Sampling techniques can be divided into two categories: probability and non-probability. In probability sampling, each population member has a known, non-zero chance of participating in the study. Randomization or chance is the core of probability sampling technique. Probability sampling methods use some form of random selection. Therefore, application of this method offers the highest chance of creating a sample that is truly representative of the population. In non-probability sampling, on the other hand, sample group members are selected non-randomly; therefore, in non-probability sampling only certain members of the population has a chance to participate in the study. The table below illustrates the main differences between probability and non-probability sampling methods. Probability sampling Non-probability sampling Randomly selected samples Subjective judgement of researchers are used to select samples Equal chance for each member of population to get selected Not everyone has equal chance to get selected Used to reduce a sampling bias Researcher is not overly concerned with sampling bias Effective to collect data from diverse population Useful in specific environment with sampling group members sharing similar characteristics Useful in obtaining accurate representation of population Does not help in representing the population accurately Finding correct audience is difficult Finding correct audience is simple Probability sampling comprises the following sampling techniques: Simple random sampling Stratified random sampling Systematic random sampling Multistage random sampling Cluster sampling   Application of Probability Sampling: an Example Let’s suppose, your dissertation topic is ‘A study into employee motivation of ABC Company and the ways of increasing it’. You chose survey primary data collection method to achieve research objectives. Sampling process comprises the following four stages: 1. Identifying an appropriate sampling frame based on your research question(s) and objectives. ABC Company has 400 employees and accordingly, your sampling frame would be 400. 2. Determining a suitable sample size. You…

Questionnaires are a data collection method that involves asking respondents a set of structured questions to gather information efficiently. They can be used for both quantitative and qualitative research depending on the type of questions included. On this page: Types of Questionnaires Types of Questions Advantages & Disadvantages When to Use Questionnaires Aspect Quantitative Questionnaires Qualitative Questionnaires Question type Closed-ended Open-ended Data type Numerical Textual Analysis Statistical Thematic Output Charts, tables Insights, interpretations Questionnaires at a glance Questionnaires involve: Asking the same questions to many people Collecting answers in a structured format Analysing responses to identify patterns They are ideal for collecting large amounts of data quickly. Types of Questionnaires Questionnaires can be classified as both, quantitative and qualitative method depending on the nature of questions. Specifically, answers obtained through closed-ended questions (also called restricted questions) with multiple choice answer options are analyzed using quantitative methods. Research findings in this case can be illustrated using tabulations, pie-charts, bar-charts and percentages. Answers obtained to open-ended questionnaire questions (also known as unrestricted questions), on the other hand, are analyzed using qualitative methods. Primary data collected using open-ended questionnaires involve discussions and critical analyses without use of numbers and calculations. There are following types of questionnaires: Computer questionnaire. Respondents are asked to answer the questionnaire which is sent by mail. The advantages of the computer questionnaires include their inexpensive price, time-efficiency, and respondents do not feel pressured, therefore can answer when they have time, giving more accurate answers. However, the main shortcoming of the mail questionnaires is that sometimes respondents do not bother answering them and they can just ignore the questionnaire. Telephone questionnaire. Researcher may choose to call potential respondents with the aim of getting them to answer the questionnaire. The advantage of the telephone questionnaire is that, it can be completed during…

