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Research Methods

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92definitions

5formulas

Introduction to Research

Defining research, types of research, critical thinking, topic selection, problem formulation, and research questions

What is Research?

Definition of Research → Why Conduct Research? → Characteristics of Good Research → Types of Research → Practical Tasks

Definitions

Mixed Methods: — a combination of quantitative and qualitative approaches in one study to obtain a more complete picture.
Cross-sectional: — data are collected at one moment in time. This is a "snapshot" of the situation.
Longitudinal: — data are collected over a certain period of time to study changes and trends.

·Organization — research follows a clear plan and structure
·Systematic nature — each stage is logically connected to the previous one
·Data-based — conclusions are based on collected empirical data, not assumptions
·Criticality — the researcher constantly questions their assumptions and conclusions
·Objectivity — minimization of subjective biases
·Scientific approach — use of established scientific methods
·Purposefulness — a clearly defined goal and research questions
·Rigour — a thorough methodological approach ensuring the reliability of results
·Testability — hypotheses must be formulated in such a way that they can be empirically tested
·Replicability — other researchers should be able to repeat the research and obtain similar results
·Accuracy and reliability — results should reflect reality as accurately as possible
·Objectivity — conclusions should be based on data, not on the personal beliefs of the researcher
·Generalisability — results should be applicable beyond the specific sample (in quantitative research)
·Parsimony — explanations of phenomena should be as simple as possible

Research is a systematic process of collecting, analyzing, and interpreting data to answer specific questions or solve particular problems. Unlike everyday observation or simple information search, scientific research follows a strict methodology and is subject to the principles of objectivity, r...

Sekaran and Bougie (2016) define research as “an organized, systematic, data-based, critical, objective, scientific process of inquiry or investigation, aimed at solving a particular problem." This definition highlights several key characteristics:

Research plays a central role in the development of knowledge and decision-making in a business context. The main reasons for conducting research:

1. Solving practical problems. A company may face declining sales, high employee turnover, or low customer satisfaction. Research helps to identify the causes of the problem and find a substantiated solution. For example, a restaurant chain may conduct research to understand why customers have st...

Critical Thinking in Research

What is Critical Thinking? → Critical Analysis Skills in Research → Typical Logical Fallacies → Choosing a Research Topic → Formulation of the Research Problem → Research Questions → Practical Assignments

Definitions

Confirmation bias: — the tendency to search for, interpret, and remember information that confirms already existing beliefs, ignoring contradictory data.
False cause-and-effect connection (Post hoc fallacy): — the assumption that if event B happened after event A, then A is the cause of B.
Generalization based on a single case: — making a general conclusion based on one or several examples without sufficient statistical basis.
Appeal to authority: — accepting a statement as true merely because it is voiced by an authoritative individual, without assessing evidence.
1. Interesting for the researcher: — motivation plays a key role over a long research process.
2. Practically significant: — the results should have potential application.
3. Feasible: — it is necessary to consider data availability, time constraints, and resources.
4. Sufficiently narrow: — a topic that is too broad will not allow for an in-depth study. "Marketing on social media" is too broad; "The influence of content marketing on Instagram on purchase intention among Generation Z in the fashion sector" is sufficiently specific.

·Analysis of arguments — breaking down an argument into its components (premises and conclusions) and assessing the logical connection between them
·Assessment of evidence — determining the quality, relevance, and sufficiency of the presented evidence
·Identification of biases — recognizing one's own and others' preconceived judgements
·Formulation of well-grounded conclusions — constructing conclusions that logically follow from the existing data

About the Research Problem

·Is the research problem clearly formulated?
·Is the problem significant and relevant?
·Is the necessity for conducting this research justified?

About the Literature Review

·Does the review cover the key sources on the topic?
·Are up-to-date and relevant publications used?
·Is a research gap identified?

About the Methodology

·Does the chosen method correspond to the research questions?
·Is the sample size adequate?
·Are the data collection procedures described in sufficient detail for replication?
·Are ethical aspects considered?

About the Results and Conclusions

·Are the conclusions supported by the collected data?
·Are the limitations of the research discussed?
·Do the conclusions contain excessive generalizations?
·Do the recommendations correspond to the obtained results?
·Describes the current situation and its divergence from the desirable one
·Justifies why the problem is important and requires study
·Defines the boundaries of the research
·Points to a gap in existing knowledge
·Clear — understandable and unambiguous
·Focused — sufficiently narrow for in-depth study
·Researchable — can be answered using collected data
·Relevant — related to the research problem

Critical thinking is the ability to objectively analyze information, assess evidence and arguments, identify biases, and form well-founded judgements. In the research context, critical thinking is a fundamental skill that distinguishes the scientific approach from superficial perception of inform...

When reading and evaluating scientific research, it is necessary to ask the following key questions:

Confirmation bias — the tendency to search for, interpret, and remember information that confirms already existing beliefs, ignoring contradictory data.

*Example:* A manager is convinced that remote work reduces productivity. While conducting research, he pays attention only to cases of low productivity among remote workers, ignoring numerous examples of high productivity.

Types of Research: Detailed Classification

Introduction → 1. By Purpose of Research → 2. By Depth of Coverage → 3. By Manipulation of Variables → 4. By Type of Reasoning → 5. By Time Horizon → 6. By Sources of Data → 7. By Method of Data Collection → Practical Assignments

Theoretical (Fundamental) Research

·Studying the influence of social networks on the formation of adolescents’ identity
·Researching the relationship between the type of organizational culture and the level of employee creativity
·Analyzing decision-making mechanisms under uncertainty

Applied Research

·Developing a strategy to reduce employee turnover in a specific company
·Assessing the effectiveness of a marketing campaign for a new product
·Determining the optimal location for a new branch office

Assignment 1

·(a) Correlational—the researcher measures the association between two variables (training hours and productivity).
·(b) Non-experimental—the researcher does not manipulate variables but observes them in natural conditions.
·(c) Field—data are collected at workplaces, in a real-life environment.

Assignment 2

·By purpose—applied, as the research is directed toward a practical task (testing drug effectiveness).
·By manipulation of variables—experimental, as the researcher manipulates the independent variable (administration of drug/placebo) and uses random assignment.
·By type of reasoning—deductive approach, as the researcher starts from a theory about the drug’s effectiveness, formulates a hypothesis, and tests it empirically.

The classification of research helps the researcher choose the most appropriate approach for studying a specific problem. There are many criteria by which research can be classified: by purpose, by depth of coverage, by manipulation of variables, by type of reasoning, by time horizon, by sources ...

Theoretical research (Basic / Pure Research) aims to expand knowledge and develop theories without an immediate practical purpose. Its goal is to understand the nature of a phenomenon, reveal patterns, and construct conceptual models.

The results of fundamental research are published in academic journals and form the foundation for future applied research.

Applied research is aimed at solving a specific practical problem. The researcher seeks to obtain results that can be directly used for decision-making.

Academic Writing and Reading

Introduction → 1. Academic Writing Style → 2. Use of Verb Tenses → 3. Paraphrasing and Plagiarism → 4. Referencing and Citation → 5. Effective Reading of Scientific Texts: SQ3R Method → 6. Structuring Academic Argumentation → Practical Exercises

Characteristic	Conversational Style	Academic Style
Tone	Informal, subjective	Formal, objective
Pronouns	“I think”, “we all know”	“It was established”, “the data indicate”
Argumentation	Based on opinion	Based on evidence
Vocabulary	Simple, colloquial	Terminologically precise
Structure	Free	Strictly organized

Key Principles

·Objectivity: Instead of “Obviously...” write “The results of the study show that...”
·Precision: Instead of “many scholars believe”—“a number of researchers (Smith, 2020; Jones, 2021) have established”
·Cautious Conclusions (hedging): Use hedging constructions: “it can be assumed,” “apparently,” “the data indicate a possible correlation”
·Coherence: Use linking words and phrases to ensure logical transition between ideas: “therefore”, “at the same time”, “on the contrary”, “moreover”

Present Tense

·“Research is a systematic process...”
·“Correlation does not imply causation.”

Past Tense

·“A survey of 200 respondents was conducted.”
·“The results showed a statistically significant correlation (r = 0.72, p < 0.01).”
·“Smith (2019) found that...”

Present Perfect Tense

·“A number of studies have demonstrated a link between motivation and performance.”
·“To date, conflicting results have been obtained.”

What is Plagiarism?

·Direct copying of text without quotation marks and reference
·Mosaic plagiarism — rearranging words in someone else's text without substantial paraphrasing
·Self-plagiarism — reusing one’s own previously published work without indicating it
·Plagiarism of ideas — appropriation of someone else's concept or theory

Academic writing and reading are key skills for any researcher. The ability to competently present research results and effectively work with scientific literature determines the quality of the entire research work. This article examines the main principles of academic style, citation rules, tech...

Academic style requires accuracy, clarity, logic, and justification of every statement. The researcher must avoid emotionally charged expressions, generalizations without evidence, and colloquial phrases.

Used for generally accepted facts, established theories, and general truths:

Plagiarism is the use of someone else’s ideas, words, or results without proper reference to the original source. Plagiarism is a serious violation of academic ethics and may result in expulsion, retraction of a publication, or loss of professional reputation.

