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Source, Types, and Scale of Data Measurement in Research

Research is an activity that utilizes data to be analyzed, leading to conclusions. In this regard, data is a vital instrument in research. Data represents recorded information about the research.

Data can also be defined as information related to a condition, description, and/or a particular matter. Meanwhile, statistics is the method used to process data to provide information.

Data based on its source and type

Data can be divided into two categories based on its source:

1. Primary Data

Primary data is data obtained directly from the research subjects.

2. Secondary Data

Secondary data is data obtained from other sources, not directly from the research subjects by the researcher.

Data based on its type can be divided into two categories:

1. Quantitative Data

Quantitative data is numerical data that is typically processed using statistical techniques and is objective in nature. Examples of quantitative data include milk production, GDP, GPA, and so on.

2. Qualitative Data

Qualitative data is non-numerical data, often in the form of verbal descriptions or categorizations. It is more subjective in nature and can be represented in the form of rankings for data with an ordinal scale. Examples of qualitative data include some students feeling happy about farming while others do not, the cow being large, the chicken being small, and so on.

Scale of Data Measurement

The types of data measurement scales can be categorized into four:

1. Nominal Scale

The values of the variable only differentiate one category from another. For example, various types of occupations are differentiated only by their categories, without implying that one occupation is better than another.

2. Ordinal Scale

The scale is based on rankings and differentiates the data based on order and levels. For example, levels of education ranked from the highest to the lowest or vice versa.

3. Interval Scale

Data that possesses the characteristics of an ordinal scale but also has known intervals between values. For example, the variable “age” has both value and interval information.

4. Ratio Scale

Data that has characteristics of an ordinal scale but is used to measure distances and weights. For example, the weight of a father is three times the weight of his child.

Guidelines for Data Collection

The process of data collection should adhere to data collection guidelines. This is done to ensure that the data used in the research is valid, reliable, and in accordance with scientific principles. The guidelines for data collection are as follows:

  1. Data collection should consider the clarity of concepts and variables to be used.
  2. Data should be standardized.
  3. The collected data should be objective and accountable.
  4. Data should be relevant to the units or observational units.

Survey and Experimental Research

Research can be broadly categorized into survey research and experimental research based on their implementation. Survey research utilizes and observes existing phenomena, while experimental research generates data through experiments.

Survey research is commonly used in social sciences and sciences. In the field of animal husbandry, surveys are not uncommon, as survey research is commonly employed in studies focusing on the social and economic aspects of livestock.

After conducting data collection in the field for survey research, the next step is data compilation and analysis using statistical tools. These statistical tools can include programs such as SPSS, Microsoft Excel, SAS, and others.

Conclusion

Understanding data is crucial in research activities. Understanding the types of data will determine the appropriate choice of tests. Based on the appropriate tests, accurate conclusions can be drawn.

This concludes the article that I can share on this occasion. Hopefully, it provides benefits and added value to those who need it. Stay tuned for the next article update in the following week.

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