AP Research Design

basic research

Focuses on expanding knowledge without immediate practical application. It explores fundamental principles and lays the groundwork for future applied research.

applied research

Aims to solve specific, real-world problems using insights from basic research. It's commonly used in fields like medicine and engineering to improve practices.

quantitative

Involves collecting and analyzing numerical data to identify patterns and test hypotheses. It produces objective results that can be generalized to larger populations.

qualitative

Explores subjective experiences and meanings through non-numerical data. Methods like interviews and focus groups provide deep insights into complex

mixed methods

Combines quantitative and qualitative approaches for a comprehensive understanding of a research problem, offering both broad and detailed perspectives.

Descriptive

Provides a detailed snapshot of a population or phenomenon, focusing on what exists without exploring causes.

correlational

Examines relationships between variables to identify associations, but it doesn't establish

experimental

Manipulates variables in a controlled environment to establish cause-and-effect relationships.

explanatory

Seeks to explain why and how certain outcomes occur, building on descriptive and exploratory research to uncover underlying mechanisms.

exploratory

Investigates poorly understood phenomena to generate ideas and hypotheses for future

descriptive rq

Describes characteristics (e.g., "What factors affect the

achievement gap in urban schools?").

comparative rq

Compares groups or variables (e.g., "How does teacher

training impact math performance?").

relational rq

Explores relationships (e.g., "What is the relationship between

diet and heart health?").

causal rq

Examines cause-and-effect (e.g., "How does interface design

influence app usability?").

how to develop an rq

identify broad area of interest —> conduct lit review —> narrow your focus —> identify specific research problem —> formulate rq —> refine rq —> finalize rq

null hypothesis

Claims there is no relationship or effect between the variables.

alternative hypothesis

Suggests that a relationship or effect is present.

non-directional hypothesis

Recognizes a relationship exists but does not predict its direction.

Hypothesis

testable proposition that anticipates how variables will interact, essentila for guiding research and ensuring validity

how to develop a hypothesis

Review problem and RQ —> Identify variables —> predict relationships between variables —> choose type of hypothesis —> formulate clear and precise hypothesis —> ensure testability —> refine and revise —> finalize

literature review

backbone of any research project, looks at existing body of work in the field, looks at what has already been discovered and where gaps lie, forms foundation of your own study

purposes of literature review

Establishes context, identifies gaps, prevents duplication, informs research methodology, builds credibility,

how to conduct lit review

ID RQ —> define inclusion and exclusion criteria —> select databases —> ID key search terms —> extract data from studies —> retrieve and screen items from databases —> develop search strategy —> synthesize results —> risk of bias assessment —> report

eligibility criteria

publication years, language, types of studies, geographical region, participant characteristics, and intervention characteristics

snowballing

searching reference lists of studies to find other similar studies or keywords

Typical number of databases to be searched

2-3

Risk of bias and quality appraisal

crucial for ensuring transparency and generalizability of the results