The Research Process and Business Research Standards
Fundamentals of Business Research and Quality Standards
Definition of Research
Research is a systematic process encompassing the collection, recording, analysis, and interpretation of information.
The primary objective of research is to generate answers to specific questions and contribute to the advancement of overall knowledge.
Specific questions vary by domain; for example, a marketer investigates consumer brand perceptions, whereas a medical researcher studies correlations between recovery times and medical treatments.
Definition of Business Research
Business research comprises a structured series of processes involving planning, gathering, analyzing, and reporting pertinent data, information, and insights to organizational decision-makers.
It provides evidence-based information designed to motivate organizations to take suitable actions, maximize performance, and achieve strategic objectives.
Before initiating research, organizations often perform a situation analysis, such as a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, or an Importance-Performance Analysis based on customer satisfaction surveys.
Six Standards of High-Quality Business Research
Standard 1: Purpose Clearly Defined
Details: The purpose of the business research (the specific problem addressed) must be clearly articulated in writing. This statement must encompass the scope, limitations, and precise operational definitions of all relevant concepts, constructs, and variables.
Researcher Responsibilities: Differentiate between the superficial symptoms of an organizational issue and the actual underlying root problem; embed scope and limitations directly within the problem statement; establish precise operational definitions for every concept, construct, and variable.
Standard 2: Research Design Thoroughly Planned and Executed
Details: Research procedures must be detailed and meticulously executed to generate objective results. Researchers must avoid personal bias when selecting research designs, sampling designs, data collection methods, and recording mechanisms.
Researcher Responsibilities: Explicitly justify the chosen research design and explain the rejection of alternative designs; meticulously execute procedures within the chosen design; define the target population, sampling unit, and selection procedures; establish and strictly adhere to data collection protocols.
Standard 3: High Ethical Standards Applied
Details: Research designs must incorporate safeguard measures to prevent physical or psychological harm, exploitation, invasion of privacy, and loss of dignity for participants. Adequate training and safety measures for data collectors must be provided, alongside rigorous procedures to preserve data integrity.
Researcher Responsibilities: Safeguard study participants, organizations, clients, and research personnel; ensure methodology and limitations sections reflect ethical restraint and accuracy; maintain strict confidentiality of subjects and sources; keep recommendations strictly within the empirical scope of the study.
Standard 4: Adequate Analysis for Decision-Makers' Needs
Details: Data analysis must utilize suitable analytical techniques, categorize information to facilitate relevant insights and conclusions, and transparently present findings and methodologies. When statistical techniques are used, the probability of error must be estimated, and statistical significance criteria disclosed. Data validity and reliability must be systematically verified.
Researcher Responsibilities: Execute analytical procedures tailored to the collected data type; directly link findings to the research instrument; rigorously verify validity and reliability; ground all insights and recommendations purely on empirical data.
Standard 5: Limitations Frankly Revealed
Details: Shortcomings in research design or execution must be openly acknowledged, accompanied by an estimate of their impact on findings. Imperfections must be evaluated to determine whether they exert minimal effect or render findings invalid.
Researcher Responsibilities: Disclose limitations by contrasting desired procedures against actual executed procedures; highlight sample limitations by comparing sample characteristics against target population attributes; explain the direct impact of limitations on final findings, insights, and conclusions.
Standard 6: Findings Reported Unambiguously; Insights and Conclusions Justified
Details: Sources of data and collection methodologies must be disclosed fully (unless restricted by confidentiality). Procedural details must be sufficient to allow estimation of data validity and reliability. Researchers must explicitly state the exact conditions under which their conclusions remain valid.
Researcher Responsibilities: Present findings using text, tables, and graphs; structure findings, insights, and conclusions to assist decision-making; summarize conclusions effectively; include a detailed table of contents in written reports to facilitate rapid access to specific information.
Overview of the Five Linear Research Stages
Stage 1: Clarify the Research Question.
Stage 2: Design the Research Project.
Stage 3: Collect and Prepare the Data.
Stage 4: Analyze and Interpret the Data.
Stage 5: Report Insights and Recommendations.
Stage 1: Clarifying the Research Question and Exploration Strategy
Identifying and Prioritizing Management Dilemmas
Organizations must deploy limited resources where they generate the greatest operational impact. The process begins by identifying management dilemmas—symptoms of underlying operational problems.
