Desk-Based Research Overview

A study that combines the results of many different systematic reviews on the same topic. Think of it like an umbrella that covers many smaller reviews.

A review that aims to understand how interventions work (or don't) in specific situations. It uses theory and explores how context influences the results.

A review that combines both quantitative and qualitative research to provide a more complete picture of a topic.

A fast and rigorous review that summarizes existing studies on a topic by following a systematic process but simplifying certain steps.

A careful review that aims to answer a specific research question by collecting and analyzing all relevant data.

A pre-defined plan outlining the objectives, criteria, and methods of a systematic review to reduce bias.

A comprehensive search for all relevant studies, including both published and unpublished ones, to avoid bias.

A bias that occurs when only studies with statistically significant results are published, leading to an incomplete picture.

A systematic review synthesizes existing research on a specific topic, identifying patterns, gaps, and relationships. It aims to understand the overall state of research on a topic and draw conclusions based on existing evidence.

A meta-analysis quantitatively integrates studies that are similar to each other, combining data to generate new results, conclusions, and uncover patterns not visible in individual studies.

A scoping review explores the size and scope of the research literature on a topic. It aims to identify the different types of research being done, the key concepts being addressed, and the gaps in knowledge. It is useful for understanding the landscape of a research area.

A literature review is a comprehensive summary and analysis of existing research on a specific topic. It aims to provide a critical overview of what is known about the topic, identify gaps in knowledge, and point to directions for future research.

Desk-based research is the process of gathering and analyzing existing data, primarily through online sources, to answer a specific research question.

The state-of-the-art review focuses on the current state of knowledge in a specific field. It aims to present the latest findings, trends, and debates within the field, and to provide a comprehensive overview of the current understanding of the topic.

The process of conducting a literature review involves defining the topic, searching for relevant information, assessing the quality of sources, and synthesizing findings to form a coherent narrative.

Data extraction is the process of collecting relevant information from multiple sources and organizing it in a standardized format. It aims to make the data comparable and ready for analysis.

The conditions that influence the effectiveness of an intervention.

The specific action or program being examined.

The processes or behaviors that explain how an intervention works.

The results or effects of an intervention.

Analyzing data across different studies to identify patterns and build theories about how interventions work.

Checking whether theories hold up across different contexts and studies.

Presenting findings in a clear and concise way, including recommendations and areas for further research.

A research design where the methods and procedures are predetermined before the study begins, allowing for replicable results.

A research design commonly used in healthcare to test new treatments, where participants are randomly assigned to different groups (e.g., treatment vs. control).

A research design similar to true experiments but without random assignment, making them easier to conduct in real-world settings.

A type of research design that uses structured questionnaires with pre-set questions to collect data from large groups.

The process of conducting a small-scale version of a study before committing to a full-scale version.

A research approach that aims to establish clear, reliable results that can be easily compared and analyzed statistically.

An initial stage of research where discussions and observations are used to identify potential mechanisms, contexts, and target participants.

A stage of fixed design research that uses highly focused methods like surveys and experiments to collect data.

A research design where the intervention is applied to a single participant repeatedly across different situations or phases (e.g., baseline, intervention, baseline again). Helps to ensure the intervention is causing the observed change.

A research design used to evaluate the effectiveness of an intervention across different participants, settings, or behaviors, starting at different times. Enables researchers to observe the impact of the intervention in various contexts.

Structured: Predetermined questions, like a survey. Semi-structured: Follows a guide, but allows for flexibility. Unstructured: Free-flowing conversation, like a casual chat.

Rich, detailed data, allows for the participant's voice to be heard, understanding of context and individual experiences.

Potential for interviewer bias, time-consuming, resources required, difficulty generalizing findings.

Building rapport, active listening, probing techniques, neutrality, asking open-ended questions, summarizing responses.

A method for gathering information from a targeted population, usually using questionnaires or surveys.

Focus groups are discussions with a group of people about a specific topic, aimed at understanding diverse perspectives and insights.

Differences between groups may exist before the intervention, affecting the results. For example, one group might have higher baseline skills than the other.

Events that occur during the study can influence outcomes. For example, a new policy change might affect everyone in the study, not just the intervention group.

Participants naturally change over time, which can affect the results. For example, students might grow academically even without new teaching methods.

Extreme pre-test scores may naturally return to average values over time. For example, students who score very high or low on a test might score closer to average on a later test.

This design is suitable when randomization isn't possible, but you need a baseline comparison to detect changes. For example, comparing two different schools using a standardized test before and after a new teaching method is introduced.

This design is useful to track the intervention effect over time using multiple data points before and after the intervention. For example, studying the impact of a new campaign on traffic accidents across different years.

This design is applicable when there's a clear cutoff for receiving the treatment or intervention. It helps establish cause-and-effect relationships without randomization. For example, determining the effect of a minimum wage increase on employment by studying workers just above and below the cutoff point.

This study is suitable for analyzing long-term trends and requires careful interpretation of findings. It is practical and feasible in real-world settings but can be prone to bias due to the lack of randomization.