The planning group
Quantitative method
We use quantitative methods when the purpose is to investigate conditions that can be quantified as numerical data, that is, what can be counted. Qualitative data provides answers to questions about “how much, how many, how often, how satisfied” and so on.
Quantitative analysis is a systematic approach to collecting, evaluating, and interpreting numerical data to uncover patterns, relationships, and trends. The method is characterized by statistical techniques that allow for informed decisions based on empirical evidence. Quantitative methods minimize bias, providing a more objective and accurate analysis that is essential for decision-making.
Quantitative methods increase the reliability of results through standardized data collection techniques that allow for replication of studies. Quantitative analysis can handle large data sets, allowing conclusions to be drawn that can be generalized, provided the sample is representative.
Examples: questionnaire surveys, telephone interviews, data analyses.
We use qualitative methods when the purpose is to explore complex topics, obtain detailed information, understand context-specific conditions, or test theories and concepts. Qualitative methods provide answers to questions such as what, how, and why.
Qualitative methods focus on non-numerical data, such as words, images, and observations. This allows for a more nuanced understanding, capturing the richness of human experience that numbers cannot convey. Qualitative methods allow for the exploration of complex topics and deeper insights into behavior and experiences.
Qualitative analysis methods are characterized by their focus on non-numerical data, open-ended inquiries, subjectivity, contextual understanding, flexibility, depth of exploration, inductive reasoning, and participant-centered approaches.
Examples: open questions, focus groups, interviews, observations, content analysis.
We use combined methods when we want to take advantage of the strengths of both methods, validate results, and deepen information for more complete insight.
Combining qualitative and quantitative research methods can increase the depth and breadth of studies and provide a more comprehensive understanding of complex phenomena. Combined methods thus make it possible to take advantage of the strengths of both methods, for a more comprehensive understanding, or if one wishes to validate and elaborate on findings.
Integrating methods provides the opportunity to capture trends while simultaneously exploring underlying causes and relationships. Combining qualitative and quantitative data can help mitigate the weaknesses inherent in each method. For example, qualitative insights can provide context to quantitative findings, while quantitative data can help validate qualitative observations.
Example: questionnaire + focus groups.
We collect and analyze primary data. Primary data is something that is collected for you, with a specific purpose. Data collection can be done through interviews, questionnaires, observations, experiments or focus groups. The advantage of primary data is high targeting, relevance and quality control.
Primary data is relevant when a project requires data that does not already exist or needs to be collected in a specific way. Primary data can also be relevant if more information and a deeper understanding of a topic is needed.
Example: interviews, questionnaires, observations, experiments, focus groups.
We collect and analyze secondary data. Secondary data is something that already exists and is available from other sources, be it internal or external data. The advantage of secondary data is that it is often cheaper and quickly available.
Secondary data is relevant when there is limited time or financial resources available, or when the problem requires a preliminary study before in-depth primary data collection. Secondary data is also relevant if data over a longer period of time is needed, which can be used to identify development trends. In addition, secondary data can be used to supplement and validate primary data.
Example: public databases, previous research studies, statistics from authorities, the organization's internal data, previous reports.
We use meta-analyses when there are many existing studies on a topic and a synthesis of results is needed to obtain a more precise assessment. The advantage of meta-studies is often high statistical power and generalizability.
Meta-analyses combine results from multiple studies to obtain a more robust conclusion on a topic by combining results from different studies. The sources can be literature, surveys, reports, and scientific publications.