Literature Review

Purpose and structure of a literature review, searching for sources, academic databases, critical reading, and writing the review

Purpose and Structure of a Literature Review

What is a Literature Review? → Purposes of a Literature Review → Searching for Sources → Critical Reading of Sources → Structure of the Literature Review → Practical Assignments

Definitions

Primary sources: — original scientific articles, books, dissertations in which authors present their own research results. These are the most valuable sources for a literature review.
Secondary sources: — textbooks, review articles, encyclopedias that summarize and interpret the results of primary research. Useful for initial familiarization with the topic.
Tertiary sources: — directories, catalogs, bibliographies that help locate primary and secondary sources.
Relevance: — how closely is the article related to your research question?
Quality: — is the article published in a peer-reviewed journal? What is the methodology?
Timeliness: — how recently was the research conducted? In rapidly changing fields, it is recommended to use sources not older than 5–7 years.
Citation count: — how many times has the article been cited by other researchers?
Authority: — who is the author? What is their experience in the field?
1. Thematic structure: — grouping by topics or subtopics. The most common and recommended approach.
2. Chronological structure: — organization by publication date. Shows the evolution of ideas.
3. Methodological structure: — grouping by research methods employed.
4. Funnel approach (from general to specific): — starts with a broad context and gradually narrows to the specific topic.

·What is already known on the chosen topic
·Which theories and models are used to explain the phenomenon
·Which methods have been applied in previous studies
·What gaps exist in current knowledge
·How the planned research contributes to filling these gaps

Academic Databases

·Google Scholar — a free search engine for academic publications. A good starting point for initial searches.
·Scopus — the largest abstract and citation database. Contains citation metrics.
·Web of Science — an interdisciplinary database with citation analysis tools.
·EBSCO Business Source Complete — a specialized database for business and management.
·JSTOR — an archive of academic journals.
·ProQuest — a database of dissertations and scientific journals.

Search Strategies

·AND — narrows the search: “employee motivation AND job satisfaction”
·OR — broadens the search: “leadership OR management”
·NOT — excludes terms: “innovation NOT technological”
·Quotation marks "" — exact phrase: “organizational culture”
·Asterisk * — truncation: “manag*” will find management, manager, managerial

Assignment 1

·Main: remote work, telework, work from home, telecommuting
·In combination with: employee productivity, performance, output, efficiency
·Additional: job satisfaction, work-life balance, organizational outcomes
·Search queries:
·("remote work" OR "telework" OR "work from home") AND ("employee productivity" OR "performance")
·("telecommuting" OR "flexible work") AND ("output" OR "efficiency")

Literature Review is a critical analysis and synthesis of existing scientific publications on the studied topic. It is not merely a retelling of articles, but an analytical work that demonstrates:

1. To establish the research context. The literature review situates your research in the broader academic context, showing how it is connected to existing works.

2. To demonstrate subject area expertise. The review shows that the researcher has a thorough understanding of the topic and is aware of key publications and debates.

3. To identify a research gap. A systematic analysis of the literature allows you to find areas that have not yet been sufficiently studied. This gap justifies the necessity of your research.

Literature Search: Strategies and Tools

Introduction → 1. Academic Databases → 2. Boolean Operators → 3. The “Snowball” Method (Snowballing) → 4. Reference Managers → 5. Assessing the Quality of Sources → 6. Literature Search Log → Practical Assignments

Date	Database	Search query	Number of results	Selected articles	Comments
15.01	Scopus	"employee engagement" AND "remote work"	342	15	Limited period 2019–2025
15.01	WoS	"work engagement" AND telework	128	8	Overlap with Scopus — 5 articles

Scopus

·Broad coverage of disciplines: natural sciences, engineering, medicine, social sciences, humanities
·Built-in metrics: CiteScore, SJR (SCImago Journal Rank), SNIP
·Ability to export results to reference managers
·“Analyze search results” function for trend visualization

Web of Science

·High selectivity: only journals that have passed rigorous screening
·Impact Factor — the main metric of journal quality (published in Journal Citation Reports)
·“Citation Report” tool for analyzing citations of publications
·Integration with EndNote for bibliography management

Google Scholar

·Advantages: free access, wide coverage, full-text search, “Cited by” function
·Limitations: lack of quality control for sources, limited filtering capabilities, non-reproducibility of searches (algorithm personalizes results)

Effective literature search is the cornerstone of quality research. It is not enough to simply enter keywords into a search box: you need to master search strategies, know the capabilities of academic databases, and be able to systematically organize the sources you find. In this article, we will...

Scopus is the largest abstract and citation database of peer-reviewed literature, covering more than 27,000 journals. Scopus provides tools for citation analysis, tracking publications of specific authors, and evaluating the quality of journals. Features of Scopus:

Web of Science (WoS) is one of the oldest and most authoritative databases, especially valued for its strict criteria for journal selection. WoS includes several indexes: Science Citation Index (SCI), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI). Key features:

Google Scholar is a free search tool indexing full texts and metadata of scientific publications. Its main advantages are the breadth of coverage and accessibility. However, Google Scholar does not strictly filter sources, so results may include unpublished works, preprints, and materials of ques...

Critical Reading and Writing a Literature Review

Introduction → 1. Structured Approach to Critical Reading → 2. Effective Note-Taking → 3. Synthesis vs. Summary → 4. The “Funnel” Structure → 5. Methods of Organizing the Review → 6. Typical Mistakes in Literature Reviews → 7. Quality Literature Review Checklist → Practical Assignments

Third Pass: Critical Evaluation

·Full bibliographic reference (APA format)
·Research aim
·Theoretical foundation
·Method: design, sample (size, characteristics), instruments
·Key findings (figures, statistical significance)
·Author-stated limitations
·Your critical assessment (strengths and weaknesses)
·Key quotations (with page numbers)
·Relevance to your research
·Thematic tags

Methodological Organization

·[ ] The review has a clear thematic structure (not organized “by author”)
·[ ] Peer-reviewed and reputable sources are used
·[ ] Sources are predominantly recent (last 5–10 years) + classical studies
·[ ] Synthesis is performed, not just summaries of individual papers
·[ ] Different viewpoints and contradictory results are presented
·[ ] Each topic is logically connected to the research question
·[ ] Smooth transitions exist between sections and paragraphs
·[ ] Funnel structure: from broad context to specific gap
·[ ] The research gap is clearly formulated
·[ ] The review justifies the necessity and relevance of your research
·[ ] Correct in-text citations and reference list

Assignment 1: Synthesizing Sources

·“Financial incentives increase short-term productivity” (Kim, 2019)
·“Intrinsic motivation has a stronger influence on long-term engagement” (Akhmedov, 2020)
·“A combination of material and non-material incentives is most effective” (Lee, 2021)

The ability to critically read scholarly articles and competently synthesize information from multiple sources is a key competency for a researcher. A literature review is not a mere list of annotations of the articles you have read, but an analytical work that demonstrates your deep understandin...

Critical reading of a scholarly article is an active process that requires a systematic approach. It is recommended to use the three-pass method:

Read the title, abstract, introduction, and conclusion. Browse through section headings and tables/figures. Answer the questions: What is this article about? What is the main aim? What are the key findings? Is it relevant to your research?

Read the article in full, paying attention to: research questions and hypotheses, theoretical framework, methodology (design, sampling, instruments, data analysis), results and their interpretation, limitations of the study.

Introduction to Quantitative Research

Basics of statistics, SPSS, types of data, descriptive statistics, and graphical data representation

Fundamentals of Quantitative Research and Statistics

What is Quantitative Research? → Types of Data → Descriptive Statistics → Graphical Presentation of Data → Practical Assignments

Definitions

Arithmetic mean (Mean): — the sum of all values divided by their number.
Median: — the value that divides the ordered data set in half.
Mode: — the most frequently occurring value.
Range: — the difference between the maximum and minimum values.
Variance: — the average squared deviation from the mean.
Standard Deviation: — the square root of the variance.
Bar Chart: — for nominal and ordinal data. Shows the frequency of each category.
Histogram: — for continuous numerical data. Shows the distribution of values by intervals.
Pie Chart: — for displaying category proportions in a whole. Use with caution: for a large number of categories the chart becomes unreadable.
Scatter Plot: — for visualizing the relationship between two numerical variables.
Box Plot: — displays the median, quartiles, range, and outliers. Very useful for comparing distributions.

Key Characteristics:

·Use of numerical data
·Statistical analysis
·Striving for objectivity and generalizability
·Testing of pre-formulated hypotheses (deductive approach)
·Large samples to ensure representativeness

Nominal Data

·Permissible operations: counting frequencies, mode
·Not allowed: ranking, calculating the mean

Ordinal Data

·Permissible operations: median, quartiles, rank correlation
·Not allowed: claiming that the difference between 1 and 2 is the same as between 3 and 4

Interval Data

·Permissible operations: mean, standard deviation, Pearson correlation
·Not allowed: saying that 40°C is "twice as hot" as 20°C

Ratio Data

·Permissible operations: all statistical operations, including proportions
·Permitted: stating that an income of 100,000 rubles is twice as much as 50,000 rubles

Quantitative research is a systematic approach to the collection and analysis of numerical data designed to describe, explain, and predict phenomena. Quantitative methods are based on the measurement of variables and the use of statistical tools to identify patterns and test hypotheses.

Understanding data types is critical, as the data type determines which statistical methods can be applied.

Categories without order. Examples: gender (male/female), nationality, company type (private/public), industry.

Categories with a certain order, but without equal intervals. Examples: education level (secondary/bachelor's/master's/PhD), Likert scale (1 = strongly disagree ... 5 = strongly agree), job level.

