- Standard Methodology of Analysing data
- Tips to follow while reporting the results of a Quantitative study
- Step by Step Process to ensure a robust Qualitative Data Analysis:
What is Data Analysis?
Data Analysis is the process of inspecting, rearranging, modifying and transforming data to extract useful information from it.
Maintaining the integrity of the data is crucial for a data analyst to procure accurate and appropriate analysis. A credible data analyst should have the skills to analyse the statistics of the data and turns the data into actionable insights. Improper analysis always distorts the scientific findings and lead the readers to believe in a wrong notion. Inappropriate analysis not only limits into numerical data but also affects non-statistical data too if the analyst has integrity issues.
Standard Methodology of Analysing data
This section covers the various methodology analysts can use to answer each research question and research hypotheses. While attending to a research question analysts must describe the descriptive statistics used while answering the question.
While analysing research hypotheses describe in details about the inferential statistic used to investigate the hypotheses. Analysts can also give the formula for the statistics provided they are simple statistics such as mean, median, percentage, etc. However, advanced statistics such as ANOVA is too complicated to mention.
As usage of statistics differs as per the necessity of the research studies, it’s crucial for every analyst to have proper knowledge in all statistical methods. A researcher has to examine each research question and hypothesis individually and assign appropriate statistics to it.
All research questions are answerable in descriptive statistics such as in percentage or mean. When analysts want to know about the total number of participants who gave one particular answer, he/she must use percentage. A percentage is ideal when the respondent falls under distinct categories such as male or female, employed or unemployed, vegetarian or non-vegetarian etc. When the data falls under such discrete categories analysts can also report frequencies under it And they might provided with loans from loanigo co.uk. Suppose there are 100 cases in a sample and analysts want to know how much people fall under a particular group, in such a situation usage of percentage is vital.
When analysts want to know a typical response of all the participants, then he/she can use mean in the report. Mean is use when the reactions are continuous. Data that involves numbers which continue from one point to other are reported using mean. Example of some continuous data is ages of participants, scores of students in exams etc.
Following Are acceptable norms for disciplines
When it comes to data analysis, every field holds its accepted practices. However, the standard rules of data analysis are based on two factors:
- It could be Quantitative or Qualitative variables.
- An assumption about the population: The assumptions could be random distributions, sample sizes, independence etc.
In some cases, analysts are allowed to use unconventional norms as well provided they clearly state the reason behind using the alternative standards. Apart from that, the analyst must also indicate how the new method could bring a significant difference from traditional methods.
The primary purpose of using conventional approach while data analysis is to establish a standard of acceptability for statistical significance. Analysts must also discuss the importance of attaining statistical relevance and whether their objectives are met using the conventional approach.
Defining the objective outcome measurements
No matter how much sophisticatedly a statistical analysis is conducted, it cannot correct poorly defined objective outcome measurements. Analysts who lack skills in identifying the objective and outcome often left the report filled with misinterpretation, which often misleads the readers.
Provide honest and accurate analysis
The main motto of data analysis is to reduce the statistical error. Issues often faced by analysts during the process include:
- Filling the missing data
- Altering the data
- Data mining
- Creating graphical representations of data
Presentation of data
There comes a time when analysts have to decide how to present the derived data in such a way that it would be more impressionable. To do that, investigators choose how much, why, when and whom to show the data, Even sometimes analysts manipulated data, they must also keep a record or a paper trail regarding why and in how much intensity the data are managed for future reviews.
Sometimes environment or context plays a significant role while procuring the data. The answers of the respondents often differ during one-on-one interviews and focus groups. In the focus group, the number of participants comprises a large number, and it often changes the repose of a person. When conducting data analysis, the researcher must keep the environmental factor into account as well.
Methods to record data
Analysis of an analyst may differ based on the process of recording the data.
Various types of methods include:
- Data Collected in audio or video format for transcribing later
- Data collected by a researcher or it may be a self-administered survey
- Is it a close-ended survey or open-ended
- Notes were taken by the researcher, or accepted by the participants and later submitted to the researchers.
Partitioning the text
Raters are staff researchers who analyse text materials during content analysis. Now while examining text materials, some evaluators tend to take comments as a whole while others dissect the texts into words, clauses, sentences etc. To maintain the data integrity, its essential from the part of raters to eliminate inconsistencies in analysis among themselves.
Training of Analysts
While working on inductive techniques, it’s important that the analysts are adequately trained and supervised by skilled personnel. In content analysis, raters assign topics to text materials. Now if the evaluators don’t perceive the material as it is supposed to be, then it will spoil the data integrity. Due to lack of training, the assigning skills of one staff researcher might differ tremendously from another researcher. To combat the challenges organisations must draft a proper protocol manual, train their analysts periodically and monitor them routinely.
