Concept of measurement in Quantitative research

 What is a concept?

Concepts are the building blocks of theory and represent the points around which business research is conducted.

Each represents a label that we give to elements of the social world that seem to have common features and that strike us as significant.

If a concept is to be employed in quantitative research, it will have to be measured. Once they are measured, concepts can be in the form of independent or dependent variables.

Why measure?

There are three main reasons for the preoccupation with measurement in quantitative research.

1. Measurement allows us to delineate fine differences between people in terms of the characteristic in
question.

2. Measurement gives us a consistent device or yardstick for making such distinctions. 

3. Measurement provides the basis for more precise estimates of the degree of relationship between concepts (for example, through correlation analysis)

Indicators

In order to provide a measure of a concept (often referred to as an operational definition, a term deriving from the idea of operationalization), it is necessary to have an indicator or indicators that will stand for the concept.

Quantitative researchers generally have four main preoccupations: they want their research to be measurable, to focus on causation, to be generalisable, and to be replicable.

Causality

There is a very strong concern in most quantitative research with explanation. Quantitative researchers are
rarely concerned merely to describe how things are, but are keen to say why things are the way they are. This emphasis is also often taken to be a feature of the ways in which the natural sciences proceed.

Generalization

In quantitative research the researcher is usually concerned to be able to say that his or her findings can be generalized beyond the confi nes of the particular context in which the research was conducted. Thus, if a study of motivation to work is carried out by a questionnaire with a number of people who answer the questions, we often want to say that the results can apply to individuals other than those who responded in the study.

Quantitative research Process

 Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research, which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

 

 

 

 

1. Theory 

 

The fact that we start off with theory signifies that a broadly deductive approach to the relationship business researcher collects data. 

 

2. The specification of hypotheses to be tested is particularly likely to be found in experimental research. Although other research designs sometimes entail the testing of hypotheses, as a general rule, we tend to 

find that Step 2 is more likely to be found in experimental research.

3. the selection of research design has implications for a variety of issues, such as the external validity of findings and researchers’ ability to impute causality to their findings.

4. Operationalizing concepts is a process where the researcher devises measures of the concepts which she wishes to investigate. This typically involves breaking down abstract sociological concepts into more specific measures which can be easily understood by respondents.

 

5. With laboratory experiments, the site will already be established, in field experiments, this will involve the selection of a field-site or sites, such as a school or factory, while with survey research, site-selection may be more varied. Practical and ethical factors will be a limiting factor in choice of research sites.

Of course some research may take place over multiple sites.

6. Step six involves ‘choosing a sample of participants’ to take part in the study – which can involve any number of sampling techniques, depending on the hypothesis, and practical and ethical factors. If the hypothesis requires comparison between two different groups (men and women for example), then the sample should reflect this.

7. Step seven, data collection, is what most people probably think of as ‘doing research’. In experimental research this is likely to involve pre-testing respondents, manipulating the independent variable for the experimental group and then post-testing respondents.

In cross-sectional research using surveys, this will involve interviewing the sample members by structured-interview or using a pre-coded questionnaire. For observational research this will involve watching the setting and behaviour of people and then assigning categories to each element of behaviour.

8. This means transforming information which has been collected into ‘data’. With some information this is a straightforward process – for example, variables such as ‘age’, or ‘income’ are already numeric.

 

9. the researcher uses a number of statistical techniques to look for significant correlations between variables, to see if one variable has a significant effect on another variable.

10. On the basis of the analysis of the data, the researcher must interpret the results of the analysis. It is at this stage that the findings will emerge: if there is a hypothesis, is it supported? What are the implications of the findings for the theoretical ideas that formed the background of the research?

11. Finally, in stage 11, the research must be written up. The research will be writing for either an academic audience, or a client, but either way, a write-up must convince the audience that the research process has been robust, that data is as valid, reliable and representative as it needs to be for the research purposes, and that the findings are important in the context of already existing research.

Once the findings have been published, they become part of the stock of knowledge (or ‘theory’ in the loose sense of the word) in their domain. Thus, there is a feedback loop from step eleven back up to step one.

Pros of qualitative research:

• Provides rich and in-depth data: Qualitative research can provide a wealth of information about people's experiences, thoughts, and feelings. This data can be used to understand the complexities of human behavior and to develop new insights into social problems.