Observation is a data collection method where the researcher gathers information by systematically watching behaviours, events, or interactions in their natural setting. It is commonly used in qualitative research to understand how people act rather than what they say. On this page: What is Observation in Research? Structured vs Unstructured Observation Overt vs Covert Observation Advantages and Disadvantages Ethical Issues in Observation When to Use Observation Aspect Structured Observation Unstructured Observation Approach Pre-defined variables Open and flexible Data collection Systematic Exploratory Comparability High Limited Use case Quantitative / formal studies Qualitative / exploratory studies Observation at a glance Observation means: Watching what people do Recording behaviours and interactions Analysing real-life situations It helps researchers understand actual behaviour, not just reported opinions. What is Observation in Research? Observation, as the name implies, is a way of collecting data through observing. This data collection method is classified as a participatory study, because the researcher has to immerse herself in the setting where her respondents are, while taking notes and/or recording. Observation data collection method may involve watching, listening, reading, touching, and recording behavior and characteristics of phenomena.   Structured vs Unstructured Observation Observation as a data collection method can be structured or unstructured. In structured or systematic observation, data collection is conducted using specific variables and according to a pre-defined schedule. Unstructured observation, on the other hand, is conducted in an open and free manner in a sense that there would be no pre-determined variables or objectives.   Overt vs Covert Observation This data collection method can be divided into overt or covert categories. In overt observation research subjects are aware that they are being observed. In covert observation, on the other hand, the observer is concealed and sample group members are not aware that they are being observed. Covert observation is…

Qualitative data collection methods are exploratory in nature and are mainly concerned with gaining insights and understanding on underlying reasons and motivations. Qualitative data is a linguistic or visual material. Qualitative data collection methods emerged after it became evident that traditional quantitative data collection methods were unable to express human feelings and emotions. Monette et al (2010)[1] credit qualitative methods with the acknowledgement of abstraction and generalisation. Polonsky and Waller (2011)[2] categorize vision, images, forms and structures in various media, as well as, spoken and printed word and recorded sound into qualitative data collection methods. Qualitative data collection methods are used in order to examine the following phenomenon: Human feelings and experiences Meanings and relationships Social norms and cultural practices. It is noted that “qualitative methods are often regarded as providing rich data about real life people and situations and being more able to make sense of behaviour and to understand behaviour within its wider context. However, qualitative research is often criticised for lacking generalizability, being too reliant on the subjective interpretations by researchers and being incapable of replication by other researchers.”[3] Popular qualitative data collection methods used in business studies include interviews, focus groups, observation and action research. Moreover, grounded theory and document analysis can be also used as data collection method in qualitative studies.  The main sources and procedures associated with the most popular qualitative methods are presented on Table 1 below as proposed by Yamagata-Lynch (2010)[4]: Methodology Sources Procedure Document analysis Reports, newsletters, publications Read all materials and documented and descriptive statistics related to the research issue Interviews Primary participants Secondary participants Tape recorded semi-structured interviews, then transcribed the interviews for the participants to review Observations Observed participants’ interactions Took notes and videotaped the observations Exit interviews    Primary participants Secondary participants Presented findings to participants during…

Futures research can be defined as a systematic study of possible future events and circumstances. As a field of study, futures evolved in 1950s. Futures research is different from forecasting in a way that the former has a forward orientation and looks ahead, rather that backwards, and is not as mathematical as forecasting. It can be argued that highly dynamic nature of the contemporary global marketplace is making is difficult to conduct quality futures studies. For example, it is now evident that COVID-19 global pandemic has compromised findings of many futures studies. This is because no researcher could foresee the occurrence of extent of implications of pandemic on global business and economics. Disadvantages of futures research are straightforward – no event or situation can be forecasted in an accurate and complete manner. Nevertheless, futures studies may offer a substantial advantage. Specifically, although futures studies are not able to produce totally accurate and complete information about the future, some researched information about the future is better than no information at all when engaging in decision making for long-term perspective. There is wide range of techniques available that can be used to conduct futures studies. The following table illustrates types and techniques of the most popular futures techniques. Method Quantitative Qualitative Normative Exploratory Agent  Modeling ü        ü    Complexity based models ü        ü    Cross Impact Analysis ü        ü    Decision Models ü        ü    Delphi   ü    ü    ü    Econometrics ü        ü    Environmental Scanning   ü      ü    Futures Wheel   ü    ü    ü    Genius Forecasting   ü    ü    ü    Morphological Analysis   ü    ü      Participatory Methods   ü    ü      Regression ü        ü    Relevance Trees   ü    ü      Scenarios ü    ü    ü    ü    Science Road Maps   ü      ü   …