Typical management dilemmas include:
Rising operational costs.
Increasing tenant move-outs from residential complexes.
Declining sales revenues.
Increasing employee turnover within service operations.
Higher frequency of product defects in manufacturing lines.
Accumulation of post-purchase delivery complaints.
Dilemmas are tracked against Key Performance Indicators (KPIs) evaluated across three organizational levels:
Company KPIs: Driven by stockholder demands (sales, profits, growth, corporate reputation), government regulations (safety standards, overtime rules, on-time delivery), and labor unions (training access, career advancement).
Business Unit KPIs: Focus on manufacturing output, defect rates, production turnaround timeframes, and supply fulfillment rates.
Human Resources KPIs: Focus on employee turnover rates, engagement scores, applicants per open position, online training completion rates, safety compliance scores, and annual performance review ratings.
In feasibility studies, dilemma identification involves recognizing potential risks and operational hurdles across market conditions, financial constraints, technical limitations, regulatory mandates, and operational procedures prior to deciding whether to proceed to exploration or terminate the project.
Exploration in Stage 1
Exploration consists of searching existing internal and external information to clarify dilemmas and evaluate potential decision options.
Researchers utilize digital data warehouses, written company archives, and external literature. Aggressively retrieving existing data is significantly more cost-effective than gathering new primary data.
Exploration is generally unstructured, beginning with broad keyword searches and progressively refining search parameters as new domain details emerge.
Valuing and Budgeting Research
Conducting research incurs monetary and opportunity costs. Before allocating resources, organizations assess whether capturing missing information yields greater financial value than relying on manager intuition.
If past management expertise produces the exact same decision as a prospective research study, the research carries zero monetary value.
If a high-stakes decision cannot be executed confidently without fresh empirical data, the research carries positive value.
Management-Research Question Hierarchy and SMART Question Formulation
SMART Criteria for Research Questions
Research questions must adhere to the SMART framework:
Specific: Explicitly define the targeted problem without vague generalities.
Measurable: Embed concrete criteria to evaluate success or degree of completion.
Achievable: Ensure the problem can be solved within realistic organizational constraints.
Relevant: Directly align with overarching organizational goals and strategies.
Time-bound: Establish an explicit timeframe for execution and completion.
Example of a SMART Research Question: "How can we increase the market share of our eco-friendly packaging products by within the next year?"
Specific: Focuses on eco-friendly packaging products.
Measurable: Target metric is a increase in market share.
Achievable: Target is grounded in practical capabilities and market conditions.
Relevant: Directly advances revenue growth and corporate sustainability goals.
Time-bound: Timeframe set to within the next year.
The Four Levels of Question Hierarchy
1. Management Questions: Restate the management dilemma in question format, asking what administrative action must be taken to resolve the dilemma.
2. Research Questions: Articulate the precise, overarching objective of the research study to guide data collection strategy.
3. Investigative Questions: Break the research question into specific sub-questions to capture detailed situational factors, options, and environmental forces.
4. Measurement Questions: Represent the actual data-gathering items administered to respondents via survey instruments, interview protocols, or observation checklists.
Four Categories of Management Questions
Evaluation of Solutions: Choices between concrete operational actions to solve problems or capture opportunities (General Form: "How can we achieve the objectives we have set?"; Example: "Should we reposition brand X as a therapeutic product from its current cosmetic positioning?").
Choice of Purpose: Selection of strategic goals or objectives (General Form: "What do we want to achieve?"; Example: "What goals should we set for sales and profits in the next ?").
Troubleshooting: Diagnosing reasons why an organizational program fails to meet targets (General Form: "Why is our program not meeting its goals?"; Example: "Why does our recruitment program generate the lowest hire-to-offer ratio in the industry?").
Control: Monitoring ongoing operational performance against targets (General Form: "How well is our program meeting its goals?"; Example: "What is our product line's sales-to-promotion cost ratio?").
Application Example: Bank Deposit Growth and Operational Study
Management Dilemma: A bank president faces declining profit growth linked to slow deposit acquisition and an accumulation of customer complaints.
Management Question Refinement: Converting the broad question ("What can we do to increase profits?") into distinct operational sub-questions:
"How can we improve deposits?"
"How can we improve internal operations that currently result in customer complaints?"
