Data Analysis: Hints And Tips

When conducting an experiment, the researcher may be confronted with two types of data: quantitative and qualitative data

. Quantitative data deals with items that can be easily measured, typically numbers or amounts. Examples of quantitative data include lengths, heights, weight, age, temperature or cost. Qualitative data can be observed but is not easily measured. These are usually descriptions and can include colour, appearance, opinions, and textures.

Data Analysis Definition

Data analysis is the practice of organising raw data into a format that is meaningful so that useful information can be extracted. This raw data is obtained by a variety of methods, including surveys, interviews, measurements and observations. In order to make better sense of the information, it is necessary to organise it in a way that is easy to manipulate and interpret.

Spotting Trends

While organising the data, the researcher might begin to notice the emergence of trends. For example, when examining survey data about what TV shows adult Australians like to watch on Friday nights, the researcher may find that there are considerably more males that answer 'Friday Night Football' than there are females. This is a trend. This type of data is useful when attempting to discover information about the population that the researcher is surveying.

Presenting Data Analysis Results

Often, when data is being reorganised it will be used to create a graph, frequency table or chart in order to provide a better visual representation of the experiment's findings. Researchers may also create a textual write up, which is a written version of the findings that they encountered while analysing their data. These reporting formats allow interested parties to examine the researcher's data and find out useful information without needing to conduct research of their own. After the data has been reorganised the researcher is able to determine the median and mean of the data set, and they can also use intervals to answer statistical questions that may arise. Relative frequencies can therefore be determined and bar graphs can be created to represent the researcher's findings.

Building a Case

Data analysis is also useful in supporting arguments. Information is presented in a clear and understandable way, and it can potentially prove a theory that the researcher came up with before the study was conducted. Good data analysis requires the researcher find the best data relevant to their research questions, compare the data, and develop conclusions from those comparisons. When evaluating arguments, a person will need to take into account the validity and reliability of the measures and in the conclusions developed from the data.

Data Analysis Risks

When conducting data analysis, it is necessary to take into account potential errors that may have occurred during the experimentation. Human error is one example, and it occurs when the researcher makes a mistake. A systematic error is an error that is caused by the experimental set up which results in the data being skewed. Keeping these possible sources of error in mind will help the researcher produce a more accurate representation of what their data expresses. More importantly, it will ensure correct conclusions are reached and the right decisions are made as a consequence.

by: Ted Brumby

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