3Is

•  process for obtaining  quantifiable information

  Quantitative Research
• describes the current status of an identified variable

  Descriptive Research
• uses the scientific method to establish the cause-effect relationship

  Experimental Research
• summarizes the major variables in your research 

  Conceptual Framework
• introduces the overview of the topic

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

  Research data
• 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.

  Data analysis
• systematic process of inquiry that involves collection of data

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

  Quantitative research
• recorded factual material commonly accepted in the scientific community as necessary to validate research findings

  Data
• process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.

  Data Collection
• collected from the first-hand experience and is not used in the past and are specific to the research’s motive and highly accurate.

  Primary data
• data that have been used in the past. The researcher can obtain data from the sources, both internal and external, to the organization.

  Secondary data
• to check whether the gathered data was performed according to the set standards.

  Data Validation
• to ensure whether each respondents was actually interviewed.

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

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

  Procedure
• to make sure that the interviewer asked the respondent all the necessary questions, rather than just choosing a few ones.

  Completeness
• 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 accurate results.

  Data Editing
• grouping and assigning values/codes to responses from the conducted survey.

  Data Coding
• 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.

  Cross-tabulation
• 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.

  Relate measurement scales with variables
• used for naming variables with no particular order

  Nominal Scale
• used for variables in ranked order, but the difference between is not determined

  Ordinal Scale
• used for numerical variables with known equal intervals of the same distance

  Interval scale
• used for variables on a scale that have measurable intervals

  Ratio Scale
• to contain available data. It can be hard to establish a pattern in the raw data.

  Connect descriptive statistics with data
• an average of values for a specific variable

  Mean
• a midpoint of the value scale for a variable


  Median
• for a variable, the most common value

  Mode
• Number of times a particular value is observed in the scale. It can simply be defined as the count of certain event which has occurred

  Frequency
• Lowest & highest values for the scale

  Minimum and Maximum Values
• Format to express scores and set of values for variables.

  Percentages
• the highest and lowest value in a set of values. It is simply the difference between the largest and smallest value in a data set.

  Range