Research Question: "Should the bank position itself as a modern, progressive institution (with appropriate changes in services and policies) or maintain its image as the oldest, most reliable institution in town?"
Fine-Tuning Outcomes: After exploration, research questions are either answered (negating further research) or refined into focused, actionable questions.
Refinement Activities: Define variables operationally; break down research questions into second- and third-level investigative questions; state clear hypotheses; determine required empirical evidence; set study boundaries by explicitly defining excluded areas.
PESTEL Framework for Environmental Feasibility Research Questions:
Political: Government policies, trade regulations, tax policies, political stability.
Economic: Employment levels, inflation rates, interest rates, currency devaluations.
Social: Demographic shifts, religious beliefs, consumer values, lifestyle trends, purchasing behaviors.
Technological: Infrastructure, internet penetration, artificial intelligence adoption, automation machinery.
Environmental: Carbon emissions, non-renewable resource usage, environmental sustainability, natural disaster risks.
Legal: Consumer protection laws, occupational safety mandates, labor standards, import/export restrictions.
Sample Investigative and Measurement Questions (Bank Study)
Investigative Question 1: What is the public's position regarding financial services and their usage?
Measurement Question: "How often do you use the following financial services? (Checking accounts, Savings accounts, Loans, Credit cards, Online banking, Mobile banking, Investment services)" [Response Options: Daily, Weekly, Monthly, Rarely, Never].
Investigative Question 2: What is the bank's competitive position?
Measurement Question 1: "How far is your residence from the nearest branch of our bank?" [Response Options: Less than , , , More than ].
Measurement Question 2: "Which branch of our bank do you most frequently visit?" [Response Options: Branch 1 (Location A), Branch 2 (Location B), Branch 3 (Location C), Online/Mobile only].
External and Internal Information Sources
Three Classification Levels of Information Sources
1. Primary Sources: Uninterpreted, original data works, raw material, or official corporate/governmental positions.
Examples: Memos, letters, full interview/speech recordings or transcripts, laws, statutes, court decisions, government census data, labor statistics, internal inventory logs, personnel files, statistical process control (SPC) charts, primary research reports.
2. Secondary Sources: Analytical interpretations, summaries, or secondary processing of primary data.
Examples: Encyclopedias, academic textbooks, business handbooks, magazine articles, newspaper pieces, news broadcasts, internal KPI dashboards, investor annual reports.
3. Tertiary Sources: Search tools, indexes, bibliographies, and location guides used to uncover primary and secondary materials.
Examples: Internet search engines, bibliographic index databases, library reference catalogs.
People as Information Sources (Information Gatekeepers)
Institutional Memory Guardians (Company Historians): Long-tenured, active, or retired employees who maintain context regarding past decisions, internal policy changes, and historical outcomes. Best interviewed via individual, face-to-face sessions.
Industry or Problem Experts: External specialists who offer macroscopic perspectives on industry trends, technical breakthroughs, structural changes, and solutions developed by peer firms.
Directly Involved Employees: Workers directly operating within the targeted problem area. Standard practice involves interviewing employees holding extreme opposing perspectives via focus groups, brainstorming sessions, or individual interviews.
Affected Employees: Personnel impacted by potential solutions. They provide insights into workspace ergonomics, morale changes, productivity impacts, and operational friction.
Published External Sources and Four-Step Literature Search Protocol
Four-Step Literature Search Procedure:
Step 1: Consult basic reference materials (encyclopedias, dictionaries, handbooks, textbooks) to define terms, identify key individuals, and locate relevant organization names.
Step 2: Search specialized indexes and bibliographic databases using established key terms.
Step 3: Filter and review located sources for explicit contextual relevance.
Step 4: Systematically evaluate source quality, authority, and data accuracy.
Key External Source Types:
Government Portals: Public portals such as Data.gov.ph (in the Philippines) provide raw and summarized datasets spanning agriculture, climate, education, energy, finance, healthcare, manufacturing, and household expenditures.
Directories: Resources used for locating organizational contacts and structural details (e.g., Encyclopedia of Associations / Associations Unlimited).
Dictionaries & Glossaries: Define domain-specific jargon, standard acronyms, and operational terminology.
Encyclopedias: Provide historical background, timeline milestones (e.g., launch dates of key technologies), and identification of field experts.