SPSS: Data Types and Working with Variables

SPSS Interface → Data Types: Categorical and Numeric → Setting Up Variables in Variable View → Entering and Importing Data → Coding Categorical Variables → Recoding Variables (Recode) → Computing New Variables (Compute Variable) → Practical Tasks

Definitions

Nominal: — categories without any natural order.
Ordinal: — categories with a defined order, but without equal intervals between them.
Interval: — numeric data with equal intervals, but without an absolute zero point.
Ratio data: — numeric data with an absolute zero.

Property	Description	Example
Name	Short variable name (no spaces, up to 64 chars)	vozrast, pol, dohod
Type	Data type: Numeric, String, Date, etc.	Numeric for numeric data
Width	Maximum number of characters	8
Decimals	Number of decimal places	0 for integers, 2 for decimals
Label	Full description of the variable (shows in tables)	"Respondent age"
Values	Value labels for coded variables	1 = "Male", 2 = "Female"
Missing	Definition of missing values	99 = missing value
Columns	Column width in Data View	8
Align	Cell data alignment	Right for numeric
Measure	Level of measurement	Nominal, Ordinal, or Scale

Name	Type	Label	Values	Measure
id	Numeric	Employee ID	—	Scale
vozrast	Numeric	Age	—	Scale
pol	Numeric	Gender	1=Male, 2=Female	Nominal
otdel	Numeric	Department	1=Sales, 2=Marketing, 3=IT, 4=HR	Nominal
stazh	Numeric	Years of work experience	—	Scale
udovl	Numeric	Job Satisfaction	1=Very low...5=Very high	Ordinal

Data View

·Columns represent variables (for example, "Age", "Gender", "Income")
·Rows represent observations (respondents, companies, cases)
·Each cell contains a single value for a particular variable of a particular observation

Categorical Data

·Examples: gender (1 = male, 2 = female), city of residence, company sector
·In SPSS: Measure = Nominal
·Examples: education level (1 = secondary, 2 = bachelor, 3 = master's, 4 = PhD), Likert scale
·In SPSS: Measure = Ordinal

Numeric (Quantitative) Data

·Examples: temperature in Celsius, year of birth, IQ score
·In SPSS: Measure = Scale
·Examples: age, income, number of employees, years of work experience
·In SPSS: Measure = Scale (SPSS does not differentiate between interval and ratio data)

Importing Data from CSV

·1 = Secondary
·2 = Bachelor
·3 = Master's
·4 = Doctorate (PhD)

Recode into Same Variables

·Transform → Recode into Same Variables
·Select the variable → click Old and New Values
·Specify old and new values → Add → Continue → OK

IBM SPSS Statistics is one of the most widespread programs for statistical data analysis in the social and business sciences. After launching the program, you work in the Data Editor window, which has two viewing modes:

This is the main table for data input and review. It resembles an Excel spreadsheet:

This is the mode for setting up variables. Here, each row corresponds to one variable, and the columns determine its properties. Switching between modes is accomplished by tabs at the bottom of the window.

Ordinal — categories with a defined order, but without equal intervals between them.

Descriptive Statistics and Graphs in SPSS

Measures of Central Tendency → Measures of Dispersion (Variance) → Measures of Distribution Shape → The Normal Distribution and Its Importance → Descriptive Statistics in SPSS → Creating Graphs in SPSS → Interpreting SPSS Output Tables → Frequency Tables and Crosstabulation → Practical Tasks

Statistic	Value	Interpretation
N	150	Number of valid observations
Mean	35.40	Average age value
Std. Deviation	8.72	Average spread from the mean
Skewness	0.45	Slight right skew
Std. Error of Skewness	0.198	For assessing the significance of skewness
Kurtosis	−0.32	Slightly platykurtic
Minimum	19	Minimum age
Maximum	62	Maximum age

Arithmetic Mean (Mean)

·Formula: x̄ = Σxᵢ / n
·When to use: for interval and ratio data with approximately normal distribution
·Limitations: sensitive to outliers—one extreme value can significantly shift the mean
·Example: the average salary of 10 employees is informative if there are no sharp outliers

Median

·Definition: the value that divides an ordered series of data exactly in half
·When to use: in the presence of outliers, for skewed distributions, for ordinal data
·Advantage: resistant to extreme values
·Example: median income better characterizes the "typical" income of the population than the mean, since incomes are distributed with right skew

Mode

·Definition: the most frequently occurring value in a dataset
·When to use: for nominal data (the only applicable measure of central tendency), for multimodal distributions
·Features: there can be several modes (bimodal, multimodal distribution) or no mode at all

Comparison of Measures by Distribution Type

·Symmetric distribution: Mean ≈ Median ≈ Mode
·Right skew (positive skewness): Mean > Median > Mode
·Left skew (negative skewness): Mean < Median < Mode

Range

·Formula: Range = Max − Min
·The simplest measure, but considers only the two extreme values and is very sensitive to outliers

Measures of central tendency indicate the "typical" or "central" value in a dataset. The choice of the appropriate measure depends on the type of data and distribution.

Measures of dispersion indicate how much values deviate from the center of the distribution.

Why important: many parametric tests (t-test, ANOVA, Pearson correlation, regression) assume normal distribution of data. Violation of this assumption can lead to incorrect results.

When performing analysis, SPSS displays results in the Output Viewer window. A typical descriptive statistics table contains:

Introduction to Qualitative Research

Definition of qualitative research, reflexivity, sampling, data coding, and using NVivo

Fundamentals of Qualitative Research

What is Qualitative Research? → Reflexivity → Sampling in Qualitative Research → Qualitative Data Analysis: Coding → Software for Qualitative Analysis → Practical Assignments

Definitions

Maximum variation sampling: — intentional selection of participants with maximally different characteristics in order to identify common patterns among differences.
Criterion sampling: — selection of all participants who meet a certain criterion. For example, all managers who have completed a leadership program in the past year.
Snowball sampling: — one participant recommends another. Useful when studying hard-to-reach groups.
Typical sampling: — selection of participants representing a “typical” case of the phenomenon.
1. Open coding: — initial “fragmentation” of data and assignment of a descriptive code to each fragment.
2. Axial coding: — grouping open codes into categories and establishing connections among categories.
3. Selective coding: — identifying the central theme or theory that connects all categories.
NVivo: — a popular program for computer-assisted qualitative data analysis (CAQDAS — Computer-Assisted Qualitative Data Analysis Software).

Key characteristics:

·Focus on understanding and interpreting phenomena
·Use of non-numeric data (words, images, observations)
·Inductive approach — theory is formed from data, not tested on them
·Small, purposive samples (purposive sampling)
·Contextuality — phenomena are studied in their natural environment
·The role of the researcher as the instrument of research
·Values and beliefs — cultural, religious, political
·Prior experience — professional and personal
·Theoretical preferences — commitment to certain schools of thought
·Social position — gender, age, ethnicity, class
·Keeping a reflexive journal, where the researcher records their thoughts, feelings, assumptions, and decisions during the research process
·Open discussion of their positionality — how the personal characteristics of the researcher may influence the research
·Decision audit — documenting why certain methodological decisions were made

Sample size

·In-depth interviews: 12–25 participants
·Focus groups: 3–5 groups of 6–10 participants each
·Case studies: 1–10 cases (depending on design)

Stages of coding:

·Undervaluation
·Opinion ignored
·Lack of feedback
·Hierarchical communication
·Organizing and storing data (interviews, documents, audio, video)
·Coding texts using “nodes”
·Visualizing connections between codes
·Creating queries to search for patterns
·Generating reports

Assignment 1

·Organizational barriers: [uncertainty of expectations], [insufficient supervisor communication]
·Personal adaptation: [adaptation difficulties], [development of self-organization]
·Results: [productivity increase]

Qualitative research is an approach to research aimed at a deep understanding of social phenomena through the study of the meanings that people attach to their experiences. Unlike the quantitative approach, qualitative research operates with words, images, and observations, rather than numbers.

Reflexivity is the ability of the researcher to be aware of and critically assess their own influence on the research process and results. This is one of the key principles of qualitative research.

Unlike quantitative research, which strives for representativeness and random sampling, qualitative research uses purposive sampling.

Maximum variation sampling — intentional selection of participants with maximally different characteristics in order to identify common patterns among differences.

Reflexivity and Sampling in Qualitative Research

What is Reflexivity and Why is it Important? → Types of Reflexivity → Reflexive Journal → Researcher Positionality → Sampling in Qualitative Research → Practical Assignments

Category	Examples of Entries
Methodological decisions	Why was this particular method chosen? What alternatives were considered?
Emotional reactions	What did I feel during the interview? What caused surprise?
Analytical notes	What patterns am I starting to notice? What assumptions are forming?
Ethical reflections	Did any ethical dilemmas arise? How did I resolve them?
Positionality reflections	How does my role influence relationships with participants?

Method	Recommended Size
Phenomenology	5–25 participants
Grounded Theory	20–30 participants
Case Study	1–5 cases (with multiple data sources)
Ethnography	1 cultural group (long-term observation)
Focus Groups	3–5 groups of 6–10 people each

·Increasing trustworthiness—demonstrating awareness of one's own biases strengthens trust in the results
·Ethical responsibility—understanding power relations between the researcher and participants
·Depth of analysis—awareness of one’s own position enables deeper data interpretation
·Transparency—the reader can assess to what extent the researcher’s subjectivity influenced the conclusions

Personal Reflexivity

·How does my personal experience influence the choice of research topic?
·How might my assumptions affect my interpretation of the data?
·How do research participants perceive me, and how does this influence their answers?

