Quantitative Data Interpretation & Data Analysis Method

• Research data


  is any information that has been collected, observed, generated or created to validate a research study.
• Data analysis


  a process that involves examining, and molding collected data for interpretation to discover relevant information, draw or propose conclusions and support decision-making to solve a research problem.
• Research


  a systematic process of inquiry that involves collection of data; documentation of substantial information analysis and Interpretation of that data information, in accordance with the appropriate methodologies set by specific professional fields of disciplines.
• Quantitative research


  defined as a systematic investigation of phenomena or inquiry by gathering quantifiable data and doing the statistical, mathematical, or computational strategies.
• Interpretation of data


  refers to the implementation of certain procedures through which data results from surveys is reviewed, analyze for the purpose of achieving at valid and evident based conclusion.
• data preparation


  It is the first stage of analyzing data, where the main goal is to transform raw data into something meaningful, significant and user friendly.
• Data Validation


  to check whether the gathered data was performed according to the set standards.
• the four-step process of data validation includes:

  • fraud

  • screening

  • procedure

  • completeness
• Fraud


  to ensure whether each respondents was actually interviewed.
• Screening


  to check that respondents were chosen according to the standard research criteria.
• Procedure


  to make sure whether the data collection process was followed.
• Completeness


  to make sure that the interviewer asked the respondent all the necessary questions, rather than just choosing a few ones.
• Data Editing - Usually, many data sets include errors. For example, respondents may fill fields incompletely or skip them. To ensure that these errors will not occur, the researcher should conduct the initial data checking and edit the raw research data to identify and clean out any points that may become the barrier to come up with an accurate results.
• Data Coding - This is the number one significant process in data preparation. It refers to grouping and assigning values/codes to responses from the conducted survey.
• Cross-tabulation - This is the most commonly used quantitative data analysis methods. It is the most preferred method since it uses a basic tabular form to draw inferences between different data-sets of dependent and independent variable. It contains data that have some connection with each other.
• Relate measurement scales with variables - Associate scales of measurement such as Nominal, Ordinal, Interval and Ratio with the variables dependent and independent variables. This step is of utmost important to arrange the data in proper sequence/order. Data can be entered/encoded into an excel sheet to organize it in a specific data format.
• Connect descriptive statistics with data - to contain available data. It can be hard to establish a pattern in the raw data.
• Some commonly used descriptive statistics are:

  Mean -An average of values for a specific variable.

  Median - A midpoint of the value scale for a variable.

  Mode -For a variable, the most common value.

  Frequency - Number of times a particular value is observed in the scale.

  Minimum and Maximum Values - Lowest & highest values for the scale.

  Percentages - Format to express scores and set of values for variables.

  Range - the highest and lowest value in a set of values.
• Decide a measurement scale - this is important to conclude a descriptive statistic for the specific variable.
• Select appropriate tables to represent data and analyze collected data - After deciding on a suitable measurement scale, researchers can use a tabular format to represent data. This data can be analyzed using various techniques such as Cross-tabulation.
• The varying scales include: Nominal Scale - non-numeric categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive. Ordinal Scale - exclusive categories that are exclusive and exhaustive but with a logical order. Quality ratings and agreement ratings are examples of ordinal scales. Interval - a measurement scale where data is grouped into categories with orderly and equal distances between the categories. There is always an arbitrary zero point. Ratio - contains features of all three.