Handbooks: Compilations of sector facts, regulatory frameworks, standard formulas, statistical tables, and citations.
Media, Blogs, & Books: Professional literature, industry trade journals, academic monographs, and active professional blogs.
Presentations, Videos, & Webinars: Keynote speeches, conference panels, digital video presentations, and slide repositories (e.g., YouTube, SlideShare.net, LinkedIn).
Evaluation Criteria for External Sources
Purpose: Identifying explicit or hidden agendas, commercial biases, and underlying promotional objectives of the publisher.
Scope: Evaluating topic depth, geographical limitations, historical era covered, and inclusion criteria.
Authority: Assessing author credentials, institutional backing, and data origin level (primary vs secondary).
Audience: Determining the target reader demographic (e.g., academic researchers, corporate executives, general consumers) to gauge technical assumptions.
Format: Assessing organization, logical flow, searchability, structural clarity, and overall readability.
Internal Information Sources, Data Warehouses, and Evaluation Criteria
Internal archives include department logs, financial memos, customer emails, sales histories, and past research reports.
Data Warehouses: Centralized electronic repositories structured into logical classifications or virtual folders to ensure real-time dynamic access to centralized organizational metrics.
Cloud data warehouses unify cross-functional data across remote locations under standard architectural definitions.
Secondary usage of internal primary data: Archived employee performance evaluations initially used for pay scale determination can be re-analyzed to identify structural turnover, promotion paths, and training gaps.
Internal Source Evaluation Criteria:
Compatibility: Degree of alignment between historical internal metrics and current research objectives.
Timelessness: Stability of historical data patterns across temporal shifts.
Recentness: How current the data entries are relative to changing market conditions.
Quality of Research Process: Methodological rigor applied when the data was originally gathered.
Author(s) Experience: Technical competence and expertise of internal personnel responsible for compiling records.
Research Valuation, Success Criteria, and Budgeting Methods
Defining Research Value
Mathematical/Conceptual Definition: Research value equals the monetary or operational difference between the net outcome of a decision made using new research information versus the net outcome of the decision executed without research.
Organizational metric impact: Investments in research must improve organizational KPIs (e.g., revenue generation, net profit margin, applicant flow, employee retention, defect reduction, accident rate reduction).
Four Success Criteria for Valuing Research
1. Options: Identification of two or more actionable corporate paths (e.g., launching new employee training, changing product features, restructuring assembly workflows, altering distributor partnerships).
2. Decision Variables: Quantifiable performance metrics tied to options (e.g., financial revenue gains, labor cost savings, retention rate increases, injury reduction numbers).
3. Decision Rule: An explicit, unbiased criterion determining option selection based on outcome thresholds (e.g., selecting the initiative that minimizes workplace injuries or maximizes year-over-year retention).
4. Budget Estimate: The calculated expenditure necessary to execute data collection, processing, and analysis for all options.
College Retention Scenario: Management dilemma is low retention. Decision variable is year-over-year student retention rate. Plausible options identified via exploration: peer mentoring, alumni mentoring, specialized introductory courses, or freshman success programs. Scenarios modeled: best-case, likely-case, worst-case. Decision rule: Select the program maximizing retention per peso spent.
Research Budgeting Classifications
Task Budget: Allocates funds from central discretionary reserves or an overall corporate research budget. Discretionary reserves are often calculated as a fixed percentage of past or projected annual sales revenues. Securing funds involves internal competitive bidding.
Functional Area Budget: Allocates funds directly out of an individual department's (business unit's) operational budget. The departmental manager retains direct spending authority over these operational funds.
Structure of a Budget Justification (Narrative)
A budget justification is a detailed narrative explaining every itemized line-cost in a proposed research project.
It details staffing allocations, material consumption rates, mathematical calculation methods (including inflation and price escalation factors), and direct vs. indirect cost classifications.
Eight Cost Categories for Individual Researchers
1. Personnel Costs: Hourly labor rates multiplied by estimated hours for principal investigators, researchers, and field assistants.
2. Materials and Supplies: Consumable testing materials, physical survey instruments, lab reagents, specialized software licenses.