Epistemological Reflexivity

·How does the chosen methodology determine what can be "discovered"?
·How does the formulation of research questions limit possible answers?
·Could a different methodology have led to different conclusions?
·Insider—the researcher is part of the studied community or group. Advantages: deep understanding of context, trust from participants. Risks: “blind spots,” taking the obvious for granted.
·Outsider—the researcher does not belong to the studied group. Advantages: a fresh perspective, ability to notice the non-obvious. Risks: superficial understanding, participants’ mistrust.

Types of Purposive Sampling

·*Example:* Studying employees’ adaptation—selecting people of different ages, positions, tenure, departments.
·*Example:* Studying the experiences of women middle managers in IT companies.
·*Example:* Studying a typical workday of a project manager.
·*Example:* Studying companies that achieved exceptional growth during a crisis.
·*Example:* Studying informal entrepreneurs—each participant found points to others.
·*Example:* All employees who have worked at a company for more than 10 years and have gone through reorganization.

Sample Size and Data Saturation

·New interviews confirm already identified themes and categories
·No new codes or themes emerge in analysis of additional data
·The researcher can predict participants’ responses

Reflexivity is a conscious process of critical self-analysis by the researcher, aimed at understanding how their own beliefs, experience, social position, and theoretical preferences influence the research process and outcomes. Unlike the quantitative approach, where the researcher strives for ma...

Personal reflexivity involves reflecting on how the researcher’s personal characteristics—their values, beliefs, life experience, gender, ethnicity, socio-economic status—shape the research process. The researcher asks themselves questions such as:

Example: A researcher studying migrants’ experiences in the labor market who is a migrant themselves must recognize that their personal experience may both deepen their understanding of the issue and lead to projecting their own experiences onto participants.

Epistemological reflexivity focuses on analyzing how research decisions—the choice of methodology, theoretical framework, methods of data collection and analysis—shape the knowledge produced as a result of the study:

Coding Qualitative Data and Introduction to NVivo

What is Coding in Qualitative Research? → Types of Codes → The Initial (Open) Coding Process: Step-by-Step Guide → Rules for Effective Code Naming → From Codes to Categories → Introduction to NVivo → Main Components of the NVivo Interface → Creating and Managing Codes in NVivo → Practical Assignments

Rule	Example of a Good Code	Example of a Bad Code
Brevity (2–5 words)	LACK OF TIME	THE RESPONDENT SAYS THAT THEY LACK TIME
Specificity	CONFLICT WITH MANAGER	PROBLEMS
Uniform style	ADAPTING TO CHANGES	Adapts, CHANGES, adjusts
Reflects content	WORK-LIFE BALANCE	ITEM 7
Active form	RESISTING CHANGE	BAD

·Data reduction — condensing large volumes of text into manageable units
·Organization — grouping related fragments for comparative analysis
·Conceptualization — shifting from description to analytical categories
·Ensuring transparency — documenting the analytical process

Descriptive Codes

·Fragment: “Every morning I spend about an hour responding to emails before I can get to my main work.”
·Code: EMAIL MANAGEMENT

In Vivo Codes

·Fragment: “Here we’re all just ‘putting out fires’ every day, there’s no strategic thinking.”
·Code: “PUTTING OUT FIRES”

Process Codes

·Fragment: “At first, I tried to control everything myself, then I gradually started trusting the team...”
·Code: DELEGATING RESPONSIBILITY

Emotion Codes

·Fragment: “When they told me about the reorganization, I felt like the ground was slipping away under my feet.”
·Code: FEAR OF UNCERTAINTY

Coding is the process of assigning short labels (codes) to fragments of qualitative data (interview texts, field notes, documents) with the purpose of systematizing, organizing, and subsequently interpreting them. Coding is a fundamental stage of qualitative analysis and serves as a “bridge” betw...

According to Saldana (Saldana, 2021), a code is “a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute to a portion of linguistic or visual data.”

Descriptive codes summarize the topic of a data fragment in one word or phrase. They answer the question: “What is this fragment about?”

In vivo codes use the exact words of the participants themselves. They preserve the respondent’s “voice” and are especially useful for capturing unique expressions.

Philosophy of Research

Research paradigms, ontology and epistemology, positivism, interpretivism, critical realism, and research ethics

Paradigms, Ontology, and Epistemology

Research Paradigms → Ontology → Epistemology → Connection Between Philosophy and Methods → Practical Tasks

Aspect	Positivism	Interpretivism	Critical Realism
Approach	Deductive	Inductive	Retroductive
Methods	Quantitative	Qualitative	Mixed
Data	Numeric	Textual	Both types
Sampling	Large, random	Small, purposive	Varies
Analysis	Statistical	Thematic	Combined
Generalization	High	Low	Medium

Positivism

·Ontology: Objective reality exists independently of the observer
·Epistemology: Knowledge is based on observable, measurable facts
·Methodology: Quantitative methods, experiments, surveys, hypothetico-deductive method
·Goal: Explanation, prediction, generalization
·Researcher’s role: Objective observer, detached from the object of research

Interpretivism

·Ontology: Multiple subjective realities constructed by people
·Epistemology: Knowledge is created through understanding subjective meanings
·Methodology: Qualitative methods, interviews, observation, narrative analysis
·Goal: Understanding and interpreting subjective experience
·Researcher’s role: Active participant in the process of knowledge creation

Critical Realism

·Ontology: Reality exists objectively, but our knowledge of it is always incomplete
·Epistemology: Reality has three layers: the empirical (what we observe), the actual (what happens), and the real (underlying mechanisms and structures)
·Methodology: Mixed methods, retroduction (search for underlying causes)
·Goal: Identifying underlying causal mechanisms
·Researcher’s role: Critical analyst, striving to see beneath the surface of phenomena

Pragmatism

·Ontology: Reality is what “works”; abstract debates are less important than the practical usefulness of knowledge
·Epistemology: Knowledge is evaluated by its practical utility
·Methodology: Choice of methods is determined by the research question, not by philosophical beliefs
·Goal: Solving practical problems

Task 2

·The objectivist will say: “Leadership is a set of measurable behavioral characteristics that can be objectively observed and measured”
·The constructivist will say: “Leadership is a social construct that is created and reproduced through interactions among people. What is considered 'leadership' depends on culture, context, and the...
·Positivist: “We can measure leadership using standardized questionnaires (MLQ) and statistically analyze its relationship with performance outcomes”
·Interpretivist: “We can understand leadership by studying people’s stories and narratives about their experience of interacting with leaders through in-depth interviews”

Paradigm is a set of fundamental beliefs and assumptions about the nature of reality and about how we can know it. A paradigm determines which questions a researcher considers important and which methods are appropriate.

The choice of paradigm is not just an academic exercise; it fundamentally affects the entire research design: from the formulation of questions to the interpretation of results.

Ontology is a branch of philosophy that studies the nature of reality. In the context of research, ontology answers the question: “What exists? What is the nature of reality?”

Reality exists independently of our perception. Social phenomena have an objective nature that can be measured and studied just as natural phenomena can.

Methodological Approaches: Quantitative, Qualitative, and Mixed

Connection Between Philosophy and Methodology → Quantitative Methodology → Qualitative Methodology → Mixed Methods → Case Study → Deduction, Induction, and Abduction → Choice of Approach for the Research Question → Practical Exercises

Definitions

Quantitative methodology is based on a deductive approach: — the researcher starts with a theory, formulates hypotheses, and tests them using numerical data. Key features:
Qualitative methodology is based on an inductive approach: — the researcher collects data, analyzes them, and builds theory "from the ground up." Key features:

Method	Description	Example
Survey	Standardized questionnaires for a large number of respondents	Survey of 500 employees on job satisfaction
Experiment	Manipulation of the independent variable and measurement of the effect	A/B testing of two website designs
Secondary data	Analysis of existing numerical data	Financial statements of companies over 10 years
Structured observation	Counting of predetermined behavioral categories	Frequency of certain customer actions in a store

Method	Description	Example
In-depth interview	Semi-structured conversation with a participant	Interviews with entrepreneurs about startup experience
Focus group	Group discussion of a given topic	Discussion of a new product with 6-8 consumers
Observation (ethnography)	Immersion of the researcher in the studied environment	Observing the work of a team for 3 months
Document analysis	Study of texts, reports, archives	Analysis of corporate sustainability reports

Type	Description	When to apply
Single case	In-depth study of a single case	Unique, critical, or typical case
Multiple case	Comparative study of several cases	Replication of results, search for patterns
Embedded	Several units of analysis within one case	Analysis of divisions within an organization
Holistic	One unit of analysis for the entire case	The organization is considered as a whole

Main Characteristics

·Objectivity — the researcher seeks to minimize their influence on the results
·Measurability — all variables are operationalized and measured with numerical scales
·Large samples — to ensure statistical significance and the possibility of generalization
·Statistical analysis — the use of descriptive and inferential statistics
·Replicability — another researcher should get similar results under the same conditions

Statistical Analysis

·Descriptive statistics — mean, median, standard deviation, frequencies
·Correlation analysis — relationship between two variables (Pearson, Spearman coefficient)
·Regression analysis — prediction of the dependent variable based on independents
·t-test, ANOVA — comparison of means between groups
·Chi-square — analysis of categorical data

Main Characteristics

·Subjectivity as a resource — the researcher's personal understanding enriches interpretation
·Depth of understanding — emphasis on meanings, experience, and context
·Small samples — a small number of participants, but deep immersion in each case
·Non-numerical data — texts, images, observations, narratives
·Design flexibility — the research plan can adapt during data collection

Main Mixed Methods Designs

·*Example:* A survey of 300 employees revealed low engagement in a certain department → then interviews were conducted with 10 employees from that department to understand the reasons.
·*Example:* Interviews with 15 consumers revealed key factors for making choices → then a survey of 500 consumers was conducted to quantitatively assess the significance of each factor.
·*Example:* Simultaneous survey (questionnaires) and focus groups with company clients, results are compared for a complete picture.

