Is Systematic Review Qualitative or Quantitative | Comprehensive Guide

In academic and professional research, a systematic review is a rigorous and highly valued approach to comprehensively synthesise existing evidence on a particular topic.

A systematic review is an exhaustive and detailed review of all existing studies and literature on a specified research question. It is designed to provide a complete, comprehensive summary of current evidence relevant to that question, drawing conclusions based on a wider array of information than any individual study might offer.

Understanding the difference between qualitative and quantitative research is essential when discussing systematic reviews, as each provides different insights and addresses different research questions. 

While quantitative research seeks to answer questions about ‘how much’ or ‘how many,’ providing measurable and numerical data, qualitative research delves deeper into understanding behaviours, perspectives, and experiences, offering rich, narrative-driven insights. Both methods have their strengths, and both can be of great value in answering varied research questions.

The methodology selected for a systematic review – whether qualitative, quantitative or even a combination of both – will significantly impact its findings and implications. The choice often hinges on the nature of the research question and the type of evidence available, among other factors.

What is a Systematic Review?

A systematic review is often described as the pinnacle of evidence-based research. It offers a comprehensive and objective method to summarise and synthesise existing research on a specific topic or question.

Systematic Review Definition

A systematic review is a rigorous and structured approach that collates all empirical evidence fitting pre-specified eligibility criteria to answer a specific research question. It employs explicit, systematic methods that are selected with a view to minimising bias, thus providing more reliable findings from which conclusions can be drawn and decisions made.

Here are the key features of a systematic review. 

Predefined Criteria

Before embarking on a review, researchers set clear inclusion and exclusion criteria for studies, ensuring that the review process is transparent and replicable.

Comprehensive Literature Search

Researchers conduct an exhaustive search of multiple databases and often grey literature (unpublished or non-peer-reviewed articles) to ensure all relevant studies are considered.

Critical Appraisal

Included studies are critically assessed for their quality and relevance. This can involve evaluating study design, methods, sample size, and potential biases.

Data Extraction

Relevant information from each included study is extracted systematically, often using standardised forms or software.

Synthesis of Findings

Results are synthesised, either qualitatively (descriptive synthesis) or quantitatively (meta-analysis), to draw overarching conclusions about the research question.

The Difference Between Qualitative and Quantitative Research

It is important to understand the difference between qualitative and quantitative research for scholars, researchers, and even consumers of research. Each approach has its own strengths, methodologies, and purposes and offers different perspectives and insights on a research question.

Qualitative Research

Qualitative research focuses on exploring and understanding the deeper meanings and interpretations of human behaviour, experiences, and social phenomena. It is concerned with the ‘why’ and ‘how’ of decision-making rather than just the ‘what,’ ‘where,’ or ‘when.’

It has the following characteristics. 

Data Type

Uses non-numerical data like texts, interviews, images, or observations.

Subjectivity

Emphasises individual experiences, making them inherently subjective.

In-Depth Exploration

Aims to capture the depth and richness of human experience.

Flexible Design

The research design can evolve based on data collection, allowing for new themes and patterns to emerge.

Inductive Approach

Begins with specific observations and moves towards broader generalisations and theories.

Examples of Qualitative Research

• Ethnographic studies are where researchers immerse themselves in a community to understand their customs and behaviours.
• Phenomenological research explores the lived experiences of individuals, such as the experience of mothers going through post-partum depression.
• Grounded theory studies aim to generate new theories based on the data collected.

Quantitative Research

Quantitative research is concerned with quantifying phenomena and producing numerical data. It seeks to establish patterns, relationships, or causal links, often using statistical methods to analyse the data.

Some of the characteristics of quantitative research include the following.

Data Type

Uses numerical data, which can be measured and counted.

Objectivity

Emphasises objectivity and reduction of subjective bias.

Generalisability

Often uses larger sample sizes, aiming for generalisations about a population based on the sample.

Fixed Design

The research design is established prior to data collection and remains consistent.

Deductive Approach

Starts with a hypothesis or theory and tests it through data collection.

Examples of Quantitative Research

• Surveys that measure customer satisfaction levels on a scale of 1-10.
• Experiments that test if a new drug is more effective than an existing one using controlled conditions and random assignments.
• Correlational studies examine the relationship between two variables, such as the link between smoking and lung cancer.

