Data were checked for completeness and consistency in semi-structured questionnaires. All statistical analyses were performed using the STATA 14.0 statistical package. Descriptive statistics was used to generate frequencies of categorical variables and to summarize data. The Pearson chi-square test was used to evaluate the statistical significance of the bivariate association of selected dependent variables with the outcome variables. An odds ratio with 95% CI was used to screen the possible associated factors. Univariate and multiple logistic regression analysis was used to examine the effect of explanatory variables (socio-demographic) on outcome variables (delayed seeking healthcare and TB Knowledge). Variables included in the multiple regression were selected based on a P-value of .1 at univariate analysis. P-values < .05 were considered as statistically significant.
Stata 14.0 statistical
Manufactured by StataCorp
Stata 14.0 is a comprehensive statistical software package for data analysis, data management, and graphics. It provides a wide range of statistical tools and techniques for researchers, analysts, and scholars across various disciplines.
Automatically generated - may contain errors
Lab products found in correlation
3 protocols using stata 14.0 statistical
1
Factors Affecting TB Healthcare Seeking
2
Smartphone-Aided Data Collection and Analysis
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Data were collected using electronically using smartphones with the aid of open data kit (ODK) software loaded with the questionnaire template. Collected data was uploaded to a secured google cloud server online. The data was later downloaded in excel format and imported in Stata 14.0 statistical software, cleaned and for analysis. Data completeness and consistency were checked by using a self-written program in Stata.
3
Demographic Factors and Policy Attitudes
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All statistical analyses were performed in Stata 14.0 statistical software (Stata Corp, College Station, TX). Frequencies were calculated to describe respondent characteristics and responses. Responses to the policy questions were dichotomized into two categories: yes and no/unsure. Missing responses were coded with “no/unsure.” For demographic variables, missing variables were coded as “other” or were included in a separate category. To examine associations between school and respondent characteristics and outcomes, chi-square tests were performed.
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