The choice of methodological approach is not an arbitrary decision—it logically follows from the researcher's philosophical position. Ontological and epistemological beliefs define which methods are considered appropriate for obtaining knowledge. A positivist, believing in an objective measurable...

This connection is often described through the model of the "research onion" by Saunders (Saunders' Research Onion): from the outer layer (philosophy) to the inner (methods and data collection techniques), each subsequent layer is defined by the previous one.

Quantitative methodology is based on a deductive approach: the researcher starts with a theory, formulates hypotheses, and tests them using numerical data. Key features:

Qualitative methodology is based on an inductive approach: the researcher collects data, analyzes them, and builds theory "from the ground up." Key features:

Research Ethics

Why is Ethics Important in Research? → Key Ethical Principles → Avoidance of Harm → Informed Consent → Confidentiality and Anonymity → Data Protection and GDPR → Ethical Approval → Ethical Specifics of Qualitative Research → Ethics of Digital and Online Research → Practical Exercises

Definitions

Confidentiality: — the researcher’s obligation not to disclose data allowing the identification of the participant. The researcher knows who provided the data but does not reveal this information.
Anonymity: — a stricter standard: even the researcher cannot link specific data to a particular participant. Full anonymity is possible in online surveys without collecting identifiers.

Type of Harm	Description	Example
Physical	Bodily injury, fatigue, discomfort	Long laboratory sessions without breaks
Psychological	Stress, anxiety, recalling trauma	Questions touching on painful experiences
Social	Stigmatization, reputational damage, disruption of relationships	Disclosure of information about group affiliation
Economic	Financial losses, foregone benefit	Prolonged participation without compensation for work time

·Nuremberg Code (1947) — adopted after Nazi medical experiments; established the principle of voluntary consent
·Milgram Experiment (1963) — participants experienced serious psychological stress
·Stanford Prison Experiment (1971) — went out of control, causing psychological harm to participants
·Tuskegee Study (1932-1972) — African-American men did not receive treatment for syphilis without their knowledge

Respect for Persons

·Autonomy — recognition of the individual's ability to make independent decisions regarding participation in research
·Protection of Vulnerable Groups — additional measures for people with limited autonomy (children, those with cognitive impairments, prisoners)

Beneficence

·Maximizing benefit — the research should bring significant results for science and society
·Minimizing harm — potential risks to participants must be reduced to a minimum
·Balance of benefit and risk — expected benefits must outweigh possible risks

Justice

·Equal distribution of burden — the burden of research should not disproportionately fall on vulnerable groups
·Equal access to benefits — research results should be accessible to all interested groups
·Fair selection of participants — inclusion/exclusion criteria must be scientifically justified, not dictated by convenience or prejudice

What should informed consent include:

·Purpose of the study — general description of what is being studied and why
·Procedures — what exactly will be required from the participant (time, actions)
·Risks and discomforts — possible negative consequences of participation
·Benefits — what the participant or society will gain from the study
·Confidentiality — how participant data will be protected
·Voluntariness — the right to refuse or withdraw from the study at any moment without consequences
·Contact information — whom to contact with questions or complaints

Research ethics is a system of principles and rules regulating the behavior of the researcher towards participants, the scientific community, and society as a whole. Ethical violations can lead to serious consequences: harm to participants, loss of trust in science, legal liability, and discredit...

Historically, the necessity for ethical standards was recognized after a series of scandalous experiments:

These cases led to the creation of the Belmont Report (1979), which laid the foundation for modern research ethical principles.

In practice, this means: a participant must understand the essence of the research and have the opportunity to freely refuse participation without any consequences.

Preparing for the Assignment

Formatting requirements, academic integrity, plagiarism, citation and referencing rules

Academic Integrity and Formatting Requirements

Academic Integrity → Plagiarism → Citation Rules → Structure of Academic Work → Practical Assignments

Definitions

1. Verbatim plagiarism: — literal copying of text without quotation marks and attribution to the source.
2. Paraphrasing plagiarism: — retelling someone else's ideas in one's own words, but without indicating the source. Even if you completely rephrase the text, the idea belongs to another author and requires citation.
3. Patchwork/Mosaic plagiarism: — combining fragments from different sources into one text without proper citation.
4. Self-plagiarism: — repeated use of one's own previously submitted work without permission and indication.
5. Contract cheating: — obtaining a work from another person (for payment or for free) and presenting it as one's own.
1. Title page: — title, author, educational institution, date
2. Abstract: — brief summary of the entire work (150-300 words): aim, methods, key results, conclusions
3. Introduction: — context, justification of relevance, aims and objectives, structure of the work
4. Literature review: — critical analysis of existing research, theoretical framework, identification of gaps
5. Methodology: — philosophical foundations, research design, methods of data collection and analysis, ethical considerations
6. Results: — presentation of the obtained data (without interpretation)
7. Discussion: — interpretation of results in the context of the literature, discussion of significance and limitations
8. Conclusion: — main findings, recommendations, suggestions for future research
9. Reference list: — complete list of all cited sources
10. Appendices: — additional materials (questionnaires, data tables, consent forms)

Direct Quotation

·Enclose the text in quotation marks
·Indicate the author, year, and page number

Paraphrasing

·Do not use quotation marks
·Indicate the author and year

Harvard Referencing

·Single author: (Smith, 2020) or Smith (2020)
·Two authors: (Smith and Jones, 2020)
·Three or more: (Smith et al., 2020)
·Quotation: (Smith, 2020, p. 45)
·Book: Smith, J. (2020) *Title of Book*. 2nd edn. London: Publisher.
·Journal article: Smith, J. and Jones, K. (2020) 'Title of Article', *Journal Name*, 15(3), pp. 45-60.
·Website: Smith, J. (2020) *Title of Page*. Available at: URL (Accessed: 15 January 2020).

Academic Integrity is adherence to ethical standards in educational and research activities. It includes honest performance of work, proper citation of sources, and respect for intellectual property.

Plagiarism is presenting someone else's ideas, words, or work as one's own without proper attribution to the source. Plagiarism is a serious violation of academic ethics and can lead to disciplinary consequences.

1. Verbatim plagiarism — literal copying of text without quotation marks and attribution to the source.

2. Paraphrasing plagiarism — retelling someone else's ideas in one's own words, but without indicating the source. Even if you completely rephrase the text, the idea belongs to another author and requires citation.

Advanced Quantitative Methods

Secondary data, sampling methods, questionnaire design, measurement scales, and data entry in SPSS

Sampling and Questionnaire Design

Secondary Data → Sampling Methods → Questionnaire Design → Practical Assignments

Definitions

Convenience Sampling: — selection of the most accessible participants. Fast, but with high risk of bias.
Quota Sampling: — non-probability analogue of stratified sampling. Quotas are determined by characteristics, but within quotas selection is non-targeted.
Snowball Sampling: — each participant refers the next. Used for hard-to-reach groups.
Closed-ended questions: — the respondent chooses from the given options:
Open-ended questions: — the respondent answers freely. Provide rich data, but are complex to analyze.
1. Clarity of wording: — questions should be clear and unambiguous
2. Avoid double-barreled questions: — “Are you satisfied with your salary and working conditions?” → two separate questions
3. Avoid leading questions: — “Don’t you think that management is doing an excellent job?” → biased question
4. Logical sequence: — from simple to complex, from general to specific
5. Piloting: — testing the questionnaire on a small group before the main study
Validity: — does the instrument measure what it is intended to measure?
Reliability: — does the instrument yield consistent results upon repeated use?

1	2	3	4	5
Strongly disagree	Disagree	Neutral	Agree	Strongly agree

Sources of secondary data:

·Government statistics — Rosstat, Eurostat, OECD, World Bank
·Corporate reports — annual reports, financial statements
·Databases — COMPUSTAT, Bloomberg, Bureau van Dijk
·Previous research — published datasets

Advantages of secondary data:

·Saves time and resources
·Access to large volumes of data
·Possibility of longitudinal analysis
·Data are often of high quality (collected by professional organizations)

Disadvantages:

·Data may not fully match your research questions
·No control over data collection quality
·Possible relevance issues
·Limited information about collection methodology

Probability Sampling

·Each element has an equal probability of selection
·Uses a random number generator
·Requires a complete list of the population (sampling frame)
·Every k-th element from the list is selected
·$k = N / n$ (population size / desired sample size)
·Example: from 1000 employees, sample 100 $\rightarrow$ $k = 10$, select every 10th
·The population is divided into homogeneous subgroups (strata)
·A proportional number is selected from each stratum
·Example: 60% men and 40% women $\rightarrow$ in a sample of 100 people, 60 men and 40 women
·The population is divided into groups (clusters), usually geographically
·Entire clusters are randomly selected
·Example: randomly select 10 out of 50 company branches and survey all employees in the selected branches

Types of Questions:

·Dichotomous (Yes/No)
·Multiple choice
·Likert scale (1-5 or 1-7)

Secondary data are data that have been collected by other researchers or organizations for other purposes, but can be used in your research.

Each element of the population has a known probability of being included in the sample.

