Neal Kaplan I'm a director of technical communications working for a data analysis startup in Redwood City. I started as a technical writer, and since then I've also been learning about information architecture, training, content strategy, and even something about customer support. I'm also passionate about cross-team collaboration and user communities.

What are the challenges in quantitative research?

1 min read

  • Improper representation of the population.
  • There is an inability to control the environment.
  • There were limited outcomes in a quantitative research.
  • It’s time consuming and expensive.
  • It is difficult in data analysis.

In the article, it was mentioned that improper representation of the target population might be a problem for the researcher. A study claims to check the proportion of female between the ages of 20 and 30 applying make-up ranges of international brands. The target population in this case is women with both professional and non- professional background who reside in Delhi.

It is possible to get partial information about the range of makeup products from the sample. Government or non-government organisation, public service providers, educational institutions are some of the interested parties in many developing countries. If the data for a study is collected on residents perception of development works, the results will be held redundant or of limited value in 2015.

This type of research is carefully planned to ensure complete randomization and correct designation of control groups. If you want to get in-depth responses on an issue, data collection in quantitative research methodology is often too expensive. To understand the influence of advertising on the propensity of purchase decision of baby foods parents of 5-year old and below of Bangalore, the researcher needs to gather data from 200 respondents.

For example, to understand the level of motivation perceived by Grade 5 students from the teaching approach taken by their class teachers, mere yes and no might lead to ambiguity in data collection and hence improper results.

What makes quantitative method challenging?

Quantitative analysis requires high-quality data in which variables are measured well, meaning the values of the variables must accurately represent differences in the characteristics of interest, which can be difficult to conduct research on.

What are the challenges of qualitative research?

Lack of attention to the philosophical underpinning of qualitative methodology, inconsistency between research question and methodology, and insufficient methodological knowledge are some of the important challenges.

What are the downside challenges you encounter as you study quantitative research?

The focus on numbers found in quantitative research can be limiting. There is a false focus on numbers. Quantitative research can be limited in its pursuit of concrete, statistical relationships which can lead to researchers overlooking broader themes.

What is the weaknesses of quantitative research?

  • Limitations of strength.
  • Difficult to understand the context of a phenomenon is easy to analyse.
  • Data can be very reliable and accurate.

Quantitative data are pieces of information that can be counted and which are usually gathered by surveys from large numbers of respondents. Smaller numbers of tools and open-ended questionnaires are used by participants in qualitative studies. In the next lesson, learn more about using qualitative and quantitative approaches in studies.

What are the weaknesses of qualitative research?

The main weaknesses of qualitative research are focused on the individuals. Subjectivity, influence of personal biases and connection to research theory are all potential issues. Numbers can tell their own stories.

What is the 5 weakness of quantitative research?

Improper representation of the population. It is possible to control the environment. There were limited outcomes in a quantitative research.

Neal Kaplan I'm a director of technical communications working for a data analysis startup in Redwood City. I started as a technical writer, and since then I've also been learning about information architecture, training, content strategy, and even something about customer support. I'm also passionate about cross-team collaboration and user communities.

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