Quantitative data collection methods are techniques used to gather numerical data that can be measured and analysed statistically. They are used to answer questions such as “how many?” and “how often?” and to test relationships between variables. On this page: What are Quantitative Data Collection Methods? Quantitative vs Qualitative Methods Advantages & Limitations Aspect Quantitative Methods Qualitative Methods Data type Numerical Textual Purpose Measure and test Explore and understand Sample size Large Small Analysis Statistical Thematic Approach Deductive Inductive Output Charts, tables Insights, interpretations Quantitative vs qualitative methods at a glance Quantitative research involves: Collecting numerical data Measuring variables Analysing relationships using statistics This type of research helps answer questions like:“How much?”, “How many?”, and “How often?” What are Quantitative Data Collection Methods? Quantitative research methods describe and measure the level of occurrences on the basis of numbers and calculations. Moreover, the questions of “how many?” and “how often?” are often asked in quantitative studies. Accordingly, quantitative data collection methods are based on numbers and mathematical calculations. Quantitative research can be described as ‘entailing the collection of numerical data and exhibiting the view of relationship between theory and research as deductive, a predilection for natural science approach, and as having an objectivist conception of social reality’[1]. In other words, quantitative studies mainly examine relationships between numerically measured variables with the application of statistical techniques.   Quantitative vs Qualitative Methods Quantitative data collection methods are based on random sampling and structured data collection instruments. Findings of quantitative studies are usually easy to present, summarize, compare and generalize. Qualitative studies, on the contrary, are usually based on non-random sampling methods and use non-quantifiable data such as words, feelings, emotions ect. Table below illustrates the main differences between qualitative and quantitative data collection and research methods: Quantitative Qualitative Requirement Question Hypothesis Interest Method…

Data collection methods refer to the techniques used to gather information in order to answer research questions, test hypotheses, and achieve research objectives. In business research, data can be collected from primary sources (original data) or secondary sources (existing data). On this page: What is Data Collection Methods? Secondary Data Collection Methods Primary Data Collection Methods How to Choose Data Collection Methods   Feature Primary Data Secondary Data Definition Original data collected for the study Existing data collected by others Cost Higher Lower Time required Longer Shorter Relevance Highly specific May be less specific Examples Surveys, interviews Books, journals, reports Differences between primary and secondary data collection   What is Data Collection Methods? Data collection is a process of collecting information from all the relevant sources to find answers to the research problem, test the hypothesis (if you are following deductive approach) and evaluate the outcomes. Data collection methods can be divided into two categories: secondary and primary methods of data collection. Data collection methods must be carefully aligned with research aims, objectives, and research questions. In other words, the type of data collected and the method used should directly facilitate the achievement of research objectives. Poor alignment between research objectives and data collection methods may result in irrelevant findings and reduced research validity. Recent developments in digital technologies have significantly transformed data collection methods in business research. Online surveys, social media analytics, digital tracking tools, and AI-powered data collection platforms are increasingly used by students to gather large volumes of data efficiently. These technologies enable students and other researchers to access real-time data and analyse behavioural patterns at scale. When using AI-based tools for data collection, you must ensure transparency, data accuracy, and compliance with ethical guidelines.   Secondary Data Collection Methods Secondary data is a type of data…