3. Equipment: Specific machinery, digital recording hardware, or computational hardware required for purchase or lease.
4. Travel Expenses: Itemized transport fares, field mileage, lodging, and daily meal per diems.
5. Data Collection Costs: Respondent compensation incentives, venue rentals, database access fees, paper printing costs.
6. Data Analysis Costs: Dedicated analytical software packages, cloud processing computing time, specialized statistical consulting fees.
7. Publication and Dissemination Costs: Open-access journal publishing fees, academic/industry conference registration, report binding, presentation visuals.
8. Contingency Reserve: An unallocated monetary buffer equal to to of the total overall budget allocated to cover unforeseen emergency expenses.
Stage 2 & 3: Research Design, Sampling, and Data Preparation
Definition and Core Tasks of Research Design
Research design is a blueprint and chronological plan for data collection, measurement, and analysis aimed at answering research questions and testing hypotheses.
Central Design Tasks: Crafting the sampling strategy, determining the data collection method, and constructing measurement tools.
Sampling Design Subprocess
Primary Questions: Identifies who or what will be measured and how target sources will be selected and recruited.
Feasibility Sample Size Threshold: A standard baseline sample size target is approximately of the defined target population. For example, if a target market population comprises , the maximum sample target is .
Sampling Steps: Define target population Establish sampling frame (database/roster) Select sampling technique (e.g., stratified probability sampling across job titles) Determine sample size Execute recruitment protocols (incorporating incentives like paid time off).
Data Classification and Measurement Scales
Primary vs. Secondary Data:
Primary Data: Direct, unprocessed data recorded first-hand from primary sources (e.g., live interview audio, raw observation counts, custom survey entries).
Secondary Data: Processed data containing at least one level of human interpretation or summary (e.g., published chart summaries, government census reports).
Four Measurement Scales:
1. Nominal Scale: Categorical classification without implicit order or rank (e.g., gender, race, brand names, geographic regions).
2. Ordinal Scale: Categorized data arranged in a logical, meaningful sequence or rank order without standardized intervals between points (e.g., satisfaction rankings, performance tiers).
3. Interval Scale: Ordered categories with uniform, equal mathematical intervals between points, but lacking a true zero origin (e.g., temperature in Celsius, IQ scores).
4. Ratio Scale: Ordered categories featuring uniform mathematical intervals and an absolute, non-arbitrary zero origin (e.g., weight, height, monetary sales volumes, unit volume counts).
Bank Branch Location Case Study (Data Collection Design)
Context: A bank board evaluates building a new branch to increase total savings deposits.
Operational Definition: "Nearness" defined operationally as a saver's home address situated within a radius of the branch where the account was opened.
Two-Phase Design:
Phase 1 (Records Analysis): Ex post facto statistical examination of historical savings logs to correlate residential proximity with transaction frequency and account balances.
Phase 2 (Customer Survey): Primary survey gathering saver demographics (age, gender, income, marital status, life stage), attitudinal factors toward saving, work location, and online/electronic banking usage.
Tested Hypotheses for Distant Savers:
1. Distant savers initially lived close to the branch when opening the account and subsequently relocated.
2. Distant savers live close to the branch, but internal address records are outdated/incorrect.
3. Distant savers work near the branch location.
4. Distant savers opened accounts due to specific promotions and utilize electronic banking channels exclusively.
Empirical Outcome: Hypotheses 1 and 3 accounted for most distant savers, confirming location nearness is significantly correlated with branch saving activity.
Stage 3 Data Collection Devices and Data Preparation Protocols
Collection Devices: Questionnaires, standardized psychological/technical tests, structured observation checklists, digital audio/video setups, focus group protocols.
Data Preparation Procedures:
Editing: Auditing raw data files to resolve recording errors, improve legibility, correct typos, and process unclear responses.
Alphanumeric Coding: Translating raw categorical text entries into numerical codes to enable computer processing.
Out-of-Range Auditing: Running summary distributions to identify erroneous code entries (e.g., detecting a code entry of when a binary question only allowed [Yes] or [No]).
Missing Data Protocols: Systematically determining whether to re-inspect original physical instruments or discard invalid entries across single variables or entire respondent records.
Stage 4 & 5: Data Analysis, Interpretation, and Recommendations Reporting
Stage 4: Data Analysis and Statistical Summarization
Raw data must be condensed into actionable statistical information. Data analysis summarizes data via descriptive statistics, identifies latent patterns, and tests hypotheses.
