Research Study: Chapter 1 Guide

Chapter 1: Background of the Study

• This chapter includes the rationale, problem statement, scope, limitations, delimitations, and significance of the study.
• This chapter is typically 9-10 pages long.

Presenting a Strong Rationale

• Use a macro-to-micro approach, moving from global to national to local contexts.
• Conduct a pre-survey if local data is unavailable.
• Pre-surveys must have clear selection criteria.
• Support pre-survey data with informal interviews with experts, as pre-surveys alone can be biased.
• Incorporate local context and current situations, not just literature.

Writing Statements of the Problem (SOP) for Quantitative Research

• Example SOP: "Level of Participation on SDG initiatives among Students of PH National Engineering University."
• Start by identifying the model being used to measure levels.
• Include a demographic profile with age, sex, year level, civil status, and college department.
• Measure the level of participation based on variables from the chosen model, like the IAP2 model (Convey, Consume, Collaborate, Empowerment).
• Use inferential statistics to determine if there is a significant difference between respondents' demographics and their participation levels.
• Potential DevCom outputs from the research should be proposed.

Writing SOP for Qualitative Research

• Example SOP: "Unveiling Advocacy Communication through the Lived Experiences of HIV/AIDS Advocates in CALABARZON, Philippines."
• One question to explore: "How may the lived experiences of HIV/AIDS advocates in CALABARZON be described?", which can be answered through thematic analysis.
• Code Book of Participants provides a glimpse into participants' backgrounds and credibility.

Chapter 2: Review of Related Literature

• Significant or relevant concepts for the study make up conceptual literature.
• Research Literature: relevant sources within the last 5 years (2019 onwards).
• Use the most appropriate theory for the study in the Theoretical Framework, and place it in your study.
• Conceptual Framework: Input, Process, Output.
• Define 10-15 important terms from the research title alphabetically.

Writing Research Literature

• Use APA 7th edition style.
• Include 25 local and 25 foreign.
• Synthesize similarities and differences in methodology, data gathering techniques, approaches, participants, and research instruments.
• Group information by continent.
• Write an introductory and closing paragraph.

APA Style (7th Edition) Examples

• Beginning: Smith (2023) stated that...
• Middle: Climate change is a threat in biodiversity as stated by Smith (2023).
• End: Climate change is a global phenomenon (Smith, 2023).

Resources for Review of Related Literature

• Google Scholar
• Elsevier.com
• Researcher Academy
• Perlego

Conceptual Framework

• Input: Variables (SOP, all types of variables).
• Process: Data gathering instrument and procedure (researcher-made if quantitative).
• Output: DevCom output.

Definition of Terms

• Conceptual: Dictionary meaning.
• Operational: How the word is used in the research (e.g., fisherfolk referring to respondents).

Quantitative Research

• Systematic investigation using numerical data and statistical analysis to understand phenomena.
• Aims to quantify variables, identify patterns, and establish relationships using tools like surveys, experiments, and secondary data analysis.

Statistical Treatment

• Refers to techniques used to analyze numerical data in quantitative research.
• Used to summarize, interpret, and draw conclusions.
• Divided into descriptive and inferential statistics.

Descriptive Statistics

• Basic computations.
• Elements include Frequency (number/population), Percentage Distribution (frequency / population x 100), Weighted Mean (used with variable 2, present the summary of means), and Rank (4-point Likert's Scale).

Basis for Interpretation (Likert Scale)

• Scale 4 (3.50-4.00): Strongly Agree
• Scale 3 (2.50-3.49): Agree
• Scale 2 (1.50-2.49): Disagree
• Scale 1 (1.00-1.49): Strongly Disagree

Example 1 Interpretation

• Communication style and weighted mean are provided.
• Provide the interpretation and rank.

Example 2 Interpretation

• Supply the total population, percentage distribution, and the rank.
• Formula for Percentage Distribution: Frequency / Population x 100
• Interpretation of the Demographic Table illustrates the majority (e.g., respondents are female).

Inferential Statistics

• Use significance differences and p-values; the standard p-value is 0.05.
• SOP 3: Is there a significant difference between the respondents' demographic profile and their perception on AI use?

Interpretation of Profile and P-Value

• If the p-value is greater than 0.05, there is no significant difference.
• If the p-value is less than 0.05, there is a significant difference.
• Found that there is a significant difference between the respondents' sex and their perception, so the male and female have different perceptions.
• Found that there is no significant difference between the respondents' age and their perception, so regardless of their age, they have the same perception.

Parametric Tools

• use statistical methods, and assume the data follows a specific distribution (usually normal distribution).
• More effective when assumptions are met.
• Common tools include T-test, Analysis of Variance (ANOVA), and Pearson’s correlation.

