data of Knowledge related to Chronic Kidney Disease (CKD) and perceptions on inpatient management among nurses at selected referral hospitals in Rwanda.

The level of knowledge related to CKD and perceived inpatient management was moderate among nurses.
Eighty four percent (84%) had moderate level of knowledge related to CKD and 51% of nurses had moderate level of perceptions regarding of inpatient management of CKD. The factors associated with knowledge were institution type (p=.024), department (p=.000), level of education (p=.010) and type of specialty (p=.000). The factor associated perceived inpatient management of CKD was department type (p=.015). A very weak non-significant positive relationship (r = .115, N = 120, p = .21) between knowledge and practice was observed.

Before analyzing the data, the data was cleaned, grouped and classified according to demographic characteristic, knowledge and perceptions. These quantitative data were analyzed descriptively using tables, and percentages, and inferential statistics (chi-square) with the help of SPSS (Statistical Package for Social Sciences) window version 21. Descriptive statistics used to describe the demographic variables, the level of nurse’s knowledge and perceptions regarding inpatient CKD management. Inferential statistics (chi-square test) used to establish association between demographic characteristics and nurse’s knowledge and perceptions regarding inpatient CKD management. Pearson correlation coefficient ® was calculated to identify the direction and the strength of the relationship between nurse’s knowledge and perceptions regarding inpatient CKD management and
The Pearson correlation determine the strength and direction of a relationship between two quantitative/numerical variables. It ranges from negative (-1) to positive (+1) coefficient values.
A negative correlation indicates that high values on one variable are associated with low values on the next. A positive correlation indicates that high values on the one variable are associated with high values of the next .