Data Reduction Calculation: A survey of asking yields . Data analysis condenses these points into summary percentages, cross-tabulations, and statistical significance tests.
Qualitative complexity: Narrative responses to open-ended questions require thematic distillation into major categorical findings.
Interpretation: Distilling statistical outputs into insights that empirically confirm or refute prior hypotheses and theoretical models.
Stage 5: Reporting Insights and Recommendations
Communication Vehicles: Formal executive presentations, detailed written technical reports, summary memos, executive dashboards, infographics.
Perspective: Reports must be written explicitly from the manager's strategic decision context, focusing on directly resolving the primary management dilemma using empirical evidence.
Factors Leading to Non-Action on Findings:
Ineffective presentation style or unclear organization.
Decision-makers' lack of statistical literacy.
External environmental shifts occurring after research completion (e.g., shifts in market interest neutralizing an advertising campaign based on valid book-buying data).
Core Dimensions of Research Design
Dimension 1: Objective of the Study
Reporting: Focuses on summarizing and reformatting past data to present a clear historical tally (e.g., tallying overall employee thefts in shopping mall stores versus free-standing retail outlets over the past year).
Descriptive: Measures current conditions across questions of who, what, when, where, and how (e.g., measuring current employee theft attributes: item types [clothing, electronics, cash], timing [time of day, day of week], physical location [stockroom, receiving dock, sales floor], and perpetrator characteristics [age, gender, tenure]).
Causal - Explanatory: Explains functional relationships between variables to determine how changes in an independent variable induce changes in a dependent variable (e.g., investigating why employee theft rates are higher in Mall A than Mall B).
Causal - Predictive: Projects future effects on a dependent variable by actively manipulating an independent variable under controlled conditions (e.g., testing whether installing physical surveillance cameras on receiving docks reduces future retail theft rates).
Dimension 2: Variable Manipulation
Experimental: The researcher exercises direct control over manipulating independent variables according to research goals (e.g., artificially altering or restricting ATM access operating hours at selected branches to observe changes in total transaction volumes relative to control branches).
Ex Post Facto: The researcher exercises no control over variable manipulation and cannot alter experimental conditions. Instead, the researcher compares observed groups already exposed to a condition against unexposed groups (e.g., comparing account balances of savers living near a branch versus those living far away).
Dimension 3: Topical Scope
Statistical Study: Emphasizes breadth across a large representative sample. Gathers standardized data points across multiple cases to test quantitative hypotheses and generalize findings to a population.
Case Study: Emphasizes contextual depth over population breadth. Conducts exhaustive investigation of a single or small number of events, organizations, or individuals using multiple qualitative/quantitative data sources (e.g., analyzing continuous surveillance footage and interviewing key staff to discover operational vulnerabilities).
Dimension 4: Research Technique / Measurement
Qualitative: Applies interpretive techniques to decode, translate, and understand underlying meanings, motivations, and operational dynamics. Focuses on non-numerical representations.
Quantitative: Measures numerical frequency, scale, or magnitude. Relies on standardized instruments and statistical tests.
Dimension 5: Design Complexity
Single Methodology: Employs a single data collection tool (e.g., administering a standalone customer survey).
Multiple Methodologies: Combines multiple data collection tools. Frequently executes a two-stage research design (e.g., Stage 1 qualitative focus groups to define variables, followed by Stage 2 quantitative survey administration to measure population characteristics).
Dimension 6: Data Collection Method
Monitoring: Observes and records behaviors, physical processes, or material phenomena without directly interacting with or eliciting active responses from participants (e.g., counting intersection traffic flow, recording parking lot license plates, tracking accident archives, recording decision-makers' physical actions).
Communication Study: Interrogates or questions participants directly to collect self-reported responses (e.g., personal interviews, telephone chats, web surveys, pre- and post-test experimental instruments).
Dimension 7: Research Environment
Field Setting: Executed under actual, real-world environmental conditions where subjects exhibit natural behaviors.
Laboratory Research: Executed under artificial, staged, or highly controlled environmental conditions (e.g., conducting confidential taste tests for a proposed fast-food menu item in a private test kitchen).
Simulation: Replicates real-world system processes using mathematical algorithms, computer models, virtual reality environments, or behavioral role-playing (e.g., utilizing mystery shoppers posing as ordinary customers to evaluate sales service compliance).