Convenience Sampling — selection of the most accessible participants. Fast, but with high risk of bias.

Quota Sampling — non-probability analogue of stratified sampling. Quotas are determined by characteristics, but within quotas selection is non-targeted.

Secondary Data and Their Use

What Are Secondary Data? → Sources of Secondary Data → Advantages of Using Secondary Data → Disadvantages and Limitations of Secondary Data → Assessing the Quality of Secondary Data → Using Secondary Data in SPSS → Practical Assignments

Definitions

Primary data: — data collected by the researcher specifically for the current research. They precisely fit the research objectives but require significant time and resource investments.
Secondary data: — data collected by other individuals or organizations for their purposes. The researcher adapts them to their research question. They are available faster and at lower cost, but may not fully meet the needs of the current project.

Advantage	Description
Time-saving	The data are already collected; the entire collection process need not be repeated
Cost-saving	Significantly cheaper than conducting one's own large-scale research
Large samples	Government surveys often cover thousands of respondents
Longitudinal comparisons	Trends can be tracked over long periods (e.g., data for 10–20 years)
High collection quality	Large organizations apply strict methodological standards
Possibility of cross-country comparisons	International databases allow comparison of countries and regions
Reproducibility	Other researchers can verify results by using the same data

Limitation	Description
Mismatch with objectives	Data were collected for other purposes and may lack the required variables
Obsolescence	Data may be too old for the current study
Unknown quality	The researcher did not control the collection process and is unaware of all errors
Differences in definitions	Operationalization of concepts may differ from what is needed
Limited access	Some data are paid or available only upon request
Aggregation	Data may be provided only in an aggregated form without access to individual responses
Lack of control	It is impossible to change the data collection instrument or add variables

1. Government Statistics

·Statistical bureaus — data on population, employment, incomes, prices, industrial production (for example, Rosstat in Russia, ONS in the United Kingdom, BLS in the USA)
·Central banks — financial and macroeconomic statistics (interest rates, inflation, money supply)
·Ministries and departments — sectoral data (education, healthcare, trade)
·International organizations — World Bank, IMF, UN, OECD publish cross-country comparative data

2. Corporate Sources

·Annual company reports — financial indicators, strategic initiatives
·Internal databases — records of sales, customers, enquiries, HR data
·Industry associations — market reviews, benchmarking
·Commercial databases — Bloomberg, Thomson Reuters, Statista

3. Academic and Research Sources

·Academic journals and publications — previously collected data from other researchers
·Dissertations and theses — appendices with data
·Data repositories — UK Data Archive, ICPSR, Harvard Dataverse
·Surveys and monitoring studies — World Values Survey, Eurobarometer, Global Entrepreneurship Monitor

4. Media and Archival Sources

·Newspapers and magazines — for content analysis
·Corporate archives — historical documents, meeting minutes
·Internet sources — websites, social networks, forums (subject to ethical guidelines)

Assignment 1

·Rosstat — data on the unemployment rate by region for each year
·Ministry of Internal Affairs of Russia — statistics on registered crimes by region
·Unified Interdepartmental Information and Statistical System (EMISS) — aggregated data from various agencies
·The longitudinal nature allows trends to be analyzed over 10 years
·Broad coverage (all Russian regions) ensures representativeness
·Standardized data collection methodology ensures data comparability
·Time-saving: primary data collection on such a scale would require enormous resources

Secondary data are data that were previously collected by someone else for different purposes but can be used by a researcher to solve their own research question. In contrast to primary data, which the researcher collects independently "first hand" (via surveys, interviews, experiments), seconda...

Primary data — data collected by the researcher specifically for the current research. They precisely fit the research objectives but require significant time and resource investments.

Secondary data — data collected by other individuals or organizations for their purposes. The researcher adapts them to their research question. They are available faster and at lower cost, but may not fully meet the needs of the current project.

Sekaran and Bougie (2016) emphasize that using secondary data is an important stage of any research: even if the researcher plans to collect primary data, they should first examine available secondary sources to formulate hypotheses and contextualize the problem.

Types of Questions and Survey Piloting

Types of Questions in a Survey → Question Formulation → Response Scale Design → Survey Structure and Flow → Survey Piloting → Coding Survey Data for SPSS → Practical Tasks

1	2	3	4	5
Completely disagree	Disagree	Neutral	Agree	Completely agree

Criterion	What to check
Clarity	Are all questions clear? Any ambiguities?
Time	How long does completion take? Is it too long?
Completeness of options	Are all necessary answer options present?
Logic of skips	Do conditional transitions (skip logic) work correctly?
Question order	Is the order logical? Is there any context effect?
Sensitive questions	Do questions cause discomfort or refusal?
Technical problems	Does the survey render correctly on different devices?

Number of Scale Points

·5-point scales — most common, offer sufficient variability with simplicity of completion
·7-point scales — provide more fine-grained differentiation of answers
·4- or 6-point (even) — remove the "neutral" middle option, forcing the respondent to take a side

Why Conduct Piloting?

·Identify unclear or ambiguous wordings
·Test skip logic and transitions
·Estimate survey completion time
·Detect missing answer options
·Test technical functionality (for online surveys)
·Assess respondents' overall impression of the survey

How to Conduct Piloting?

·"Think-aloud protocol" — respondent voices their thoughts while completing
·Debriefing interview — after completion, difficulties are discussed
·Analyzing answer patterns — identifying questions everyone skips or answers the same way

Principles of Coding

·Dichotomous questions: 0 = No, 1 = Yes
·Likert Scale: 1 = Completely disagree, ..., 5 = Completely agree
·Multiple choice (several answers): Each option is a separate dichotomous variable (0/1)
·Open-ended questions: Coded after content analysis into thematic categories

Task 2

·Distribute the survey in paper and online format (to test both channels)
·Conduct debriefing interviews with 5–7 pilot participants
·Record each respondent's completion time
·Completion time should not exceed 15 minutes
·All questions must be understood correctly (according to interview data)
·Percentage of missed responses per question must not exceed 5%
·No technical problems with online completion
·Sufficient variability in answers (no question should receive identical answers from all)

The choice of question type depends on the nature of the information the researcher wants to obtain. Each type has its advantages and limitations.

The respondent answers in their own words, without preset answer options. Example: "In your opinion, what is the main problem in the department's work?"

Advantages: allow for unexpected answers, rich data, deep understanding of opinions. Disadvantages: difficulty of coding and analysis, require more time from the respondent, possible irrelevant answers.

The respondent chooses from predetermined answer options. This is the main question type in quantitative research.

Qualitative Data Collection

Ethics and informed consent, interviews, semi-structured interviews, and focus groups

Research Ethics and Methods of Qualitative Data Collection

Research Ethics → Interview as a Data Collection Method → Focus Groups → Practical Assignments

Key Ethical Principles:

·Understand the purpose of the research
·Know what is expected of them (time, procedures)
·Be informed about possible risks and benefits
·Know how data will be used and stored
·Have the right to refuse to participate or withdraw at any time without negative consequences
·Confidentiality — the researcher knows who provided the data but does not disclose this information to third parties
·Anonymity — even the researcher does not know which data belong to a specific participant
·Data must be stored securely
·Pseudonyms are used in the report
·Minimization of physical, psychological, and social risks
·Special caution when studying sensitive topics (violence, health, financial status)
·Readiness to refer the participant to specialized help if necessary

Types of Interviews:

·Pre-prepared list of questions
·All questions are asked in a fixed order
·Minimal flexibility
·Essentially an oral questionnaire
·Suitable for quantitative analysis of responses
·Flexible list of topics/questions (interview guide)
·The researcher can change the order of questions, ask clarifying questions
·Balance between structure and flexibility
·The most common type in qualitative research
·Minimal preliminary structure
·Conversation is guided by the participant's interests
·Maximum depth, but complexity in analysis

Conducting a Semi-structured Interview

·Descriptive: "Tell me about your typical workday"
·Evaluative: "How do you assess the effectiveness of this program?"
·Comparative: "How does your current experience differ from your previous one?"
·Hypothetical: "If you could change one thing, what would it be?"
·Active listening — demonstration of attention, nodding, clarification
·Probing — deepening answers through additional questions
·Managing pauses — comfortable silence gives the participant time to think
·Neutrality — avoid reactions that might influence answers

Characteristics:

·Moderator directs the discussion according to set topics
·Group dynamics — participants react to each other’s statements, generating richer data
·Usually lasts 60–90 minutes
·Recorded on audio (with participants' consent)

Advantages:

·Group dynamics generate ideas that would not arise in an individual interview
·More time-efficient than individual interviews
·Opportunity to observe social interaction

Ethical principles of research are aimed at protecting the rights and well-being of participants. Every study involving humans must undergo an ethical review/approval.

Informed consent is usually formalized as a consent form, which the participant signs. For online studies, electronic consent is allowed.

4. Right to Withdraw Participants may cease participation at any time without explaining reasons and without negative consequences for them.

Interview is one of the main methods of qualitative data collection. It is a purposeful conversation between the researcher and the participant.

Interview: Types, Preparation and Conduct

Types of Research Interviews → Developing a Guide (Topic Guide) → Types of Interview Questions → Conducting the Interview → Recording and Transcribing Interviews → Recruiting Participants → Practical Tasks

Probing Questions

·Clarification: “What exactly do you mean when you say...?”
·Elaboration: “Could you tell me more about that?”
·Completion: “What happened after that?”
·Evidence: “Can you give a concrete example?”