Types of Systematic Reviews

Systematic reviews come in different forms, each tailored to accommodate the varied types of primary research and answer specific research questions. The three primary categories are qualitative, quantitative, and mixed-methods systematic reviews. Let’s dive into each of them.

Qualitative Systematic Reviews

• Purpose: To synthesise qualitative evidence, aiming to understand phenomena in depth and in context.
• Significance: Offers a comprehensive understanding of experiences, behaviours, and social processes, revealing patterns, themes, or theories from various qualitative studies.

Process and Methodologies Used

• Meta-Ethnography: Pioneered by Noblit and Hare in 1988, this method involves translating studies into one another to provide a higher-order interpretation.
• Framework Synthesis: This employs a priori frameworks to categorise findings, making it particularly useful for policy-related reviews.
• Thematic Synthesis: This involves coding textual data and then grouping these codes into descriptive themes, which are then developed into analytical themes.

Pros

• Deeper Insights: Qualitative systematic reviews allow for an in-depth exploration of a topic, providing rich details and understanding of participants’ experiences.
• Contextual Understanding: They offer insights into the cultural, societal, and personal contexts in which phenomena occur.
• Interpretation of Complex Phenomena: Complex issues, like motivations, beliefs, or perspectives, can be explored and understood more holistically.
• Adaptive and Flexible: They can evolve based on emerging data, allowing new patterns or themes to be identified.

Cons

• Subjectivity: Findings can be influenced by researcher biases or perspectives.
• Limited Generalisability: The insights gained, while deep, often come from smaller sample sizes, making them less generalisable to broader populations.
• Time-Consuming: The process can be lengthy due to the need to interpret and synthesise textual or narrative data.
• Requires Specialised Skills: Effective qualitative analysis demands a nuanced understanding and specific expertise.

Quantitative Systematic Reviews

• Purpose: To aggregate quantitative results from multiple studies to provide an overall answer to the research question.
• Significance: Delivers statistically robust conclusions, can determine the effectiveness of interventions, and provides evidence for policy and practice.

Process and Methodologies Used

• Meta-Analysis: A statistical technique that combines results from multiple studies to identify an overall trend or effect. When done appropriately, it provides a more precise estimate of effect.
• Pooled Analysis: Individual-level data from several studies are statistically combined to analyse as if they were from one large study, providing greater statistical power.

Pros

• Statistical Strength: By pooling data from multiple studies, they offer robust statistical analyses that can provide definitive insights.
• Generalisability: Typically involves larger sample sizes, allowing for broader generalisations about populations.
• Objectivity: Emphasise measurable and replicable data, leading to more objective conclusions.
• Clear Outcome Measures: Quantitative metrics can clearly indicate the effectiveness or impact of interventions.

Cons

• Overlooks Nuances: The focus on numerical data might mean missing out on nuanced insights that qualitative data might offer.
• Restricted to Numerical Data: They are limited to questions that can be addressed using numerical metrics, potentially leaving out context.
• Susceptible to Publication Bias: Studies with significant findings are more likely to be published, and thus included in reviews, potentially skewing results.
• Requires Homogeneity: Combining data from different studies (e.g., in a meta-analysis) demands a level of similarity or homogeneity among those studies, which might not always be present.

Mixed-Methods Systematic Reviews

These reviews incorporate both qualitative and quantitative evidence. They acknowledge that some research questions are best addressed by different types of evidence and harness the depth of qualitative findings and the generalisability of quantitative results.

Rationale: By combining methodologies, mixed-methods reviews provide a fuller understanding of research questions. They can explore not just whether an intervention works (quantitative) but also how and why it works (qualitative).

Challenges: Integration demands careful consideration of how the distinct data types can be synthesised. Differences in study quality assessments, the potential for increased complexity, and the need for expertise in both methodologies are additional challenges.

Real-World Examples and Applications of Systematic Review

Systematic reviews are valuable tools in a variety of fields, but they are particularly influential in areas like healthcare, where decisions can have profound impacts on people’s lives. Let’s explore some real-world applications of each type of systematic review:

Qualitative Systematic Review

Example: Exploring patients’ experiences with a specific medical procedure

Application: Imagine a scenario where a new minimally invasive surgical procedure has been introduced for the treatment of a specific heart condition. While clinical trials have shown its safety and efficacy, little is known about patients’ experiences undergoing the procedure – their fears, perceptions, post-operative recovery, and overall satisfaction.

