Open AccessManagement of Utilizing Data Analysis and Hypothesis Testing in Improving the Quality of Research Reports
Author(s) -
Muhamad Yusup,
Romzi Syauqi Naufal,
Marviola Hardini
Publication year - 2019
Publication title -
aptisi transactions on management
Language(s) - English
Resource type - Journals
eISSN - 2622-6812
pISSN - 2622-6804
DOI - 10.33050/atm.v2i2.789
Subject(s) - statistical hypothesis testing , descriptive statistics , computer science , coding (social sciences) , parametric statistics , data quality , statistics , alternative hypothesis , data collection , statistical analysis , data mining , null hypothesis , mathematics , engineering , metric (unit) , operations management
Data analysis and mathematical techniques play a central role in quantitative data processing. Quantitative researchers estimate (strength) the strength of the relationship of variables, and test hypotheses statistically. Unlike the case with qualitative research. Although qualitative researchers might test a hypothesis in the analysis process, they do not estimate or test hypotheses about the relationship of variables statistically. Through tests or statistical tests can be used as the main means for interpreting the results of research data. It is through this statistical test that we as researchers can compare which data groups and what can be used to determine probabilities or possibilities that distinguish between groups based on an opportunity. Thus, it can provide evidence to determine the validity of a hypothesis or conclusion. In this study, we will discuss the preparation of data for analysis such as editing data, coding, categorizing, and entering data. As well as discussing the differences in data analysis for descriptive statistics and inferential statistics, differences in data analysis for parametric and non-parametric statistics in research, explanations of multivariate data analysis procedures, and also forms of research hypotheses.