Dimension 8: Time Dimension
Cross-Sectional: Captures data points across a single snapshot in time. Highly cost-effective and fast, though requiring careful contextual interpretation.
Longitudinal: Measures targeted variables across repeated time intervals over an extended period. Can track the same individuals over time (panel design) or distinct sample cohorts over time (e.g., tracking financial needs of aging population groups by sampling in , in , and in ). Requires fresh sampling when tracking public awareness over a ad campaign to prevent respondent learning bias.
Design of Measurement Instruments and Data Collection Protocols
Sequential Instrument Construction Workflow
Step 1: Select specific measurement approaches for each data collection task.
Step 2: Draft operational measurement questions or specific checklist items.
Step 3: Establish logical question sequencing and structural flow.
Step 4: Write non-question elements, including participant introductions, term definitions, section instructions, and closing statements.
Step 5: Assemble the complete measurement instrument draft.
Step 6: Determine requirements for data collector training and create training manuals.
Step 7: Conduct pre-testing on individual questions and the entire instrument under simulated field conditions to fix ambiguous phrasing, logical skips, and timing flaws.
Specific Survey Instrument Requirements for Feasibility Studies
Demographic Variables: Respondent age, gender, exact residential location, household income, employment status.
Market Demand Metrics: Quantifiable purchase frequency, transaction size, and usage rates of similar products/services.
Customer Preferences: Consumer willingness to adopt proposed offerings versus existing market alternatives.
Research Ethics, Compliance, and Data Privacy Frameworks
Tripartite Ethical Responsibilities
Participant Rights & Duties: Right to be fully informed; duty to provide truthful responses (ensuring researcher's right to absence of deception) and complete assigned tasks diligently (ensuring researcher's right to quality research).
Researcher Rights & Duties: Duty to maintain record accuracy, disclose research purpose transparently, prevent data falsification, and protect subject safety; right to adequate monetary compensation and freedom from sponsor coercion.
Sponsor/Manager Rights & Duties: Duty to avoid coercing specific findings or misrepresenting results.
Core Ethical Research Principles
Voluntary Participation: Participants retain the absolute right to opt into or withdraw from the study at any point without penalty.
Informed Consent: Participants receive explicit information detailing the purpose, procedures, expected benefits, risks, and funding sources prior to agreeing to participate.
Anonymity: The researcher cannot trace collected data back to participant identities, as no personally identifiable information (PII) is captured.
Confidentiality: The researcher can associate data with participant identities but guarantees that identities are kept secure and anonymized in all public outputs.
Harm Minimization: All physical, psychological, social, or economic risks to subjects are minimized.
Results Communication: Research reporting must be entirely free from plagiarism, outcome manipulation, or academic misconduct.
Republic Act No. 10173 (Data Privacy Act of 2012) - Section 19 Implementing Rules
General Principle A: Collection Rules
1. Personal data collection must have a clearly declared, specific, and lawful purpose. Express consent is mandatory prior to collection unless statutory legal exemptions apply. Consent must be time-bound and revokable.
2. Data subjects must be provided detailed information regarding the processing scope, including automated profiling, direct marketing, or third-party data sharing.
3. The precise processing purpose must be disclosed before or promptly after collection.
4. Collected personal data must be strictly essential and compatible with the declared purpose.
General Principle B: Processing Rules
1. Personal data processing must be fair, lawful, and transparent, respecting data subject rights to object or withdraw consent.
2. Communications must utilize clear, straightforward language.
3. Processing must strictly align with the declared purpose.
4. Processed data must be adequate, relevant, and limited to what is necessary.
5. Processing systems must implement appropriate technical, physical, and organizational security measures.
General Principle C: Data Quality Rules
1. Personal data must be accurate and updated as necessary for its lawful purpose.
2. Inaccurate or incomplete data must be corrected, supplemented, erased, or restricted.
General Principle D: Retention and Disposal Rules
1. Personal data retention must not exceed the duration necessary to fulfill the declared lawful purpose, settle legal claims, or meet legitimate business standards.
2. Extended retention is permitted only when explicitly authorized by law.
3. Upon expiration of the retention period, personal data must be securely destroyed or anonymized to prevent unauthorized access, processing, or harm to data subjects.