, bivariate logistic regression analysis was done and crude odd ratio (COR) with 95% CI was computed. In the Bivariate analysis, variables with a p-value of below 0.2 were included in the multi-variable logistic regression analysis. Adjusted odd ratios with 95% CI were calculated and factors with a p-value less than 0.05 were declared as independent predictors. Model goodness of fit was checked by Hosmer-Leme show goodness-of-fit test.

The coded data were checked, cleaned by entering into epi.info version 7.1 and then exported into Statistical Package for the Social Sciences (SPSS window version 20).
Descriptive statistics were used to summarize tables and figures and statistical summary measures were used for presentation. Association of HIV-related perceived stigma variables and demographic characteristics were analyzed using chi-square, fisher's exact test, and binary logistic regression with odds ratio and 95% CI in the univariate analysis.
Multivariate logistic regression analysis was carried out to examine the associations between each independent variable and the outcome variable. The model was checked for fitness with R-squared value was an R-squared value greater than 50% considered as good. Hosmer and Lemshow goodness of fit test was also used to check the model fitness. All variables with a p-value of ≤ 0.25 in the bivariable analysis were considered as the candidate for multivariable regression to control possible confounders. Finally, variables with a p-value of <0.05 were as having a statistically significant association with HIV-related perceived stigma at corresponding 95% CI.