A qualitative systematic review might gather data from various qualitative studies conducted in different settings where patients were interviewed or observed post-procedure. Through this review, themes regarding patients’ anxieties, their pain management experiences, interactions with healthcare providers, and overall satisfaction with the procedure outcomes can be synthesised.

Outcome: Such insights can guide healthcare professionals in improving patient care, refining the procedure, or tailoring pre- and post-operative counselling to better meet patients’ needs.

Quantitative Systematic Review

Example: Comparing the effectiveness of two drugs based on multiple clinical trials

Application: Suppose two antiviral drugs, Drug A and Drug B, are available for treating a viral infection. Multiple clinical trials have been conducted globally to test the efficacy of each drug, but results vary across these studies.

A quantitative systematic review can pool data from all these clinical trials, comparing the effectiveness of the two drugs. By statistically analysing outcomes from thousands of patients, the review can provide a more definitive answer about which drug is more effective or if both have comparable efficacy.
Outcome: Such a review can shape clinical guidelines, informing doctors about the best treatment choice and influencing pharmaceutical research and drug pricing.

Mixed-Methods Systematic Review

Example: Investigating the efficacy and patient perceptions of a new therapy

Application: Consider a new cognitive behavioural therapy introduced for managing chronic pain. While clinical studies using quantitative measures (like pain scales) can evaluate its efficacy, qualitative studies might explore patients’ experiences and perceptions of the therapy.

A mixed-methods systematic review will synthesise findings from both types of studies. It can provide statistical data on the therapy’s efficacy in reducing pain and qualitative insights into how patients perceive the therapy, its impact on their daily lives, and any challenges they face.

Outcome: Such a comprehensive understanding can aid clinicians in deciding when and how to use the therapy, and it might also guide refinements in the therapeutic approach to better serve patients’ needs.

Is a Systematic Review Qualitative or Quantitative?

Navigating research often requires making pivotal decisions about the methodologies employed. When undertaking a systematic review, the choice of method – qualitative, quantitative, or mixed – can significantly influence the outcomes and insights gained. Let’s talk about how researchers can make this important choice:

Factors to Consider

• Research Question: The foundational step. Is your question aiming to measure and quantify (e.g., “How effective is Drug X compared to Drug Y?”) or to understand and interpret (e.g., “How do patients feel about their treatment experience with Drug X?”)?
• Available Data: Are there numerous qualitative studies available on the topic, or are there primarily quantitative clinical trials? Sometimes, the nature of existing research can guide your review method.
• Desired Outcomes: Do you want statistical data to guide clinical decisions, or are you looking for insights to inform patient counselling, education, or further qualitative research?
• Resources: Quantitative reviews, especially meta-analyses, might require specific statistical software and expertise. Qualitative reviews demand rigorous interpretative skills and sometimes more time to sift through narrative data.
• Expertise: Your team’s skill set can influence your choice. A team proficient in qualitative analysis might lean towards a qualitative systematic review and vice versa.

The Role of Research Objectives in Dictating the Type of Systematic Review

Your research objectives act as a compass, guiding the direction and depth of your inquiry.

• Quantitative Objectives: If your primary objective is to determine the efficacy of an intervention, measure the prevalence of a condition, or ascertain any form of numerical data, then a quantitative systematic review is likely more suitable. These objectives seek measurable, often generalisable, outcomes.
• Qualitative Objectives: If your aim is to understand experiences and perceptions or explore phenomena without pre-set hypotheses, then a qualitative systematic review can be more fruitful. Such objectives delve into the ‘why’ and ‘how’ rather than just the ‘what’.
• Mixed Objectives: For more comprehensive research goals that require both numerical outcomes and in-depth insights, a mixed-methods review becomes relevant. For instance, if you aim to determine the effectiveness of a new therapeutic intervention (quantitative) and understand patients’ experiences with it (qualitative), a mixed-methods approach is ideal.

Torreto Marwano, ellie cross,