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...

Measuring relationship strength in meta analysis

The primary function of the procedures described so far is to help meta-analysis accept or reject the null hypothesis .Until recently,most researchers interested in social theory and the impact of social interventions have been content to simply identify relations that have some explanatory value.The prevalence of this “yes or no” question was partly due to the relativity recent development of the social sciences. Social hypothesis were crudely stated first approximation to the truth .Social researchers rarely asked how potent theories or interventions were for explaining human behaviour or how competing explanations compare with regard to their relative explanatory value .Today, as their theories and interventions are becoming more sophisticated ,social scientists are more often making enquiries about the size of relationship.   Giving further impetus to the “how much?” question is a growing disenchantment with the null hypothesis significance test itself.If an ample number of participants are available if a sensitive research design is employed ,a rejection of the null hypothesis meta-analyses that include combined significance level ,where the power is not guarantee that an important social insight has been achieved.   Finally,when used in applied social research ,the vote count and combined significance-level techniques give no information on whether  the effect of a treatment or the relationship between variables is large or small,important or trivial.For example, if we find the relationship between whether a particular (a) is a male and (b)believes that women share some culpability when a rape occurs is statistically significant and the correlation is r = .01,is this a ered? What if the result is statistically significant and the correlation is r =.30? This example suggests that the “yes or no?” question is not the question of greatest important .Instead, the important question is , “How much does the sex sex of the participant influence beliefs about rape?” The answer might be zero or it...

Open Ended Questions – How to devise open ended questions in your survey questionnaire for PhD research

An open-ended question is an open question where the response is recorded verbatim. An open-ended question is nearly always an open question. (It would be wasteful to record yes-no answers verbatim.)Open-ended questions are also known as ‘unstructured’ or ‘free response’ questions. Open-ended questions are used for a number of reasons: The researcher cannot predict what the responses might be, or it is dangerous to do so. Questions about what is liked and disliked about a product or service should always be open-ended, as it would be presumptuous to assume what people might like or dislike by having a list of pre-codes. We wish to know the precise phraseology that people used to respond to the question. We may be able to predict the general sense of the response but wish to know the terminology that people use. We may wish to quote some verbatim responses in the report or the presentation to illustrate something such as the strength of feeling that respondents feel. In response to the question ‘why will you not use that company again?’, a respondent may write in: ‘They were that awful. They mucked me for months, didn’t respond to my letters and when they did they could never get anything right. I shall never use them again.’ Had pre-codes been given on the questionnaire this might simply have been recorded as ‘poor service’.The verbatim response provides much richer information to the end-user of the research. Through analysis on the verbatim responses, clients can determine if the customer is talking about a business process, a policy issue, a people issue (especially in service delivery surveys), etc. This enables them to determine the extent of any challenges they will face when reporting the findings of the survey to their management. Common uses for open-ended questions include : Likes and dislikes of a product, concept, advertisement, etc; Spontaneous...

Preparing for Interviews – Guide for Qualitative Research

There are many different types of question that can be asked and in many different ways. What is common to all questions, though, is that they must be worded in a way that is understood by the respondents and to which respondents can relate. This means ensuring that there are minority-language versions of the questionnaire if the sample is likely to include people who speak a language is unlikely to be sufficiently good to be able to complete an interview in it. By denying sections of the survey population the opportunity to participate in the study, the questionnaire writer is effectively disenfranchising them from influencing the findings. For many studies commissioned by the public sector in countries, it is important that the interview is capable of being conducted in any language that is spoken by a significant number of people in the any language that is the spoken by a significant number of people in the survey population to avoid the danger of disenfranchisement. In the UK Many government studies require questionnaire versions in Welsh, Urdu, Hindi and other languages, and in USA a Spanish-language version will often be required.  The relevance of Minority-language speakers will naturally vary by the subject of the study and degree of accuracy required by the data. For a study of housing conditions it is likely to be important that recently arrived immigrant communities are represented in a sample in correct proportions. If the questionnaire is not available in a language that they understand, they will be effectively excluded and hence under-represented.  For many commercial studies , the issues of minorities can be mostly ignored in many countries, although Spanish versions of questionnaires are frequently necessary in the USA. This is because the most commercial studies the difference that a minority or non- majority language speaking speakers is likely to make the finding small...

An introduction to Data Mining & its Applications in Bioinformatics

With the increasing importance of bioinformatics in agriculture, molecular medicine, microbial genome applications, etc. the research in this field has gained momentum than ever before. Bioinformatics, also known as computational biology, deals with interpreting biological data by using computer science and information technology. Of lately, research in bioinformatics has produced vast amounts of data and will continue to generate proteomic, genomic, etc. data. To analyze and gain deep insights into such biological data necessitates making sense of the information by inferring the data. For instance, gene classification, protein structure prediction, clustering of gene expression data, protein-protein interactions, etc. These processes, in turn, increases the need for interaction between bioinformatics and data mining.   Data mining, also regarded as Knowledge Discovery in Database (KDD) is the automated extraction of patterns that represent the knowledge stored or captured in large sets of data. Some of the steps included in the KDD process are data collection, selection, transformation, visualization, and assessment of extracted knowledge.  The processes involved in mining precise and meaningful data pattern are: Classification – This involves learning of a function that classifies input data items into predefined classes.   Estimation – It shows value for unknown variables with a given data input value.   Prediction – Although the prediction involves classification and estimation, data will be classified based on the future estimated value.  Association rules – Also called as dependency modeling, association rule identifies data associated and the possible outcomes.  Clustering – This involves segmenting the population into clusters or subgroups. In bioinformatics, data mining leverages genetic algorithms and statistical techniques from machine learning, statistics, databases, artificial intelligence, etc. Additionally, generally mining systems including SPSS, SAS enterprise miner, S-plus, Microsoft SQL server 2000, SGI MineSet, IBM intelligent miner, etc. can be utilized for mining biological data.  So, what is the need for data mining in bioinformatics? Biologists, after performing rigorous studies on...