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Steps to Quantitative Data Analysis

                 The four steps involved in quantitative data analysis are:
                 1.   Relate measurement scales with variables: Understand the type of data you have and match it with the
                    appropriate measurement scale. For example, if you are looking at student grades, use ordinal (A, B, C) or
                    ratio (numeric scores) scales.

                 2.  Connect descriptive statistics with data: Use statistical measures to summarise and describe your data.
                     For example, if you have test scores for a class of students, calculate the mean score to know the average
                    performance, the median to understand the central tendency, and the standard deviation to see how varied
                    scores are.
                 3.   Decide a measurement scale: Choose the appropriate scale to measure your data based on the type of variable
                    and the level of detail required. For example, if you are measuring weight, use a ratio scale because weight can
                    be zero and can be measured precisely.

                 4.   Represent data in an appropriate format: Display your data in a way that makes it easy to understand and
                    interpret. For example, if you want to show the distribution of test scores in a class, you might use a histogram.
                    If you want to show the relationship between study hours and test scores, a scatter plot would be appropriate.

                 Qualitative Data Interpretation

                   It is the process of analysing and understanding non-numeric data. This type of data is unstructured and often
                 comes from interviews, surveys, observations, or textual content. Qualitative data tells us about the emotions
                 and feelings of people. Qualitative data interpretation is focused on insights and motivations of people.



                 Data Collection Methods in Qualitative Data Interpretation
                 Data collection methods in qualitative data interpretation involve techniques such as interviews and observations
                 to  gather  rich,  descriptive  data  for  detailed  analysis,  fostering  a  deeper  understanding  of  complex  human

                 experiences and behaviours. Some methods are as follows:
                    • Record keeping: This method utilises documents that are reliable and well curated and other similar sources of
                   information as the data source that are verified and maintained. It is similar to going to a library.

                    • Observation: In this method, data is collected by observing the participants, their behaviour and emotions,

                   carefully.
                    • Case studies: In this method, data is collected from case studies.

                    • Focus groups: In this method, data is collected after a group discussion on topics of relevance.

                    •  Longitudinal studies: In this data collection method, data is collected on the same data source repeatedly over
                   an extended period of time.

                    • One-to-one interviews: In this method, data is collected using a one-to-one interview.


                 Open Ended Surveys and Questionnaires
                 Open ended surveys and questionnaires allow organisations to collect views and opinions from respondents
                 without meeting in person.




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