DATA ANALYSIS 1.

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Research Methodology
This research will use descriptive research using qualitative and quantitative measures. The tools
used in the data collection have a descriptive design. A questionnaire was formulated to help in
conducting the research. After formulating the questionnaire, we will decide on the target group
and target population to be used in the data collection. Therefore, we will use primary data to
conduct our research. The questionnaire was either administered online or face-to-face by
interviewing the respondent. The questionnaire was administered to the desired group for the
data collection. The interview will aim at getting clarification on the health information system.
After collecting the data, the data will be analyzed using SPSS software. SPSS is statistical
software for social science. Before conducting the data analysis, the data is edited and recorded
in SPSS software. The questionnaire questions will be recorded in the label under the variable
view. The questionnaire will also be assigned simple names and the scores will be defined in the
Values section. After recording the data, we will apply descriptive statistic, correlation, and
regression analysis. Under descriptive analysis, we will tackle the frequency distribution method
such as the measure of central tendency (mean, mode and median) and the measure of dispersion
(variance, standard deviation, and interquartile range). Correlational Analysis will be conducted
to determine the relationship between the dependent and independent variables. Regression
Analysis will be conducted to create a prediction of the dependent variable using one or several
independent variables. There are two types of missing values in SPSS:
i) System missing values - here, the values are totally absent from the data
ii) User missing value – the values are invincible while editing the data.

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You can conduct the analysis and exclude the missing values. In every analysis conducted in
SPSS software, there is an option to exclude the missing values. Another way to tackle the
missing values is by editing data with missing values before conducting the analysis Josse and
Husson (2016).

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References
Josse, J. and Husson, F., 2016. missMDA: a package for handling missing values in multivariate
data analysis. Journal of Statistical Software, 70(1), pp.1-31.