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Qualitative and Quantitative Data Analysis: 7 Differences and the Common Sense

Fundamentally different research types like quantitative and qualitative have always been positioned as opposing ways of collecting and processing the data, yet they share the same objectives of investigation, they overlap in the numerous spheres and only with the help of both the most full and comprehensive data can be generated. For some researchers it became a good tone to combine both for conducting the surveys and the others refuse to accept that kind of practice, taking them as two various dimensions, two various philosophies that should not be mixed in the one study.

Qualitative vs Quantitative Data Analysis

But what are the differences between quantitative and qualitative data analysis that make them particularly good or bad for some kind of research? Let’s take a brief look at the definition that may uncover the essence:

Quantitative Research. The main purpose of quantitative research and analysis is to quantify the data and assess it from the angle of numbers and other commonly adopted metrics. Such kind of approach gives the ability to generalize the examples let it be a separate sample of something or the entire population such. At the same time, such kind of research in most cases is followed by the qualitative research for specifying the studying the findings more closely.

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Qualitative Research. That kind of research is used for getting the larger, more closeup picture of the issue in order to understand something deeper and dig the problem until the cause is found. At the same time, the qualitative research may be a preceding one to the quantitative for generating ideas.

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qualitative and quantitative data analysis difference

Qualitative Analysis: Rich and Precise

The detailed picture that is rich of data and descriptions appears to be the ultimate purpose of conducting a qualitative analysis. If the data has identified the frequencies that are not assigned to the linguistic features and it happens that a rare phenomenon gets  more attention than the frequent one that might be counted as a problem in particular cases because of providing subjective data.

Qualitative analysis is multifaceted, it enables to draw the solid distinction between findings because for this kind of analysis the data doesn’t need to be restricted by the particular number of classifications. Ambiguity that the language creates for the qualitative analyses is inborn, natural feature of human language, however, it doesn’t distort the results of analysis, on the opposite it can bring deeper understanding, it can be pictured using the following example:

For instance “red” is normally signified as a color, in some cases, it can mean the political orientation, especially in the countries where the socialism or the communism adopted, thus

differences between quantitative and qualitative data analysis types

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in the qualitative analysis the both meanings take place if the “red flag” phrase exists, so in the qualitative analysis, more room left for interpretations. The disadvantages of the qualitative method involve the drawback related to the inability of applying the findings to the bigger scale and wider population groups using the same certainty degree, however, such thing is available for the quantitative analysis. The cause that brings such inconveniences is in the testing of the data that is not properly conducted, it is important to prove that the data that was found holds a statistical significance and doesn’t come as result of the random chance.

Quantitative Analysis: General, Steady and Reliable

For the quantitative analysis, the researcher needs to process the received data using the detailed set of classification and rules, before that the futures are classified, that helps to create the statistical models, reflecting the outcomes of the observation. Quantitative analysis is convenient because the research patterns can be applied to the larger scale and the larger populations of studied objects, that’s where the generalization takes place.

Such method can be called more objective as it skips the mere coincidences or events that happen randomly leaving the place for discovering what phenomena will likely take place in the future based on given research data. Quantitative analysis constructs the precise picture of the event occurrences, it can describe the normality and the abnormality of something that takes place in statistics media.

fast analysis of qualitative and quantitative data

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So the features of qualitative and quantitative analyses can be combined to get the perfect picture, the most objective and detailed one at the same time. While qualitative analysis idealizes the data causing opening the gap for the rare occasions in the research results the quantitative skips the rare and random events.

In order to strengthen your understanding of the qualitative and quantitative analyses go through the easy quest, containing 5 categorical data exercises.

Collaboration of Opposites: Analysis of Qualitative and Quantitative Data

Both qualitative and quantitative data analysis bear their own value and have features that can contribute the research results of each other and enrich the research results. The combined approach involving the both methods now gaining more and more popularity among the scientists all around the world it helps to reject the biases and eliminate the breaches of the both approaches creating broader ground for studying the objects groups.

principles of data analysis online

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The limitations of qualitative analysis

  • Does not generalize the population
  • Difficult for applying with statistical methods at times
  • Instruments of research affect the effectiveness

The limitations of quantitative analysis

  • Difficult to deal with new and undiscovered phenomenon (especially “why things happen” phenomenon)
  • Restricted by statistical designed, causes limited conclusion

Schmied (1993)qualitative and quantitative data analysis quiz has stated that both qualitative and quantitative analyses have something to contribute to science development. There hasbeen a recent move in social science towards multi-method use more than one method, and provide more comprehensive conclusion.

principles of data analysis tips

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Methods Make It Easy: Principles of Data Analysis

If you ever dealt with analyses it will be rather easy for you to go through all stages of research – from data collection to sorting and processing. It is very important to remember to take one step back from time to time in order to re-think the data gathered. Upon gaining the fresh look and new data understanding you will be able to sort and code information more successfully, reducing all unnecessary elements. Coding too many pieces of irrelevant data can take a serious negative toll on the time you spend on your research and lead to the distortions of the results. Before you started the research set the questions the resulting research should give the definite answers on, only replying to all of them will give your research its fullness. Apart of those questions you need to determine the key elements like:

  • Who conducts the research?
  • What are the research questions?
  • What is the research design?
  • When is the data collected?
  • Who are the participants of the research?
  • What analysis plan is used?
  • What are the findings?

Basically, the research moves through 4 big stages during which the researchers take the particular steps, defined by the research flow sequence. If you know where to get the qualitative analysis help the whole procedure will be very easy for you.

methods and principles of data analysis

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  • Upon gathering the data the reading and rereading process begins, as soon as you get familiarization with the material you will be able to find the initial patterns in the data. Primary and secondary nuances are discussed.
  • Data codification stage begins, information that you’ve gathered for the research should get codifying so that it becomes easier to manage, for this task the codebook is created where definitions, abbreviations, and exemplary quotes are included.
  • The data source trustworthiness verification. That stage implies that the data sources should be sorted and eliminated according to the initial standards set for the informational sources.
  • The data reducing stage that is based on the interpretation. The collected coded data should be ready and systematized for synthesizing your findings. As the result, the researcher should come up with new themes, taxonomies, and theories.

Analysis of qualitative and quantitative data is different. For getting the flexible and precise results for your research it is important to use reliable research methods and follow the instructions for the research conduction but that is not enough. The qualitative analysis provides good opportunities to gather the profound and extensive data for the research but does not generalize the population. The quantitative analysis causes limited conclusions as it ignores the additional factors for analysis so the better practice for researchers becomes combining advantages of both analyses.

Puzzled with qualitative and quantitative data analysis? Nothing easier than that when you do the research with our help!