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We propose methods to obtain the variability of model-based reliability estimates for survey data analysis. Health questionnaires play a role in health economics research by providing a means to collect data on individuals’ health-related information, preferences, behaviors, and outcomes. Model-based reliability, or coefficient omega, have become a popular concept to estimate test score reliability for various health care and health utility research instruments. Notably, these instruments are often embedded in data collection for nationwide health surveys. Data analysis of survey data needs to be capable of incorporating the survey design, where the data commonly accompany with unequal probabilities caused by clustering and poststratification. Methods for estimating the variability of coefficient omega estimates for survey data analysis have not been investigated in the statistical literature, although it is a widely used tool to assess instrument reliability. In this article, we discuss a generally applicable linearization scheme for the relevant inference of such estimates based on the influence function approach when applied to complex survey data. Through the Monte Carlo study, we show adequate coverage rates for the confidence intervals based on scenarios of stratified multistage cluster sampling. Using data from the Medical Expenditure Panel Survey (MEPS), we provide the confidence intervals for the two types of coefficient omega (i.e., omega hierarchical and omega total) for the Short Form-12 version 2 (SF-12v2), a widely used health survey instrument to assess quality of life, and compare reliabilities of the instrument by different demographics.

Keywords: Coefficient omega, composite reliability, complex survey, influence function, linearization.

The study investigated the effect of domestic debt on Nigerian economy. The study utilized a time series data from 1990 to 2018, and adopts among other techniques the Ordinary Least Squares (OLS) test method. A multiple regression model is formulated to ascertain the relationship between the economic growth and debt financing variables. Our findings establish that Domestic Debt and Debt Servicing (DBS) will increase the Real Gross Domestic Product. However, our result with positive coefficients for Domestic Debt (DDB) and Debt Servicing (DBS), indicates that if they are increased, can also increase economic growth. External Debt (EXD) on the other hand exhibited a positive but insignificant relationship with Real Gross Domestic Product (RGDP). This means that government External Debt (EXD) has not contributed to meaningfully to the economy. The study therefore advocate for adequate coordination of the debt financing policy to better the economy. Also government should revive active process in the public sector that will ensure adequate utilization and accountability of borrowed fund.

Keyword: Domestic Debt, Debt Servicing, External Debt, Real Gross Domestic Product.

The study titled Time Series Modelling and Forecasting of Seasonal Rainfall Patterns in Bida Basin, Nigeria, modelled and forecasted time series data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis precipitation data from 1981-2020. The mean method, naïve method and seasonal naïve method were used as benched mark forecasting method to linear regression methods, exponential methods and ARIMA methods. The R-statistical package was used for analysing the data. In choosing among exponential methods and ARIMA methods, the est() and auto.arima() functions were employed respectively to select model that best capture the features of the time series data while the R-squared was employed to select from linear regression models. The scaled methods for prediction error evaluations and residual analysis were performed on the various models considered. The linear regression, seasonal ARIMA and exponential smoothing methods were better than the three benched mark forecasting methods. However, root mean squared error (RMSE) evaluation showed that the linear regression model with trend and seasonality had the closest predicted value to the actual rainfall values. With the mean absolute error (MAE) and the mean absolute scaled error (MASE), ARIMA (0,0,0)(0,1,1)[12] has the least value. Based on the results from the residual analysis and error dependent evaluation methods, linear regression model was selected for modelling and forecasting Bida basin rainfall since it had the closest prediction value to the actual value.

Keywords: Rainfall, Modelling, Forecasting, Bida Basin, Pattern

This paper presented autoregressive distributed lag method in gross domestic product, agriculture and industry interaction in Nigeria. The study tries to examine trend and causal relationship between gross domestic product, agriculture and industry in Nigeria over the period 1981 to 2022. The data for the study was from National Bureau of statistic. To analyze the data, the autoregressive distributed lag (ARDL) model was introduced involving the unit root test, bounds test and standard diagnostic test for serial correlation and stability. From the results obtained, agriculture and industry contributed significantly to gross domestic product growth in Nigeria in the short run. While in the long run only gross domestic product had significant influence on agriculture. It was recommended that more innovations should be channel to agriculture and industry in Nigeria.

Keyword: Autoregressive Distributed Lag, Gross Domestic Product, Industry, Agriculture, Interaction.

This research is determine to investigate the rampant cases of domestic violence in North East region of Nigeria. In some state in north eastern region of Nigeria, many women are brutally treated by their intimate partners who in some cases lead to their death. Domestic violence affects all social groups and can consist of physical, sexual or psychological abuse. The level of violence against women is highly increasing by the day and many certain communities experiencing violence in their families. Many women do not report their cases as they are ashamed that their futures will be in jeopardy. So they suffer in silence. It is against this background that the researcher looks into cases of domestic violence against women in Nigeria specifically in north east, types, causes, effects, and management. The model used to carry out this study is Latin square design (LSD) as cases between 2015-2020 are critically examined. The results shows that there is significant difference on domestic violence between the state, years and rates of violence. The research recommended comprehensive and extensive premarital counseling should be given to intending couples on how to manage their marital relationship; and the government should establish and fund counseling centers at the community, Local Government levels and State levels and employ professional counselors to help victims and perpetrators of domestic violence. Also government should provide a means of livelihood such as employment and financial support to people to reduce the hardship and

suffering, so that the rates of violence will reduce.

Keywords: Univariate Anova, Latin Square Design, Domestic, Violence and Abuse.