III

• Data Interpretation

  refers to the implementation of certain procedures

  through which data results from surveys is reviewed

  and analyze for the purpose of achieving at valid and

  evident based conclusion.
• Step 1: Data Validation

  goal is to check whether the gathered data was performed according to the set standards
• Step 1: Data Validation

  a four-step process, which includes fraud, screening, procedure and 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.
• Step 2: Data Cleaning

  To ensure that these errors will not occur, the researcher

  should conduct the initial data checking and review the raw

  research data to identify and clean out any points that may

  become the barrier to come up with an accurate results.
• Step 3: Data Coding

  Data coding refers to grouping and assigning values/codes to

  responses from the conducted survey.
• Data Analysis

  a process of uncovering insights from data sets by using

  statistical techniques, research theories and methods, and

  visual representations
• Data Analysis

  involves gathering, processing, exploring, and interpreting

  data to find patterns and other insights
• Cross-tabulation - is the most preferred method in analyzing

  data since it uses a basic tabular form to draw inferences

  between different data-sets of dependent and independent

  variable.
• Steps in Data Analysis

  1. Relate measurement scales with variables:

  • Associate scales of measurement such as Nominal, Ordinal, Interval and Ratio with the variables – dependent and independent variables.
• Steps in Data Analysis

  2. Connect descriptive statistics with data:

  • to contain available data to establish a pattern in the raw data.
  • Some commonly used descriptive statistics are: Mean, Median, Mode, Frequency, Minimum and Maximum Values, Percentages and Range
• Steps in Data Analysis

  3. Decide a measurement scale:

  • It is important to decide the measurement scale to conclude a descriptive statistic for the specific variable.
• Steps in Data Analysis

  4. 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.
• Qualitative Data Analysis

  Finding the explanation of “how” and why of a certain

  event or phenomenon.
• Content analysis

  • used to analyze documented information in the form of texts, media, or even physical items.
• Discourse analysis

  • used to analyze interactions with people but focuses o analyzing the social context in which the communication between the researcher and the respondent occurred.
• Grounded theory

  • refers to using qualitative data to explain why a certain phenomenon happened by studying a variety of similar cases in different settings and using the data to derive causal explanations.
• Narrative analysis

  • used to analyze content from various sources, such as interviews of respondents, observations from the field, or surveys by focusing on the stories and experiences shared by people to answer the research questions.
• Thematic Analysis

  • researcher looks across all the data to identify some recurring issues where main themes are derived
  • The main stages involves: (1) Read and annotate transcript, (2) Identify themes, (3) Develop a coding scheme, (4) Coding the data and (5) Identifying themes
• Nvivo - one of the most used qualitative analysis software
• Developing a Coding Scheme

  • Initial determination of the preset codes based from literature review or pre analysis during interviews
• Coding the Data

  • Get an idea of the entire set.
  • Select one interesting document.
  • Start the document coding process.
  • List all the code words.
  • Review the list against the data (matrix analysis).
  • Categorize the codes for emerging themes or descriptions of the subject or setting.
• Qualitative Data Interpretation

  • Consider the data from various perspectives.
  • Think beyond the data.
  • Make visible personal assumptions and beliefs or models that influence the interpretation, representing personal views of the world.
  • Watch out for some data may come in surprise, contradictory, outlying, or puzzling, because they usually lead to useful insights.
• Conclusion - focuses on implying the totality of result
• Implications - potential consequences, applications, or outcomes of the findings and conclusion of a research
• Types of Implication

  • theoretical
  • practical
  • methodological
  • ethical
  • policy
  • societal
• Recommendation - opportunity to give suggestions for the betterment and improvement of the study
• Considerations in writing recommendations

  • must be brief
  • should be clear - state specific suggestions
  • must be precise - avoid vague recommendations
• Sharing research results

  • essential component of the research process
  • research outcome reach a wider audience
• Methods for Sharing

  • publishing articles in academic journals
  • presenting at conferences
  • posting preprints on online repositories
  • using social media platforms
• Open Access Publishing

  • making research freely accessible to all