SYSTEMIC REVIEW

Faculty Of Dentistry

Topic Title:
Methodology
Under Supervision By
Dr. Nehal El-mashad

Presented By
Aiman Al-Juwaihi 200049341
Fathi Jamal Alfaran 200049396
Talal Al-Amoudi 200049570
Amrou Aiman Awad 200049243
 Objectives

• Choosing the Right Study Design

• Research Design
• Inclusion and Exclusion Criteria
• Sample Size
• Data Analysis
 Introduction To Methodology
The methodology section of a research study
outlines the approach, procedures, and techniques
used to collect and analyze data. It serves as the
blueprint for the research process, ensuring clarity,
transparency, and reproducibility. This section
provides the rationale for selecting specific methods
and explains how they align with the research
objectives and questions.
 Choosing the Right Study Design

Key Factors to Consider:

• Aim of the Study: Define your research
question clearly.
• Design Pros and Cons: Assess the
strengths and limitations of different study
designs.
• Resources: Account for available time,
budget, and personnel.
• Ethical Considerations: Ensure ethical
practices, particularly for intervention
studies.
 Choosing the Right Study Design
Broad Study Types:
• Interventional Studies: Ideal for testing interventions in
clinical research (e.g., RCTs).
• Observational Studies: Useful for natural observations
without manipulation (e.g., cohort, case-control).
Specific Study Selection Tips:
• Use Case-Control or Retrospective Cohort Studies if:
- The outcome is rare.
- There’s a long time lag between exposure and outcome
(long induction/latent period).
- The population is dynamic or difficult to track (e.g.,
transient or changing locations).
• Use Prospective Cohort Studies if:
- The population is stable and allows long-term tracking
for exposure-outcome analysis.
 Research Design
Research design is the blueprint for conducting a
study, detailing how data will be collected, measured,
and analyzed to address research questions or test
hypotheses. It ensures the study is systematic, reliable,
and valid.

Key Components:
• Research Objectives: Clearly defined goals and
questions that guide methodology and scope.
• Data Collection Methods: Techniques like surveys,
interviews, or experiments, using primary or
secondary data.
• Sampling Strategy: Defines participant selection,
including population, sampling methods (e.g.,
random, stratified), and sample size.
 Research Design
• Measurement Tools: Instruments (e.g., questionnaires) ensuring reliability (consistency) and
validity (accuracy).
• Data Analysis Plan: Specifies analytical methods like statistical or thematic analysis.
• Ethical Considerations: Addresses informed consent, confidentiality, and minimizing harm.

Types of research design:

• Qualitative: Explores phenomena in depth using interviews or case studies.
• Quantitative: Focuses on numerical data and statistical analysis through surveys or
experiments.
• Mixed-Methods: Combines qualitative and quantitative approaches for comprehensive
understanding.
 Research Design
Classification of research design:
• Descriptive: Observes and describes phenomena
without analyzing causal relationships.
• Exploratory: Examines new topics to generate
hypotheses, often using qualitative methods.
• Explanatory: Investigates relationships between
variables using quantitative methods.
• Experimental: Tests cause-effect relationships with
control groups and randomization.
• Longitudinal: Studies subjects over time to observe
changes.
• Cross-Sectional: Examines populations at a single
point to identify patterns.
 Research Design
Steps to Develop a Research Design:
• Define the problem and objectives.
• Choose qualitative, quantitative, or mixed methods.
• Identify independent, dependent, and control variables.
• Select sampling methods (probability or non-probability).
• Develop reliable, valid data collection tools.
• Plan data analysis techniques aligned with research
objectives.
Importance of Research Design:
• Ensures rigor and systematic execution.
• Enhances validity by reducing bias.
• Facilitates replication by clearly defining methods.
• Guides decision-making in choosing methods and tools.
 Inclusion and Exclusion Criteria
Inclusion and exclusion criteria are essential in
defining the study population, ensuring the
research aligns with objectives, maintains
validity, and produces reliable results.

Inclusion Criteria
Purpose:
• Ensure participants align with study goals.
• Minimize variability for more reliable
findings.
• Define a manageable and focused population.
 Inclusion and Exclusion Criteria
Examples:
• Demographics: Age, gender, ethnicity, or location.
• Health Conditions: Presence of a specific diagnosis or disease.
• Behaviors: Smoking status or dietary habits for lifestyle research.
• Time Frame: Participant availability during the study.

Challenges:
• Overly restrictive criteria may limit diversity and reduce the generalizability of results.
• Balancing focus with sufficient participant recruitment.
 Inclusion and Exclusion Criteria
Exclusion Criteria
Purpose:
• Remove factors that could distort the data or outcomes.
• Protect participants from harm or undue burden.
• Maintain focus on the core research objectives.