Field Notes

·Criterion Sampling — selection by pre-set criteria (e.g., managers with more than 5 years of experience)
·Snowball Method — participants recommend other potential participants
·Maximum Variation — selecting participants with maximally varied experience to cover different perspectives

A research interview is a purposeful conversation between a researcher and a participant, in which the researcher seeks to gain a deep understanding of the respondent’s experiences, opinions, perceptions, and motivations. Depending on the degree of structure, three main types of interviews are di...

The researcher asks a pre-determined set of questions in a strictly fixed order. All respondents receive identical questions, formulated in the same way. Answers are usually coded according to pre-set categories.

When to use: when data standardization is needed for quantitative analysis; with a large number of respondents; when comparability of answers between participants is important.

Advantages: high reliability, ease of analysis, minimization of interviewer influence. Limitations: does not allow for in-depth exploration of unexpected topics, restricts spontaneity of responses.

Focus Groups: Planning and Moderation

What is a Focus Group? → Differences from Individual Interviews → Planning Focus Groups → Recruiting Participants → The Role of the Moderator → The Role of the Assistant (Helper) → Conducting the Session → Analyzing Focus Group Data → Advantages and Limitations of Focus Groups → Practical Assignments

Criterion	Individual Interview	Focus Group
Depth	Deep dive into individual experience	Broader range of opinions, but less depth
Interaction	Researcher–participant	Participant–participant (with moderation)
Data	Individual narratives	Group dynamics, points of agreement and disagreement
Sensitive topics	More suitable (confidentiality)	Less suitable (presence of others)
Group influence	Absent	Possible conformist behavior
Time per participant	45–90 minutes	10–15 minutes of speech per participant
Efficiency	1 participant per session	6–10 participants per session

·When studying collective opinions, norms, and values
·To explore how people form and justify their views in a social context
·When developing new products, services, or policies to obtain feedback
·In the early stages of research to identify key topics and formulate hypotheses
·When it is important to understand points of agreement and disagreement within the group

Group Composition

·Facilitation of discussion: guide the conversation, ensure all planned topics are covered, without imposing personal opinion
·Managing dominant participants: tactfully limit excessively active participants (“Thank you, that’s very interesting. What do the others think?”)
·Engaging quiet participants: address silent participants by name, ask them direct questions (“Maria, what has your experience been?”)
·Managing conflict: acknowledge differences of opinion as a valuable result, redirect destructive arguments
·Neutrality: do not express agreement or disagreement with statements, avoid evaluative reactions

Analysis Process

·Group dynamics generate more diverse ideas than individual interviews
·Efficiency: data from several participants in one session
·Allow observation of opinion formation in a social context
·Reveal points of agreement and disagreement within the group
·High ecological validity (opinions are formed in interaction, as in real life)
·Influence of group pressure (conformism) — participants may withhold unpopular opinions
·Domination of certain participants may suppress others
·Not suitable for sensitive or intimate topics
·More complex logistics (gathering 6–10 people in one place)
·Data are harder to analyze due to overlapping statements
·Results cannot be generalized to the general population (small, non-representative samples)

Assignment 1

·2 groups — rank-and-file employees
·2 groups — middle managers
·2 groups — employees directly interacting with clients

Focus Group is a qualitative data collection method in which a small group of participants (usually 6–10 people) discusses a specific topic under the guidance of a moderator. The key feature of a focus group is group dynamics: participants respond to each other’s statements, generating richer and...

A focus group guide (scenario) usually includes 5–6 key questions, each opening up into a group discussion. Questions should be open-ended and stimulate opinion exchange. A typical structure:

1. Introductory question (5 minutes) — a simple question to help participants get to know each other 2. Transition question (10 minutes) — introduces the main topic 3. Key questions (40–60 minutes) — 3–4 substantive questions central to the research 4. Concluding question (10 minutes) — summing u...

The optimal duration of a focus group is 90–120 minutes. The venue should be neutral and comfortable, with circular seating that ensures visual contact among all participants. It is necessary to provide equipment for audio or video recording, as well as light snacks and drinks to create an inform...

Quantitative Data Analysis

Statistical significance, hypothesis testing, confidence intervals, t-tests, analysis of variance (ANOVA), correlation, and regression

Statistical Significance and Hypothesis Testing

Inferential Statistics → Hypotheses → Statistical Significance → Errors in Hypothesis Testing → Confidence Intervals → Main Statistical Tests → Practical Assignments

Definitions

p-value: — this is the probability of obtaining the observed result (or a more extreme one), if the null hypothesis is true.
Type I Error: — rejection of a true null hypothesis (false positive result). Probability = α (usually 0.05).
Type II Error: — failure to reject a false null hypothesis (false negative result). Probability = β.
Confidence interval: — this is a range of values which, with a certain probability, contains the true value of the population parameter.
Independent samples t-test: — comparison of means of two independent groups.
Paired samples t-test: — comparison of means for one group under two conditions.
R² (coefficient of determination): — proportion of the variance in the dependent variable explained by the model. R² = 0.45 means the model explains 45% of the variation.

Formulas

Type I Error — rejection of a true null hypothesis (false positive result). Probability = α (usually 0.05).Simple linear regression: Y = a + bX

Interpretation of p-value:

·p < 0.05 — the result is considered statistically significant (standard threshold). The null hypothesis is rejected.
·p < 0.01 — the result is highly significant
·p < 0.001 — the result is very highly significant
·p > 0.05 — the result is not significant. There is no reason to reject the null hypothesis.
·95% confidence interval means: if the study is repeated 100 times, the true value will fall within the calculated interval in about 95 cases.

ANOVA (Analysis of Variance)

·If ANOVA shows a significant difference (p < 0.05), it means that at least one pair of groups differs significantly
·Post-hoc tests (Tukey, Bonferroni) are used to determine which groups specifically differ

Correlation

·r = +1: perfect positive correlation
·r = 0: no linear relationship
·r = −1: perfect negative correlation
·|r| < 0.3 — weak
·0.3 ≤ |r| < 0.5 — moderate
·|r| ≥ 0.5 — strong

Regression Analysis

·Y — dependent variable
·X — independent variable
·a — intercept
·b — regression coefficient (slope)

Assignment 2

·Higher salary → higher satisfaction (direct causal link)
·Higher satisfaction → better work → salary increase (reverse causality)
·A third variable (for example, education level) affects both variables (confounding variable)

Inferential statistics allows one to draw conclusions about the population based on sample data. Unlike descriptive statistics, which simply describes the collected data, inferential statistics enables one to generalize results.

States that there is no significant difference, relationship, or effect. This is the "status quo" hypothesis that we attempt to disprove.

*Example:* H₀: There is no statistically significant difference in employee satisfaction between office and remote workers.

States that a difference, relationship, or effect exists. This is the hypothesis we aim to confirm.

Confidence Intervals and t-Tests

Confidence Intervals: Concept and Interpretation → Link Between Confidence Intervals and Hypothesis Testing → One-Sample t-Test → Independent Samples t-Test → Paired Samples t-Test → Effect Size: Cohen's d → Assumption Checking (Assumptions) → Introduction to Analysis of Variance (ANOVA) → Practical Assignments

Formulas

Hypotheses: H₀: μ = μ₀ (the mean equals the benchmark value); H₁: μ ≠ μ₀ (the mean differs from the benchmark).Hypotheses: H₀: μ₁ = μ₂ (means of the two groups are equal); H₁: μ₁ ≠ μ₂ (means differ).Formula: d = (M₁ − M₂) / SD_pooled, where SD_pooled is the pooled standard deviation.

Value of d	Interpretation
0.2	Small effect
0.5	Medium effect
0.8	Large effect

Key Components of a Confidence Interval

·Point Estimate — the value of a statistic computed from the sample (for example, the arithmetic mean X̄)
·Confidence Level — the probability that the interval contains the true parameter (usually 95% or 99%)
·Margin of Error — the amount added to and subtracted from the point estimate

A confidence interval (CI) is a range of values within which the true value of a population parameter is likely to be found with a certain probability. A confidence interval is constructed based on sample data and reflects the uncertainty associated with using a sample instead of the entire popul...

Formula for the CI of the mean: CI = X̄ ± t(α/2) × (s / √n), where X̄ is the sample mean, t(α/2) is the critical value of the t-distribution, s is the sample standard deviation, n is the sample size.

Correct: "We are 95% confident that the population mean lies in the interval from 4.2 to 5.8."

Incorrect: "There is a 95% probability that the mean is in this interval." The true mean is a fixed value, not a random variable.

Correlation and Regression Analysis

Concept of Correlation → Pearson's Correlation Coefficient (r) → Spearman's Rank Correlation (ρ) → Interpreting the Strength of Correlation → Correlation Does Not Imply Causation → Chi-Square Test (χ²) for Categorical Data → Simple Linear Regression → Multiple Regression: Introduction → Practical Tasks

Definitions

Multicollinearity: — a problem arising when there is a high correlation between predictors. Checked in SPSS via Statistics → Collinearity diagnostics. A value of VIF (Variance Inflation Factor) > 10 indicates multicollinearity.

r	or	ρ	Strength of relationship
0.10 – 0.29	Weak correlation
0.30 – 0.49	Moderate correlation
0.50 – 1.00	Strong correlation

Types of Correlational Relationships

·Positive correlation — when one variable increases, the other also increases (for example, number of hours of preparation and exam results)
·Negative correlation — when one variable increases, the other decreases (for example, stress level and sleep quality)
·No correlation — changes in one variable are not systematically related to changes in the other (the correlation coefficient is close to zero)

Conducting Regression in SPSS

·Model Summary: R, R², Adjusted R² — show the quality of the model
·ANOVA table: F-statistic and Sig. — tests the significance of the model as a whole. If Sig. < 0.05, the model is statistically significant
·Coefficients table: values of a (Constant) and b (predictor coefficient), standard errors, t-statistic, and p-value for each coefficient. The B column contains unstandardized coefficients, Beta — s...