• Can be used to explore new areas of research: Qualitative research is well-suited for exploring new areas of research where there is little existing knowledge. It can be used to identify important questions and to generate hypotheses for further study.

• Is flexible and adaptable: Qualitative research is a flexible methodology that can be adapted to a variety of research questions and contexts. This makes it a valuable tool for researchers who are working with diverse populations or who are studying sensitive topics.

• Can be used to understand the context of data: Qualitative research can be used to understand the context in which data is collected. This is important because the meaning of data can be influenced by the social and cultural context in which it is produced.

Cons of qualitative research:

• Can be time-consuming and expensive: Qualitative research can be time-consuming and expensive to collect and analyze. This is because it often involves in-depth interviews, focus groups, or observations.

• Can be subjective: Qualitative research is subjective because it is based on the researcher's interpretation of data. This means that different researchers may come to different conclusions from the same data.

• Can be difficult to generalize: Qualitative research is often difficult to generalize to a wider population. This is because the findings are based on a small sample of participants.

• Can be biased: Qualitative research can be biased if the researcher is not careful to control for their own biases. This can be done by being aware of their own biases and by using a variety of data collection methods.

  Reference: https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/
  
  

Experimental Design: Concept of Independent & Dependent variables

Independent and dependent variables are two fundamental concepts in experimental design. They help researchers understand the relationship between different factors in an experiment.

Independent Variable

The independent variable is the variable that the experimenter manipulates or changes. It is the presumed cause of the effect that is being measured in the experiment. For example, in an experiment to investigate the effect of fertilizer on plant growth, the amount of fertilizer applied would be the independent variable.

Dependent Variable

The dependent variable is the variable that is measured or observed in an experiment. It is the presumed effect of the independent variable. In the plant growth experiment, the height of the plants would be the dependent variable.

Relationship between Independent and Dependent Variables

The independent variable is thought to cause changes in the dependent variable. However, it is important to note that there may be other factors that also affect the dependent variable. These are called extraneous variables. It is important for experimenters to control for extraneous variables as much as possible in order to isolate the effect of the independent variable.

Example

Let's consider an experiment to investigate the effect of sleep deprivation on memory performance. In this experiment, the independent variable would be the amount of sleep (e.g., 0 hours, 4 hours, 8 hours). The dependent variable would be memory performance, which could be measured by a test that assesses recall and recognition.

Types of Experimental Designs

There are different types of experimental designs of research. They are:

• Pre-experimental Research Design
• True-experimental Research Design
• Quasi-Experimental Research Design

Pre-experimental Research Design

The simplest form of experimental research design in Statistics is the pre-experimental research design. In this method, a group or various groups are kept under observation, after some factors are recognised for the cause and effect. This method is usually conducted in order to understand whether further investigations are needed for the targeted group. That is why this process is considered to be cost-effective. This method is classified into three types, namely,

• Static Group Comparison
• One-group Pretest-posttest Experimental Research Design
• One-shot Case Study Experimental Research Design

True-experimental Research Design

This is the most accurate form of experimental research design as it relies on the statistical hypothesis to prove or disprove the hypothesis. This is the most commonly used method implemented in Physical Science. True experimental research design is the only method that establishes the cause and effect relationship within the groups. The factors which need to be satisfied in this method are:

• Random variable
• Variable can be manipulated by the researcher
• Control Groups (A group of participants are familiar with the experimental group, but the experimental rules do not apply to them)
• Experimental Group (Research participants where experimental rules are applied)

Quasi-Experimental Design

A quasi-experimental design is similar to a true experimental design, but there is a difference between the two.

In a true experiment design, the participants of the group are randomly assigned. So, every unit has an equal chance of getting into the experimental group.

In a quasi-experimental design, the participants of the groups are not randomly assigned. So, the researcher cannot make a cause or effect conclusion. Thus, it is not possible to assign the participants to the group.

Apart from these types of experimental design research in statistics, there are other two methods used in the research process such as randomized block design and completely randomized design.

 Steps in experimental design process

The experimental design is a set of procedures that are designed to test a hypothesis. The process has five steps: define variables, formulate a hypothesis, design an experiment, assign subjects, and measure the dependent variable.

References 

1. https://www.scribbr.com/methodology/experimental-design/

2. https://byjus.com/maths/experimental-designs/

3. https://study.com/academy/lesson/experimental-design-in-science-definition-method.html