Significance Difference

• ANOVA tests for independent sex variables using Kruskal Tools

Significance of Association

• Mann Whitney

Statistical Tests

• t-Test: Compares means of two groups. Example: Comparing test scores between males and females.
• Analysis of Variance (ANOVA): Determines statistically significant differences among three or more group means. Example: Comparing the effectiveness of three different teaching methods.
• Pearson Correlation: Measures the strength and direction of the relationship between two continuous variables. Example: Examining the relationship between study time and exam scores.
• Regression Analysis: Predicts the relationship between independent and dependent variables. Example: Predicting student performance based on attendance and previous grades.

Variables

• Any characteristic, number, or quantity that can be measured or controlled in research.

Types of Variables

• Independent Variable (IV): Manipulated or categorized factor to observe its effect.
• Dependent Variable (DV): Outcome being measured or observed.
• Control Variables: Factors kept constant to avoid influencing results.
• Extraneous Variables: Uncontrolled factors affecting the dependent variable.

Hypothesis

• A prediction of the relationship between variables. Null Hypothesis (H0): No relationship or difference exists. Alternative Hypothesis (H1): Suggests a significant difference or relationship.
• Writing hypothesis: no significant difference, accept the null, reject the hypothesis. there is significant difference, reject the null, accept the alternative hypothesis.

Population vs Sample

• Population: entire group of individuals, events, or items being studied.
• Sample: subset of the population selected for analysis (representation of the population).

Sampling

• The process of selecting a subset of individuals from a population for research.
• Can be classified into probability (quantitative) and non-probability (qualitative) sampling.

Probability and Non-Probability Sampling Techniques

• In probability sampling, every individual has an equal chance of being selected. Examples include simple random, stratified, systematic, and cluster sampling. Non-probability sampling uses non-random subjective selection. Examples include convenience, quota, snowball, and purposive sampling.

Determining Sample Size

• Use a Raosoft calculator to decide for the sample size and compute the sample for the sample provided.
• The sample size decreases when the margin of error is raised and with the use of maximum allowable adjustment (7.5%). If there is data for frequence then we need to to provide the sample.

Probablity Techniques

• Systematic use fishbowl, magcut ng odd numbers then bunot pag may refusal edi skip tas yung katabi yung magsasagot, kailangan maidentify ang starting point, then the direction.
• Random/Simple Random listahan ng pangalan/list of entire population, gugupitin yung names tas bubunitin/randomizer hanggang makabunot ka ng number of sample size.
• Stratified Proportionate Random Formula: Frequency / Population x Sample size, Checking: add the sample, if kulang magbase sa isang pinakamataas ang decimal, we need the list first then compute for the total population then go to raosoft then get the sample size (standard requirement: margin of error 5%, confidence level 95%)
• Cluster Sampling

Sampling Techniques

• Snowball - referral system.
• Convenient - availability.
• Purposive - target sample audience (for ex. Teachers, students, etc)
• Block use for larger population.
• Qouta - identified number of respondents (for example: LGBT L-25 respondents, G-25 respondents, B-25 respondents, T-25 respondents)

Sampling vs Census

• Not all times, we need to use sampling techniques, census may also use for specialized population, those with small populations, use percentage retrievel.
• In the example study acceptable percentage retrieval is 90%. the result will be invalid kapag mas mababa sa 90% ang percentage retrieval
• Ex) we have 30 members of panel if ilang percentage ng retrieval. example, 95% ang gusto ng panel then kukunin yung 95% ng 30 members

Qualitative Research

• A method of inquiry that explores human experiences, behaviors, emotions, and social phenomena through non-numerical data.
• Seeks to understand meaning, context, and perspectives, unlike quantitative research that focuses on numerical measurement.
• It can provide an understanding of how official figures are created through social processes

Key features of QUALI

• Exploratory - explore complex issues that cannot be measured numerically.
• Descriptive – provides rich, detailed accounts.
• Interpretive - focuses on how individuals or groups interpret their experiences.
• Subjective - acknowledges research findings are shaped by context, culture, and the researcher's perspective.

Approach to Qualitative Research

1. Identify the Research Problem
2. Review Literature
3. Choose a Research Design
4. Select Data Collection Methods
5. Analyze the Data
6. Interpret and Report Findings

Types of Coding in Qualitative Research

• Coding in qualitative research is the process of labeling and organizing data to identify themes and patterns.

How to approach qualitative research

1. Step 1: Identify the Research Problem- Step 2: Review Literature
2. Step 3: Choose a Research Design:

• 1.Ethnography - Studying cultural groups through observation.
• 2.Phenomenology - Exploring lived experiences (e.g., how cancer survivors perceive life).
• 3.Case Study - Analyzing a single case or event in detail.
• 4.Grounded Theory - Developing theories from data.
• 5. Narrative Research - Analyzing personal stories and experiences.

3. Step 4: Data Collection Methods- data through interviews Focus Groups, and Observations,
4. Step 5: Unlike quantitative research qualitative analysis is interpretive and thematic through Coding and Thematic Analysis step 6 is to Discuss themes.