What are the key differences between qualitative and quantitative designs?

In the realm of Business Intelligence (BI), understanding the differences between qualitative and quantitative designs is essential for data analysis. Qualitative data refers to non-numerical information that captures concepts, opinions, and experiences. It's often descriptive and collected through interviews, focus groups, or observation. This type of data is invaluable for gaining insights into behaviors, motivations, and the 'why' behind certain trends. On the other hand, quantitative data is numerical and can be measured or counted. It's typically gathered through surveys or experiments and is used to quantify problems, opinions, or other defined variables. This data is crucial for statistical analysis and making data-driven decisions.

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1 Data Nature

Qualitative designs deal with data that is inherently subjective and rich in narrative. You'll encounter words, images, and objects that require interpretation and thematic analysis. These designs thrive on context and the depth of understanding they provide about human behavior, social phenomena, or organizational culture. In contrast, quantitative designs focus on data that can be objectively measured and expressed in numbers. The goal here is to classify features, count them, and construct statistical models to test hypotheses. This approach is ideal for identifying patterns or making generalizations about a larger population.

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Boubacar Poty DIALLO

Monitoring & Evaluation Officer. chez Amref Health Africa, West Africa Hub
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Je suis en phase avec la définition des deux concepts présentés. Cependant il faut aussi dire que ces deux approches se complètent dans la mesure où le quali. permet de renforcer l'analyse des grands constats notés par le quanti. Dans ce cadre figure on obtient une bonne compréhension de la situation et donc de prendre de meilleures décisions.

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2 Data Collection

When collecting qualitative data, you engage with smaller, non-random samples where the depth of information is paramount. Techniques like in-depth interviews or participant observation allow you to uncover the layers of meaning behind human interactions. Quantitative data collection, however, often involves larger, random samples that provide a basis for generalizing results to a broader population. Surveys with closed-ended questions or structured experiments are common methods that yield quantifiable insights.

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Boubacar Poty DIALLO

Monitoring & Evaluation Officer. chez Amref Health Africa, West Africa Hub
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Je trouve très limitative de dire que le quali. requière une taille d'échantillons très petite. Pour moi ça depend de l'objectif suivi et les ressources disponibles même si les deux approches demandent de taille différente. Si l'on cherche juste de comprendre un phénomène donné dans un sous ensembles alors le quali. est plus indiqué. Toutefois, il faudra très bien choisir ça population clé à interroger.

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3 Analysis Approach

The analysis of qualitative data is interpretive and subjective. You might code textual or visual data and look for recurring themes or narratives. The process is iterative and non-linear, often involving constant comparison and reflection. Contrastingly, quantitative data analysis employs statistical methods. You use mathematical models to analyze numerical data, looking for statistically significant differences or correlations. This type of analysis is more linear and structured, aiming to test specific hypotheses or predictions.

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Boubacar Poty DIALLO

Monitoring & Evaluation Officer. chez Amref Health Africa, West Africa Hub
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Une des choses que j'ai trouvé trop intéressant est la mise en relation de modèle qui combine à la fois des données quali. et quantitative.

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4 Research Objectives

Qualitative research designs are exploratory. They aim to understand the underlying reasons and motivations behind certain behaviors or phenomena. This design is useful when you need to develop theories or when the area of study is not well understood. Quantitative research designs, however, are conclusive. They seek to quantify problems and predict outcomes based on the data collected. This design is most appropriate when the research question is clear and you aim to measure the extent of something.

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5 Outcome Interpretation

Interpreting outcomes from qualitative research involves constructing meaning from the data. You synthesize data into a narrative that explains the nuances of the subject under study. These outcomes often lead to more questions and further exploration. In quantitative research, outcomes are interpreted through statistical analysis, providing a clear-cut answer to research questions. The results are often presented in graphs or tables, offering a visual representation of the data.

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6 BI Applications

In Business Intelligence, qualitative designs can provide context to the numbers that quantitative methods yield. You might use qualitative data to understand customer sentiment or to explore new market trends that haven't been quantified yet. Quantitative designs are used to measure performance indicators, forecast sales, or understand customer demographics. Both approaches are complementary; qualitative data can inform the creation of quantitative surveys, while quantitative data can highlight areas where qualitative insights are needed.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the key differences between qualitative and quantitative designs?

1

2

3

4

5

6

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1 Data Nature

2 Data Collection

3 Analysis Approach

4 Research Objectives

5 Outcome Interpretation

6 BI Applications

7 Here’s what else to consider

Business Intelligence

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What are the key differences between qualitative and quantitative designs?

1

2

3

4

5

6

7

1 Data Nature

2 Data Collection

3 Analysis Approach

4 Research Objectives

5 Outcome Interpretation

6 BI Applications

7 Here’s what else to consider

Business Intelligence

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