Correlation is a statistical measure describing the degree and direction of the linear relationship between two variables. Correlation analysis allows us to answer the question: do two variables change in agreement?

Pearson's coefficient (Pearson's r) measures the strength and direction of the linear relationship between two interval or ratio variables. The values of r range from −1 to +1.

When to use: both variables are measured on an interval/ratio scale; the relationship between the variables is linear; the data are approximately normally distributed; there are no significant outliers.

Steps in SPSS: Analyze → Correlate → Bivariate → transfer the variables into the Variables list → make sure Pearson is checked → choose the test type (Two-tailed or One-tailed) → OK.

Qualitative Data Analysis

Thematic analysis, coding and developing themes, presenting qualitative data, and ensuring quality

Thematic Analysis

What is Thematic Analysis? → Six Stages of Thematic Analysis (according to Braun and Clarke) → Presentation of Qualitative Results → Ensuring Quality in Qualitative Research → Practical Exercises

Definitions

Theme: "The Paradox of Flexibility": — participants describe remote work as simultaneously liberating (flexible schedule, autonomy) and restrictive (blurred work/life boundaries, feeling of constant availability), creating an internal contradiction in their experience.
Credibility: — analogous to internal validity:
Transferability: — analogous to external validity:
Dependability: — analogous to reliability:
Confirmability: — analogous to objectivity:

Data Fragment	Codes
"The manager never gives feedback, I don't know if I’m working well or not"	[lack of feedback], [uncertainty], [interaction with management]
"I like that I can plan my remote day myself"	[autonomy], [schedule flexibility], [self-organization]
"I miss the conversations at the coffee machine, it was an important part of my workday"	[social isolation], [informal communication], [loss of rituals]

Stage 1: Familiarisation with the Data

·Multiple readings of interview transcripts
·Listening to audio recordings
·Recording first impressions and ideas
·Goal: immerse oneself in the data and begin to notice recurring patterns

Stage 2: Generating Initial Codes

·Systematic assignment of codes to interesting fragments of data
·Coding the entire data set, not just what confirms expectations
·Each fragment can receive multiple codes
·Codes are the smallest units of analysis

Stage 3: Searching for Themes

·Grouping codes into potential themes
·Creating a thematic map—a visualization of the connections between codes and themes
·A theme is something that reflects a significant pattern in the data
·Codes: [lack of feedback], [uncertainty], [social isolation], [informal communication], [technical communication problems]
·Codes: [autonomy], [schedule flexibility], [self-organization], [self-discipline], [time management]

Stage 4: Reviewing Themes

·Checking whether themes work in relation to the coded fragments and the entire dataset
·Combining, splitting, or discarding themes as needed
·Creating the final thematic map
·Internal homogeneity—the data within the theme is consistent
·External heterogeneity—the themes are clearly distinct from each other
·The theme reflects a meaningful pattern, not just a singular observation

Stage 5: Defining and Naming Themes

·Clearly defining the essence of each theme
·Creating brief, accurate names
·Writing a detailed description of each theme

Thematic Analysis is a method for analyzing qualitative data aimed at identifying, analyzing, and describing patterns (themes) in the data. It is one of the most widespread and accessible methods of qualitative analysis.

Braun and Clarke (2006) define thematic analysis as a method for "identifying, analyzing, and reporting patterns (themes) within data." It can be used within various philosophical paradigms.

*Example:* Theme: "The Paradox of Flexibility" — participants describe remote work as simultaneously liberating (flexible schedule, autonomy) and restrictive (blurred work/life boundaries, feeling of constant availability), creating an internal contradiction in their experience.

Presenting the results as a coherent narrative, supported by quotations from the data.

From Codes to Themes: Practicing Thematic Analysis

Introduction → Phase 1: Familiarization with the Data → Phase 2: Initial Coding → Phase 3: Searching for Themes → Phase 4: Reviewing Themes → Phase 5: Defining and Naming Themes → Phase 6: Writing Up → Typical Errors in Thematic Analysis → Practical Assignments

Potential Theme	Constituent Codes
Work–family conflict	Physical exhaustion, lack of time with children, guilt, inability to combine roles
Organizational injustice	Unequal task distribution, “glass ceiling,” formal vs real equality
Coping strategies	Delegation of duties, lowering standards, seeking social support

Key Actions

·Multiple readings of transcripts — at least two or three full readings of each interview or document
·Note-taking (memos) — recording first impressions, interesting fragments, preliminary ideas
·Active reading — not passive perception of the text, but seeking meanings, patterns, and contradictions
·Checking transcript quality — comparing the transcript with the audio recording if necessary

Approaches to Coding

·Line-by-line coding — assigning a code to each meaningful line or sentence. Provides maximal detail
·Open coding — generating codes without pre-existing categories, “bottom-up” from the data
·Paragraph coding — assigning codes to larger semantic units

Example of Coding an Interview Fragment

·Physical exhaustion after work
·Work–family conflict
·Parental guilt
·Lack of time with children
·Inability to combine roles

Coding Recommendations

·Code all data, not just what matches expectations
·One fragment may have several codes
·Use descriptive codes (what is said) and interpretive codes (what is implied)
·Keep a codebook — a list of all codes with definitions and examples

Level 1: Checking Against Coded Extracts

·Reread all data fragments assigned to each theme
·Make sure fragments consistently support the theme
·If fragments don’t align — reconsider the theme: split it, merge with another, or move codes

Thematic analysis (TA) according to the Braun & Clarke model includes six consecutive phases, each requiring systematic analytical work. In this article, each phase is considered in detail with practical examples, typical mistakes, and recommendations for implementation.

The goal of the first phase is deep immersion in the data. The researcher must “know” their data so well that they freely navigate its content.

1. Annotating — writing comments in the margins of the transcript: “The participant describes a conflict between work and family,” “Emotional reaction when discussing the supervisor” 2. Creating a brief summary for each interview (1–2 paragraphs): main topics, tone, key quotes 3. Keeping a reflex...

Sample memo: “Interview 3: the female participant repeatedly returns to the topic of injustice. She uses the metaphor ‘glass ceiling.’ Contrast between formal equality policy and actual experience.”

Writing Qualitative Research Results

Structure of the Results Chapter → Presentation of Themes with Data Extracts → Short Embedded Quotes vs Block Quotes → Balance of Description and Interpretation → Anonymization of Participants → Tables and Visual Elements → Writing Style of Qualitative Results → Linking Results to Literature → Quality and Rigor in Reporting Results → Practical Assignments

Theme	Subthemes	Description
Invisible labor	Emotional labor; Domestic work	Unpaid and unrecognized labor performed predominantly by women
Coping strategies	Delegation; Lowering standards	Mechanisms for adapting to multiple role demands
Institutional barriers	Formal policy; Informal practices	The gap between declared equality and real practices

Selection of Quotes

·Vividness and expressiveness — the quote should be compelling and lively
·Representativeness — the quote should reflect typical experience, not an exceptional case (unless precisely a deviant case is being analyzed)
·Variety of sources — use quotes from different participants, don't rely on 2–3 of the most eloquent ones
·Relevance — the quote should directly support the analytic claim

Block Quotes

·Description — what participants said, what experiences they described, what events they mentioned
·Interpretation — what this means in the context of the research question, how it’s connected to theoretical frameworks, what latent meanings can be identified

Approaches to Anonymization

·Pseudonyms — replacing real names with fictitious names (e.g., "Anna", "Dmitry"). Gives the text humanity, but can unintentionally create stereotypical associations
·Code designations — use of alphanumeric codes (e.g., "Participant 5", "P7", "R-12"). A more neutral approach, often used in academic publications
·Descriptive characteristics — indicating relevant demographic data without identification (e.g., "woman, 34 years old, mid-level manager")

Principles of Anonymization

·Remove or alter all identifying details: organization names, geographic references, unique positions
·Be consistent in the use of the chosen system throughout the text
·When using pseudonyms, consider cultural context (do not assign names inconsistent with the ethnic or cultural background of the participant)

Language and Terminology

·Avoid jargon unless necessary—the text should be accessible to a broad academic audience
·Use participant language (in vivo codes) to convey their own categories and meanings
·Be cautious with generalizations—in qualitative research, say "most participants", "some participants", not "all" or precise percentages
·Compare the results with existing theory and empirical data
·Explain matches and discrepancies with previous studies
·Show the contribution of this study to knowledge development
·Discuss theoretical implications – how the results expand, refine, or refute existing concepts

The results chapter of a qualitative study is different from that of a quantitative study—it represents an analytic narrative rather than a set of tables and statistical measures. A typical structure includes:

1. Brief introduction — reminder of the research question, method of analysis, number and nature of participants 2. Overview of themes — a brief listing of the identified themes (often as a table or diagram) 3. Expanded presentation of each theme — a separate section for each theme with subthemes...

Participant quotes are the foundation of a qualitative report. When selecting quotes, use the following criteria:

Quotes should never "hang in the air"—they require contextualization. Before the quote, explain what it illustrates; after the quote, comment on the contribution it makes to the argument.