Reliability and Validity
Reliability and validity of the research are the most crucial aspects of a study, and an analyst must know that whether he/she is working on quantitative data or qualitative data. Coders often re-code the same data over in the same way over a period. Such actions tamper data reliability and invalidate the data.
All researchers must fully aware of the potential for compromising the integrity of data whether the method includes statistical or non-statistical. The most statistical analysis focuses on quantitative data, but there are a lot of analytic procedures which concentrates mostly on qualitative materials such as thematic, ethnographic analysis, and content analysis.
Whether an analysts study quantitative or qualitative phenomena, he/she have to utilise a range of tools to test hypotheses, analyse the behavioural pattern and reach a conclusion. Improper implementation and interpretation skills may result in a compromise data integrity.
Quantitative Data Analysis
In this process, analysts use their rational and critical thinking to turn raw numbers into meaningful data. In quantitative analysis, analysts calculate the frequency of variables and differences between variables to support or reject hypotheses. Most of the hypotheses include researches and findings one conducted at the early stages of the research process. Analysing quantitative data also requires a fair and careful judgement of an analyst as the same figure within data can result in multiple interpretations.
The primary objective of conducting a quantitative research study is to figure out the relationship between one independent variable, with a dependent variable, within a population. The designs of quantitative research are often descriptive or experimental. While a descriptive study finds the association between variables, and experimental studies figure out the causality between variables.
Quantitative data analysis deals with numbers, credible objectives and logic with the help of numeric and unchanging data. It focuses on convergent reasoning and discards different rationale.
Main characteristics of Quantitative Data Analysis:
- The data collected in Quantitative Research are precise and structured.
- In Quantitative Research, the results rely mostly on large sample sizes.
- Due to its high reliability, researchers can replicate and repeat the studies.
- The research questions in Quantitative research are clearly defined with an intention to seek precise answers related to the objectives.
- All collected data comprises of numbers and statistics. The analysts rearranged the received data into tables, figures, charts and non-textual forms.
The purpose of the projects resides with an objective to find a broader aspect of a general concept. It also investigates causal relationships between two ideas along with predicting the future results of the research.
To collect numerical data, researchers use structured questionnaires either in physical form to obtain answers from respondents. Nowadays most researchers use computer software to conduct the research. With an aim to classify features between two subjects, the researchers count the data, construct statistical models and carry out an in-depth observation.
Tips to follow while reporting the results of a Quantitative study
- Analysts must explain all the collected data in details along with all the statistical treatment utilised while investigating the research problems. However, in this section analysts does not need to explain his/her interpretation.
- Analysts must report all the limitations and unanticipated events that happened while collecting the data. The actual analysis often differs from the planned analysis. Explain in details how planned, and actual analysis differs in the current study. The Analysts must also explain how he/she handled the missing data while explaining the reason why the missing data does not undermine the credibility of the analysis.
- Cleaning the data set is quite reasonable. Analysts must mention the techniques they used to clean their data set
- No need to use complex and critical statistical methods unless its mandatory. Always provide sufficient reason for using the analytical process. Analysts must also mention the computer program and references used while preparing the report.
- All assumptions taken by analysts must be discussed systematically. Besides, analysts must suggest various steps taken by him/her to conduct the report without violating any rules.
- If an analysts use inferential statistics, it’s important to mention the descriptive statistics, sample sizes of each variable, confidence intervals. To report the actual p-Value, it’s mandatory to provide the value of the test statistics, the direction of the test statistics, the degree of freedom and the level of significance.
- To represent exact values, it’s crucial to use tables and figures. Also, try to keep the characters in small fonts. While mentioning confidence intervals, it’s much better to include graphic representations as much as possible.
- Give a few details about your tables and figures, making it easier for readers to know what the tables & figures represent.
While using pre-existing statistical data collected and produced available by anyone other than an analyst such as a government agency, an analyst must report on the methods that were used to gather the data. Also, the analyst must also report any missing data that exists and, if there is any, provide a clear explanation of why the missing data does not impair the efficacy of your final analysis.
How Qualitative Data Analysis Differs from Quantitative Data Analysis
People who are new to research methods, it’s crucial for them to know the main differences between qualitative and quantitative data analysis. Without knowing the proper difference, one may tend to go off track and spoil the proposed research methodology. In simple terms, qualitative data focused more subjective interpretation and analysed non-numerical data while in quantitative data analysis, analysts mostly concentrate on numerical or statistical data to get concrete proof of their findings.
