Data Presentation of Quantitative Research

• Quantitative Research: the data that will be gathered are numerical in nature the following are the ways on how to present your gathered data.
• Researchers and scientists often use tables and graphs to report findings from their research.
• Bar graphs should be used for categoric, ordered, and discrete variables. If the number of units in a discrete variable is large it may be displayed as a continuous variable.
• Line graphs should be used for continuous variables.
• Pie graphs (sometimes called pie or circle charts) are used to show the parts that make up a whole. They can be useful for comparing the size of relative parts.
• Descriptive Statistics


  This describes a certain aspect of a data set by making you calculate the Mean, Medium, Mode (measures of central tendency) and Standard Deviation. It tells about the placement or position of one data item in relation to the other data, the extent of the distribution or spreading out of data, and whether they are correlations or regressions between or among variables. This kind of statistics does not tell anything about the population.
• Inferential Statistics


  (Focused on the characteristics of the sample that are also true for the population from where you have drawn the sample.) Your analysis begins with the sample, then, based on your findings about the sample, you make inferences or assumptions about the population. [predictions]
• Correlation or Covariation (correlated variation)


  describes the relationship between two variables and also tests the strength or significance of their linear relation. This is a relationship that makes both variables getting the same highscore or one getting a higher score and the other one, a lower score.
• Covariance is the statistical term to measure the extent of the change in the relationship of two random variables. Random variables are data with varied values like those ones in the interval level or scale (strongly disagree, disagree, neutral, agree, strongly agree) whose values depend on the arbitrariness or subjectivity of the respondent.
• Cross Tabulation


  is also called “crosstab or students-contingency table” that follows the format of a matrix (plural: matrices) that is made up of lines of numbers, symbols, and other expressions. Similar to one type of graph frequency and percentage distribution of data, a crosstab explains the reason behind the relationship of two variables and the effect of one variable on the other variable.
• If the Table compares data on only two variables, such table is called Bivariate Table.
• Statistical Methodologies


  • Descriptive Statistics (calculating Mean, Median, Mode, and Standard Deviation)
  • Inferential Statistics
  • Correlation or Covariation (Covariance)
  • Cross Tabulation (Bivariate)
• Measure of Correlation


  • Correlation Coefficient (strength and direction of the linear relationship)
  • Regression
• Correlation Coefficient


  This is a measure of the strength and direction of the linear relationship between variables and likewise gives the extent of dependence between two variables; meaning, the effect of one variable on the other variable.
• Regression


  Similar to correlation, regression determines the existence of variable relationships, but does more than this by determining the following:

  (1) which between the independent and dependent variable can signal the presence of another variable;

  (2) how strong the relationship between the two variables are; and

  (3) when an independent variable is statistically significant as a soothsayer or predictor.
• Spearman’s Rho


  test to measure the dependence of the dependent variable on the independent variable
• Pearson product-moment correlation


  measures the strength and direction of the linear relationship of two variables and of the association between interval and ordinal variables.
• Chi-square


  is the statistical test for bivariate analysis of nominal (what type of variable) variables, specifically, to test the null hypothesis. This cannot in any way show the extent of the association between two variables.
• T-test


  evaluates the probability that the mean of the sample reflects the mean of the population from where the sample was drawn. It also tests the difference between two means: the sample mean and the population mean.
• ANOVA


  analysis of variance also uses t-test to determine the variance or the difference between the predicted number of the sample and the actual measurement.
• Types of ANOVA:

  • One-way analysis of variance – study of the effects of the independent variable
  • ANCOVA (Analysis of Covariation) – study of two or more dependent variables that are correlated with one another
  • MANCOVA (Multiple Analysis of Covariation) – multiple analyses of one or more independent variables and one dependent variable to see if the independent variables affect one another
• Steps in Presenting Data in Qualitative Research

  1. Upon the interview the proper recording of data thru writing and recording gadget is very important

  2. Transcribing is the process of transporting recorded data to a written data

  3. You have to put into writing whatever data you have gathered from the interview

  4. Using the written data, you will sort all the similar answer and put them in one paragraph

  5. If you have 5 questions you should have 5 paragraphs explaining the answer of your participants

  6. Using the responses from the participants you will now present the data using paragraphs
• One of the significant features of qualitative research is that it uses words to present data basically in qualitative research the researcher uses interviews to gather the data.
• One of the vital parts of Quantitative research is the statistical method that you will use for your data.
• Soothsayer: predictor; statistically significant independent variable
• Measures of central tendency help you find the middle, or the average, of a dataset.
• Mode


  the most frequent value
• Median


  the middle number in an ordered dataset
• Mean


  the sum of all values divided by the total number of values
• 2 Types of Distribution

  • Normal Distribution
  • Skewed Distribution
• In a normal distribution, data is symmetrically distributed with no skew. Most values cluster around a central region, with values tapering off as they go further away from the center. The mean, mode and median are exactly the same in a normal distribution.
• Skewed distributions: more values fall on one side of the center than the other, and the mean, median and mode all differ from each other.
• In a positively skewed distribution, there’s a cluster of lower scores and a spread out tail on the right.
• In a negatively skewed distribution, there’s a cluster of higher scores and a spread out tail on the left.
• Distribution is skewed to the right, and the central tendency of your

  dataset is on the lower end of possible scores. (Clustered to the left; Skewed to the right)

  A) Positive

  
• Distribution is skewed to the left, and the central tendency of your dataset is on the higher end of possible scores. (Clustered to the right; Skewed to the left)

  A) Negative

  
• In this median formula, what is n?

  Number of values in a dataset. (odd number dataset)

  
• In this median formula, what is n?


  Number of values in a dataset. (even number dataset)

  A) mean

  
• Determine the missing terms in this picture.

  A) mode

  B) nominal

  C) ordinal

  D) interval

  E) ratio

  F) median

  G) mean

  
• A statistics is a measure that describes the sample (e.g., sample mean).