Mediating variable is a variable that explains the correlation between the two variables

What is a mediator? A mediator is an inner part of the relationship between cause and effect. A lot of novices researchers have confusion between moderator and mediator variables. Let us first understand what we mean by the mediator variable and its application in the conceptual framework. What is Mediating Variable? Mediating variable is a variable that explains the correlation between the two variables. It finds its use in the conceptual framework and helps the researcher to develop a thorough understanding of the way in which the independent variable influences the dependent variable. It primarily aids in helping to understand the facts given the relationship existing between the independent and dependent variable and the way in which the independent variable influences the dependent variable. In order to understand clearly, we can name the mediating variable as an intervening variable, which comes in between the independent and dependent variables and tells that how these variables are interrelated. For example, if variable X causes the variable Y, then the mediating variable A will identify the factor which shows the kind of relationship X and Y have with each other. The application of mediator variable in the conceptual framework Let us look at an example to understand the application of the mediator variable in the conceptual framework. If a researcher is studying the impact of late-night phone usage on the sleeping hours of the respondents, the independent variable is going to be the phone usage and the number of hours spent sleeping be the dependent variable. Now, for the purpose of expansion of the conceptual framework, the mediating variable is going to be stress level. The mediating variable is introduced between the dependent and independent variable in the process of establishing cause-and-effect relationships between the dependent and the independent variable. A lot of researchers find it challenging to interpret the mediating variable....

An Overview of the Methodology and Reporting Standards for Umbrella Reviews

What are Umbrella Reviews? Definition Essentially, Umbrella reviews are a cluster of existing systematic reviews on a shared topic. They are called by many names, overview of reviews being the most common one. Umbrella reviews are overarching in nature as they aggregate the findings from different reviews and address the specific questions. Each of the umbrella reviews focuses on a broad condition or problem in which there are more than two or at least two potential interventions and brings out the reviews that address these potential interventions and their results. The past few decades have seen a phenomenal rise in publications by researchers. This has increased the need for evidence synthesis so that the knowledge about specific topics can be collated and summarized. This practice helps in creating substantive evidence for policy makers, clinicians, researchers, educators which is useful across a variety of issues. A lot of literature review techniques are available and all of them have their own methods. Some of the common literature review techniques that are used by researchers are, systematic review, scoping review, Integrative, Narrative, and the qualitative methodologies are the most common to see. Reviews are being used extensively as a secondary data source for they help in summarizing huge volumes of published evidence in some of the fields of research. There is plethora of primary and secondary sources that are available. This has made the task of researchers and readers difficult, where they must spend a vast amount of time sourcing, collating, and analyzing the compiled summaries into meaningful conclusions. The umbrella methodology comes to rescue here. It overcomes the problem by synthesizing findings from so many sources to report on a single source of research. Background of Umbrella Reviews There is an increase in the number of publications using the umbrella technique. As the name is suggestive, umbrella technique brings the literature...

How does Data Cleaning impact the quality of Data Analysis in your dissertation? Check out our expert-approved blog

If you want to open and grow your business, you first have to check if your product or service is market fit or not. We also did the same with this blog. But do you know that we failed FOUR times before it reached you? But what is the reason? It’s not that we didn’t know data cleaning and how to implement it to improve the quality of the data analysis. Then what is it? Can you guess it? After failing to gain traction with our blog for the 4th consecutive time, then one of our low-grade researchers told us this, “I think people don’t know why to implement data cleaning, that’s why maybe they are not reading this”. We were shocked by that answer. Then in the 5th time when we incorporated this point in our blog, then all the readers got engaged in our research-backed blog. In this blog, we are not only going to talk about the impact of data cleaning to improve the quality of your data analysis but also we are going to save a lot of your precious time so that you don’t face this problem again. This blog has been divided into 4 parts and more. To understand the other portions of the blog, you need to study the initial 4 parts. So, let’s get started  🙇. How to Analyze data in research The process of analysing data in research involves several steps. Here is a general overview of the steps involved in analyzing data in research: Prepare the data: This involves organizing and cleaning the data, which includes checking for missing values, removing outliers, and transforming the data into a format suitable for analysis. Explore the data: This step involves examining the data using descriptive statistics and visualizations to identify patterns, trends, and relationships in the data. This can be done...

How does Data Cleaning impact the quality of Data Analysis in your dissertation? Check out our expert-approved blog

If you want to open and grow your business, you first have to check if your product or service is market fit or not. We also did the same with this blog. But do you know that we failed FOUR times before it reached you ? But what is the reason? It’s not that we didn’t know data cleaning and how to extract the qualitative data required for the dissertation  . Then what is it? Can you guess it? After failing to gain traction with our blog for the 4th consecutive time, then one of our low-grade researchers told us this, “I think people don’t know why to implement data cleaning, that’s why maybe they are not reading this”. We were shocked by that answer. Then in the 5th time when we incorporated this point in our blog, then all the readers got engaged in our research-backed blog. In this blog, we are not only going to talk about the impact of data cleaning to improve the quality of your data analysis but also we are going to save a lot of your precious time so that you don’t face this problem again. This blog has been divided into 4 parts and more. To understand the other portions of the blog, you need to study the initial 4 parts. So, let’s get started  🙇.     How to Analyze data in research The process of analysing data in research involves several steps. Here is a general overview of the steps involved in analyzing data in research: Prepare the data: This involves organizing and cleaning the data, which includes checking for missing values, removing outliers, and transforming the data into a format suitable for analysis. Explore the data: This step involves examining the data using descriptive statistics and visualizations to identify patterns, trends, and relationships in the data. This can be...

Survey Questionnaire Development: Benefits of Professional Help. What to look for when working with an expert questionnaire development service?

Questionnaire surveys are a technique for gathering statistical information about the attributes, attitudes, or actions of a population by a structured set of questions. Survey Questionnaire designing is a multistage process that requires attention to many details at once.   What are questionnaires? Questionnaires can be a simple survey form that any researcher can use to collect basic data about their research participants. It’s easy to conduct a questionnaire survey, but it can also be straightforwardly simple, since questions don’t have to be complex to yield useful information. For example, you can ask participants to indicate the extent to which they agree, disagree, or are unsure about a set of statements. These types of questions can be useful for finding patterns in people’s thoughts and feelings. More complicated questionnaire surveys can be conducted to collect more detailed information about the participants. These surveys are more complicated to conduct, but can yield more detailed information about your research participants.   Issues in questionnaire development Depending on the purpose of your questionnaire, you will have different issues that you must address in questionnaire development. If you want to understand the attitudes of your research participants, you can increase understanding by focusing on the issues below. Participants’ competencies – participant ability to answer the questions; Participants’ interests – the types of questions participants like to answer; Participants’ intentions – their ability to answer the questions honestly; Participants’ motivations – why participants are answering the questions; Participants’ perceptions – their ability to understand the questions. Benefits of professional help in questionnaire development   A good questionnaire development process is the key to creating good surveys, which in turn will help you get the information you need to make good decisions. Here are some of the benefits of professional help in questionnaire development: Better Understanding – A questionnaire development professional can help you better understand...