APR design part 2

Interviews

(qualitative) involve direct, face-to-face interactions between the researcher and the

participant, valuable for obtaining detailed personal accounts and

insights into participants' thoughts, feelings, and behaviors, can be structured or semi-structured

Focus Groups

(qualitative) involves gathering a small group of participants to discuss a

specific topic or set of issues, particularly useful for exploring collective views and

experiences, identifying common themes, and generating new ideas through

group dynamics.

Qualitative strengths

rich detailed data on experiences and perspectives
captures complexity of phenomena and nuances of experiences
flexible and adaptable

Qualitative limitations

time consuming to collect data
potential for researcher bias when it comes to interpreting
Challenging to generalize to larger populations

quantitative methods

involves collection and analysis of numerical data to identify patterns, trends, or relationships
effective for testing hypotheses, predicting outcomes, or generalizing findings
very objective, numerical analusis

Surveys

(quantitative) involve administering structured questionnaires to a large sample of

respondents to gather data on attitudes, behaviors, characteristics, or

opinions; can be conducted online, face to face, telephone, or by mail; involve administering structured questionnaires to a large sample of

respondents to gather data on attitudes, behaviors, characteristics, or

opinions.

Experiments

(quantitative) controlled studies where researchers manipulate one or

more independent variables to observe their effect on dependent variables; highly valued for establishing cause and effect; minimize external influence; random assignment

Ethnography

(qualitative) entails the researcher immersing themselves in a community

or organization to observe and understand its culture and practices, involves participant observation, provides a holistic view of the social dynamics, behaviors, and

interactions within a specific context, offering deep insights into the cultural

and social aspects of the phenomenon under study.

Observational studies

(quantitative) involve systematically observing and recording

behaviors or events in a natural or controlled setting without manipulating

any variables; useful for studying phenomena as they

naturally occur, particularly useful in fields like

psychology, sociology, and education.

Secondary Data Analysis

(quantitative) involves analyzing existing data that was collected by other researchers or organizations; utilize large datasets that are often comprehensive and representative of larger populations; cost-effective and time-efficient,

Quantitative strengths

Generalizability since it uses large random samples
Objectivity due to structured approach and statistical analysis
Reliability due to standardized procedures
Efficiency of collecting data

Quantitative limitations

Lack of depth - doesnt capture complexity
Rigidity - often inflexible methods
Context ignorance - may overlook factors that infleunce behavior and attidtudes
Assumptions - stat. analysis relies on assumptions that could affect validity if violated

Mixed Methods research

combines qualitative and quantitative approaches to offer a comprehensive understanding of a research problem; combines qualitative and quantitative approaches to offer a comprehensive understanding of a research problem.

Sequential Explanatory Design

(mm) quantitative data is collected and analyzed first, followed by qualitative data collection and analysis; used when the goal is to explain or build upon initial quantitative results with qualitative data.

Sequential Exploratory Design

(mm) starts with qualitative data collection and analysis, followed by quantitative data collection and analysis, used when the goal is to explore a phenomenon and then test the findings quantitatively.

Concurrent Triangulation design

(mm) qualitative and quantitative data are collected simultaneously but analyzed separately —> findings are compared and contrasted; used to validate and corroborate

results from both data types.

Concurrent Embedded Design

(mm) involves collecting both qualitative and quantitative data simultaneously, but one type plays a supportive role;

MM strengths

Comprehensive understanding provides a fuller picture
Triangulation cross verifies data through different methods
Flexibility
Depth and Breadth by combining methods

MM limitations

Resource intensive - time consuming and costly
Complexity due to interpreting both types of data
Potential for conflicts - due to data leading to conflicting results
Technical Skills - requires proficiency in both methods

The right methodology

shapes research design by guiding data collection, analysis, and interpretation
well chosen design ensures rigor, validity, and minimizes bias

Bad methodology

leads to flawed conclusions and undermines integrity

How to choose the right method

Understand RQ and problem —> review literature —>consider nature of RQ —> evaluation resources and constraints —> assess ethical implications —> make an informed decision —> plan research design —> seek feedback and refine —> implement and monitor

Data collection

directly impacts validity, reliability, and success of the research and objectives,

Sampling techniques

involves selecting a subset of individuals or elements from a larger population to represent the entire group.

random sampling

a research technique where a subset of individuals is randomly selected from a larger population so that every member has an equal and fair chance of being chosen

stratified sampling

Divide population into subgroups and sample from each to ensure representation.

Cluster sampling

- Divide population into clusters and randomly select

clusters.

Purposive sampling

Select participants based on specific characteristics

relevant to the study.

Snowball sampling

- Use participant referrals to reach additional participants.

Convenience sampling

- Select participants who are readily available and willing to participate.

Quota sampling

- Fill quotas for different subgroups based on specific

characteristics.

Data analysis

transforms raw data into meaningful insight

Descriptive stats

summarizes and describes main features of data (mean, median, mode, range, SD), doesnt infer beyond data or examine relation

Inferential stats

Makes inferences about a population based on a sample, used when testing hypotheses or making predictions (t-tests, anova, chi-square), allows generalization

T-tests

compares means of two groups to see if they are statistically different

ANOVA

compares means of three + groups

Regression analysis

examines relation between dependent and independent variables, used for predicting outcomes and id relationships

chi-square test

assess association between categorical variables

Multivariate Analysis

Examines relationships between multiple variables simultaneously

Thematic analysis

ids themes or pattern in qualitative data

Content analysis

systemically categorizes textual info to id trends patterns or biases

Grounded theory

develops theories based on data collect, involving refining theories through comparison

Narrative analysis

explores and interprets stories and personal accounts to understand experineces and perspectives

Sequential Analysis

analyzes data in phases, where findings from one phase inform the next

Concurrent analysis

simultaneously analyzes quali and quant data, comparing and contrasting results

Factor analysis

reduces data dimensionality by iding underlying factors that explain patterns in the data

Multiple regression

predicts valye of a dependent variable based on multiple independent variables

Cluster analysis

groups people or items into clusters based on similarity, reveals natural grouping s in data

Path analysis

examines causal relationships among variables through direct and indirect effects (testing theoretical models)

Meta analysis

combines results from multiple studies to id overall trends and effects

Validity

accuracy and truthfulness of measurements
the extent to which a research study measures what it intends to measure.

reliability

consistency of measurements

methods to ensure validity

control groups, randomization, representative sampling, replication, field experiments, clear definitions, pilot testing, etc

writing

contains title page, abstact, intro, lit review, methods, results, discussion, conclusion, references, and appendices,

presenting practices

clarity and conciseness, visual aids, organization, audience engagement, practice and prep