Definition of Data Analysis
Data analysis in quantitative research proposal is one part of the chapter that researchers need in the beginning of writing a research proposal. Whereas in the research, it is an activity after the data from all collected. Activities in data analysis are: grouping data based on variables and types of respondents, tabulating data based on variables from all respondents, presenting data for each variable studied, doing calculations to answer the problem formulation, and doing calculations to test the proposed hypothesis.
Quantitative Data Analysis Techniques
In a research proposal, it must be clear what method of analysis is capable of answering the research hypothesis. Hypothesis is a temporary answer to the research problem. Data analysis techniques in quantitative research commonly use statistics. There are two kinds of statistical data analysis in research. These are descriptive statistics and inferential statistics. Inferential statistics include parametric and non-parametric statistics.
In preparing research proposals, researchers need to explain what is descriptive research. Descriptive statistic is a method to analyze data by describing data without intending to make generalizations. Descriptive statistics only describes the sample data and does not make conclusions that apply to the population. While, conclusion that applies to the population, then the data analysis technique is inferential statistics.
In addition descriptive statistics also function to present information in such a way that data generated from research can be utilized by others in need.
When researchers want to generalize broader conclusions in the research proposal, it is necessary to write inferential statistics. Inferential statistics (often also commonly inductive statistics or probability statistics) are statistical techniques used to analyze sample data and the results are applied to populations. It requires a random sampling process.
Inferential research involves statistical probability. Using of probability theory is to approach sample to the population. A conclusion applying to the population has a chance of error and truth level. If the chance of error is 5%, then the truth level is 95%. While the chance of error is 1%, then the truth level is 99%. This opportunity for error and truth is the level of significance. Statistical tables are useful for carrying out tests of the significance of this error. For example, t-test will use table-t. in each table provides significance level of what percentage of the results. For example the correlation analysis found a correlation coefficient of 0.54 and for a significance of 5% it means that a variable relationship of 0.54 can apply to 95 out of 100 samples taken from a population.
Inferential statistics is a higher level then descriptive statistics. To that in the research proposal, the flow of conclusions becomes clear. Data Analysis is to make general conclusions (conclusions), make a prediction (prediction), or make an estimate (estimation).
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