How to Analyse Qualitative Data
To analyse qualitative data, analysts may come across many methods along with different types of steps and rules. With such a vast range of way to interpret qualitative data, analysts often get confused about the right methodology to use. The good things about qualitative data are one can use any method and still can achieve the desired result. In qualitative data, researchers only have to follow specific ground rules rather than following any rigorous process. The findings of Qualitative data often relies on the interpretation of the researcher and the context of the study.
How to Effectively Carry Out a Qualitative Data Analysis
Conducting a Qualitative Data Analysis is a challenge by itself due to its unstructured nature. However, utilising the proper analysing skills of an analyst along with the right methodology can provide reliable data analysis. Qualitative Data Analysis may sound worrisome and boring, but it started on the right foot, one can quite enjoy it.
What is Qualitative Data?
Also known as, descriptive data, these types of data comprises non-numerical values that mostly consists of concepts and opinions. Interviews with customers, audio/video recordings, notes etc. are some of the few examples of qualitative data.
What is a Qualitative Data Analysis?
In Qualitative Data Analysis, the analyst examines the data and concludes an explanation for a particular phenomenon. By revealing the different patterns and themes from the data, an analyst can provide a good understanding of a research objective.
Purpose of Qualitative Data Analysis
The primary goal of Qualitative Data Analysis is to organise, interprets and identify the patterns from a given data. With analysing all the field data, the analyst reaches to informed and valid conclusions.
Two Main Approaches to Qualitative Analysis
The deductive approach in qualitative data analysis comprises procedures where the researcher prepare a set of structured questions and then use them in grouping & analysing the data. This type of approaches is ideal when the researchers have an idea of the Responses from a population sample.
Contrary to Deductive approach, the Inductive approach involves more thorough and time-consuming procedure on behalf of the researcher. When a researcher is unknown to research phenomenon, this kind of approach proves as a vital option.
Step by Step Process to ensure a robust Qualitative Data Analysis:
Data collected from the field are often unstructured and sometimes sounds nonsensical. As a researcher, one has to transcribe the raw data and extract sense out of them. Transcribing Data means to convert all data into the textual form. Analysts can choose from an extensive range of transcription tools known as Computer-assisted qualitative data analysis software (CAQDAS). Various Tools such as NVivo, ATLAS and EvaSys are some of the best transcribe data known for providing faster and accurate transcription results.
Upon completion of the transcribing process, analysts are left with a vast number of information, which is enough for any new researched feel confused and frustrated. Rather than being lazy and working with random data, it’s important for analysts to organise their data before starting their work. Analysts can go back to the objective/questions of the research and realign their collected data as per the questions/objectives. Using tables while preparing your reports makes it more presentable and visually vibrant. Distribute your research objectives into a table and assign appropriate data accordingly. As specified earlier, researchers can also use a research software to organise their data more productively.
Coding data in Qualitative analysis refers to segregating data into concepts, patterns and properties. To increase the efficiency of the investigation, one must focus on compressing the gathered data into more lucid ideas. Coding proved to quite vital in the analysis process as it provides more meaning to the collected data. Deriving the codes for analysis requires sharp observation skills of an analyst. The analyst can refer to various theories from previous findings and objectives of relevant researches.
Some popular coding terms include Descriptive coding, in-vivo coding, and pattern coding. Descriptive coding refers to summarising the central theme of a data. In In-vivo coding, analysts can use the language of the respondents to code them accordingly. In pattern coding, analysts look for patterns in the data and use them as their coding base.
With proper coding, analysts can move on to build the themes or patterns in their data analysis for a more in-depth analysis.
4. Validating Data
Data Validation is not just one phase in data but an ongoing process. An analyst needs to analyse the data in each step of data analysis. As data validation is considered as a prominent pillar of every successful research, it’s crucial for the analyst to ensure the vitality and reliability of the data. The data validations have two sides, and each side carries equal importance as the other does. On the first side, one has to check the accuracy of the data based on the design/methods he/she working on. The second part consists of checking the reliability of the data and ensuring that the data are adequate to produce consistent and dependable results.
5. Concluding the Data Analysis
Concluding the analysis refers to stating the findings in details ensuring that the research outcomes matches perfectly as per the research objectives. While presenting the final report, analysts must provide that all the necessary processes and methods are mentioned in it. Apart from stating the pros and cons, the report must include the limitations of the study as well. It should also include the implication of the research as well as possible areas where the report can be used in the future.
Pointers for Effective Qualitative Data Analysis
- Analysts must be ready to ask questions and try level best to find the answers.
- Analysts must take notes on a regular basis.
- All data must be organised entirely before working on it.
- One must compare the current findings with other relevant students in the same categories to find relationships between the studies
- Researchers must have a welcoming attitude towards second opinions.
- Before starting work, researches must gather all the relevant studies and references based on their current analysis.
- Analysts should ensure they have enough resources before handling the project.
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