III

• Quantitative data refers to numerical data that could usefully be quantified to help you answer your research question/s and to meet your objectives
• Median


  The middle score when all scores are organized in numerical order
• Mean


  Calculated by summing all the values and dividing by the number of values
• Mode

  The most common value
• Measures of central tendency

  Methods used to compute average or central value of collected data
• Standard deviation


  A way of measuring extent of spread of quantifiable data
• Data collection


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


  Concerned with testing hypotheses derived from theory and/or being able to estimate the size of a phenomenon of interest
• Quantitative data collection method

  • Relies on random sampling and structured data collection instruments that fit diverse experiences into predetermined response categories
  • Produces results that is easy to summarize, compare, and generalize
• Steps in data collection

  1. Determine the objectives of the study

  2. Define the population of interest

  3. Choose the variables to measure

  4. Decide on an appropriate design for producing data

  5. Collect the data

  6. Determine the appropriate descriptive and/or data analysis techniques
• Data editing


  The raw data collected will be transferred to the data editors to check for the completeness, accuracy, and preciseness of data
• Importance of data editing

  • The quality of your analysis depends on the quality of the raw data you used
  • Quality data collection requires training data collectors and monitoring completeness and accuracy of raw data
• Steps in data editing

  1. Manual or visual editing before forms are encoded

  2. Coding open-ended questions

  3. Coding close-ended questions with unclear or ambiguous responses, multiple responses, written comments
• Coding and naming conventions

  Should be standardized for files, variables, programs, and other entities in a data management system
• Data entry


  Entering the edited data into the computer system
• Codebook


  Codes for responses of close-ended questions
• Consistent rules must be used for coding variables
• Variables in sample codebook

  • Gender
  • TrackSHS
  • OAHE
• Data entry must be performed by well-trained and responsible individuals
• Consistency in data entry is best achieved by one rather than multiple individuals
• As the number of persons involved in data entry increases, the chance of error also increases
• Systematic bias may be an issue with only one data entry individual
• Data cleaning


  The process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted
• Data cleaning is not simply about erasing information to make space for new data, but rather finding a way to maximize a data set's accuracy without necessarily deleting information
• Data cleaning includes more actions than removing data, such as fixing spelling and syntax errors, standardizing data sets, and correcting mistakes such as empty fields, missing codes, and identifying duplicate data points
• After validating and cleaning the data, you can now start summarizing them through descriptive statistics or test your hypothesis using inferential statistics
• Data storage


  Storing data in the cloud is preferable since teammates can view and edit the file wherever they are as long as they are online
• Examples of cloud storage


  Google Drive, OneDrive
• Data maintenance


  Creating a back-up copy of the files on regular basis, storing printed and digital copies in a secured location, and proper documentation of the information, procedures, and data analysis conducted
• Levels of measurement


  The way a set of data is measured
• Levels of measurement


  • Nominal scale
  • Ordinal scale
  • Interval scale
  • Ratio scale
• Nominal scale data


  Qualitative (categorical) data that is not ordered
• Ordinal scale data


  Data that can be ordered but the differences between data cannot be measured
• Interval scale data


  Data that has a definite ordering and the differences between data have meaning
• Ratio scale data


  Data that has a 0 point and ratios can be calculated
• Frequency


  The number of times a value of the data occurs
• Relative frequency


  The ratio (fraction or proportion) of the number of times a value of the data occurs in the set of all outcomes to the total number of outcomes
• Cumulative relative frequency


  The accumulation of the previous relative frequencies
• Pie charts should have a maximum of six slices, the first slice must start at 12 o'clock, and explosion should only be used to focus on a pie slice

  
• Bar charts can be used to compare the counts, the means, or other summary statistics using bars to represent groups or categories