Descriptive research refers to a type of research design that aims to accurately describe characteristics, behaviours, or phenomena as they exist at present, without manipulating variables. This type of research answers questions such as what, where, when, and how, rather than explaining why something occurs. On this page: • Meaning of descriptive research and key characteristics• Methods used in descriptive research• Examples of descriptive research• Advantages and disadvantages   Feature Descriptive Research Exploratory Research Causal Research Purpose Describe characteristics or phenomena Explore a problem Identify cause-and-effect relationships Research questions What, where, when, how What, why, how Why (cause-effect) Structure Structured Flexible Highly structured Outcome Detailed description Insights and ideas Causal conclusions Methods Surveys, observation, case studies Interviews, focus groups Experiments Research designs at a glance   Meaning of Descriptive Research Descriptive research can be explained as a statement of affairs as they are at present with the researcher having no control over variable. Moreover, “descriptive studies may be characterised as simply the attempt to determine, describe or identify what is, while analytical research attempts to establish why it is that way or how it came to be”[1]. Three main purposes of descriptive studies can be explained as describing, explaining and validating research findings. This type of research is popular with non-quantified topic. Descriptive research is “aimed at casting light on current issues or problems through a process of data collection that enables them to describe the situation more completely than was possible without employing this method.”[2] To put it simply, descriptive studies are used to describe various aspects of the phenomenon. In its popular format, descriptive research is used to describe characteristics and/or behaviour of sample population. It is an effective method to get information that can be used to develop hypotheses and propose associations. Importantly, these types of studies…

Exploratory research design aims to explore a research problem that is not clearly defined. The purpose of exploratory research is to gain insights, identify patterns, and develop a better understanding of the phenomenon under investigation rather than to provide final and conclusive answers. Exploratory studies are typically conducted at the early stages of research when limited information is available about the research topic. On this page: Meaning of exploratory research Key characteristics Exploratory research methods Differences between exploratory and conclusive research Advantages and disadvantages of exploratory research Feature Exploratory Research Conclusive Research Purpose Explore a problem and generate insights Provide final answers and support decision-making Structure Flexible and open-ended Structured and systematic Hypotheses Usually not formulated Often tested Sample size Small Large Data analysis Qualitative and interpretive Quantitative and statistical Outcome Preliminary insights Definitive conclusions Differences between exploratory and conclusive research   Meaning of Exploratory Research Exploratory research, as the name implies, intends merely to explore the research questions and does not intend to offer final and conclusive solutions to existing problems. This type of research is usually conducted to study a problem that has not been clearly defined yet. Conducted in order to determine the nature of the problem, exploratory research is not intended to provide conclusive evidence, but helps us to have a better understanding of the problem. When conducting exploratory research, the researcher ought to be willing to change his/her direction as a result of revelation of new data and new insights.[1] Accordingly, exploratory studies are often conducted using interpretive research methods and they answer to questions such as what, why and how. Exploratory research design does not aim to provide the final and conclusive answers to the research questions, but merely explores the research topic with varying levels of depth. It has been noted that “exploratory…

Applied research is a type of research that focuses on solving specific, real-world problems faced by organisations or society. It uses existing theories and methods to generate practical solutions and actionable recommendations. On this page: What is Applied Research? Key Characteristics of Applied Research Types of Applied Research Applied Research vs Fundamental Research Examples in Business Studies Advantages and Disadvantages When to Use Applied Research Feature Applied Research Fundamental Research Main objective Solve specific real-world problems Develop general theories and knowledge Focus Practical application Theoretical understanding Context Often conducted within organizations Usually conducted in academic environments Outcomes Practical recommendations Conceptual or theoretical contributions Time horizon Short- to medium-term solutions Long-term knowledge development Applied vs fundamental research at a glance Applied research means: Identifying a real problem Analysing it using research methods Providing practical solutions It answers the question:“How can this problem be solved in practice?” What is Applied Research? Applied research “aims at finding a solution for an immediate problem facing a society, or an industrial/business organisation, whereas fundamental research is mainly concerned with generalisations and with the formulation of a theory”[1]. Applied research is considered to be non-systematic inquiry and it is usually launched by a company, agency or an individual in order to address a specific problem.[2] This type of research in a business dissertation plays a crucial role in bridging the gap between academic theory and practical application. It aims to address real-world business challenges and provide solutions that can be implemented in organizations.   Key Characteristics of Applied Research  Applied research has the following key characteristics: Focus on a specific problem or issue. This type of research typically focuses on a specific problem or issue faced by businesses. This could be anything from improving marketing strategies to increasing employee satisfaction. Practical relevance. The research findings must be relevant and…