Examples:
• Confounding Factors: Other medical conditions that could interfere with results.
• Medication Use: Drugs that might interact with study interventions.
• Demographic Restrictions: Age or professions introducing bias.
• Non-Compliance Risks: Barriers like literacy, language, or inability to follow protocols.
 Inclusion and Exclusion Criteria

Challenges:
• Excessive exclusions may lead to
recruitment difficulties.
• Narrow criteria can reduce external
validity, limiting the broader
applicability of results.
 Sample Size

Definition of Sampling:
• Procedure by which some members of a
given population are selected as
representatives of the entire population.
OR
• The process of selecting a number of
participants for a study in such a way that
they represent the larger group from which
they were selected.
 Sample Size

Characteristics:
A ‘good’ sample is one that:
• closely resembles the population, i.e. it is representative of the population from which it was
selected.
• should be sufficiently large to minimize sampling variation.

What we need to know:

Population: all members of a specified group.
• Target population: population to which the researcher wants to generalize the results to.
• Accessible population: population to which the researcher has access.
 Sample Size
Reasons for doing Sampling:
• Access to the whole population is impossible.
• There is usually insufficient time, manpower
and money to investigate the whole population.
• Samples can be studied more quickly than
population if we need a quick answer to an
important question e.g. when information is
required to plan dental services for a
community.
 Sample Size
Sampling error:
• An estimate of how much the characteristics of a sample differ from the characteristics of the
population it represents.
• The major threat to representativeness is bias.
• A biased sample is one in which the distribution of characteristics is systematically different
from that of the target population.
Bias:
• Selection bias.
• Response bias.
• Non response bias.
• Measurement error.
 Sample Size
There are several considerations that determine sample size:
• The larger the sample, the more valid and accurate the study
• The lesser the sampling error desired in the sample statistics, the larger the sample size
required.
• Samples of more homogenous populations can be smaller than samples of more
heterogenous populations.
• As the actual difference between study groups gets smaller, the size of the sample required
to detect the difference gets larger.
• If a researcher is expecting a strong relationship between a disease and a risk factor or a
cause, a smaller sample will be needed than if a weaker relationship is expected.
• In case of experimental studies, as the actual difference between the study groups gets
smaller, the size of the sample required to detect the difference gets larger.
 Sample Size

Random sampling:
• Random sampling is the one in which each and every unit
in a study population has an independent and equal chance
of being included in the sample.
Probability sampling:
• If the chance of each unit being chosen in the sample is
known so that the sampling error can be calculated, then
the type of sampling is called probability sampling.
 Data Analysis
Definition:
Data analysis is the systematic examination, transformation, and interpretation of data to extract
meaningful insights and support decision-making in research. It involves converting raw data into
actionable information through quantitative (numerical) or qualitative (non-numerical) methods.

Importance:
• Validates Hypotheses.
• Identifies Patterns.
• Ensures Reliability.
• Supports Decisions
• Communicates Findings.
 Data Analysis
Steps of Data Analysis Process:
1. Data Collection
Gather relevant data through surveys, experiments, or secondary sources.
2. Data Cleaning
Fix errors, inconsistencies, or missing values in the dataset.
3. Data Transformation
Reformat data for analysis (e.g., normalize or encode variables).
4. Exploratory Data Analysis (EDA)
Understand data distributions and detect patterns using visuals and stats.
5. Statistical Analysis
Use inferential methods to test hypotheses and relationships.
6. Interpretation and Reporting
Draw conclusions and present findings with context and visuals.
 Data Analysis

Data Analysis Methods:

1. Descriptive Analysis
• Summarizes data using measures like mean, median,
and standard deviation.
2. Inferential Analysis
• Draws conclusions about a population based on sample
data.
• Example: Conducting a hypothesis test to determine
whether fluoride toothpaste significantly reduces cavity
incidence compared to non-fluoride toothpaste in a
group of patients.
 Data Analysis
3. Regression Analysis
• Examines relationships between variables to
predict outcomes.
• Example: Using regression analysis to predict the
likelihood of gum disease based on factors like
age, smoking status, and frequency of dental
visits.
4. Qualitative Analysis
• Analyzes non-numerical data to identify themes
and patterns.
• Example: Conducting thematic analysis of patient
interviews to explore perceptions of pain
management during dental procedures and
identifying common concerns or preferences.
 Data Analysis

Selecting Appropriate Data Analysis

Techniques:
This selection depends on:
• Research Objectives: What are you trying
to achieve? (e.g., finding correlations,
testing hypotheses, or summarizing data).
• Data Types: Is the data numerical
(quantitative) or non-numerical
(qualitative)?
• Nature of the Research Question: Does it
involve comparing groups, predicting
outcomes, or exploring patterns?
 Data Analysis
Tools and Software of Data Analysis:
 Data Analysis

• The article, "Treatment of Skeletal Class III Malocclusion in Adolescents Using Miniscrew-
Supported Orthopedic and Fixed Orthodontic Appliances," applies data analysis as
follows:
Descriptive Analysis:
• Cephalometric Analysis: Measurements like SNA and ANB angles were collected pre-
and post-treatment to quantify skeletal and dental changes.
Data Analysis
 Data Analysis

Comparative Analysis:
• Changes in dental and skeletal
parameters were compared to
assess treatment effectiveness.
3D Imaging and Modeling:
• Utilized CBCT scans and 3D-
printed guides for precise
miniscrew placement, enhancing
treatment accuracy.
Clinical Outcomes:
• Validated improvements in alignment,
maxillary advancement, and patient
aesthetics through statistical and
imaging data.