L4

• Methodology


  The systematic procedure and theoretical analysis of the method applied in a research study
• Methodology


  • It is a science of study on how research is to be carried out
  • It aims to give a work plan to research
  • It is the actual procedures, numerical schemes, and statistical approaches used by the researcher that help him/her collect data and find a solution to a problem
• Research methodology is an important part of research because it proves the accuracy and validity of a research study
• Guidelines when writing methodology

  • Always be direct and precise
  • Write it in past tense form and third-person point of view
  • Include enough information so that future researchers could easily replicate your study to judge the validity of your results and conclusions
  • Take a rough draft of your work with your research teacher for additional assistance
  • Always proofread your paper
• Guidelines in writing research methodology

  1. Explain your methodological approach

  2. Describe your methods of data collection

  3. Describe your method of analysis

  4. Evaluate and justify your methodological choices
• Parts of research methodology

  • Research design
  • Population and sample
  • Instrument
  • Validation process
  • Data gathering procedure
  • Treatment of data
• Descriptive data analysis


  Provides simple summaries about the sample and the measures, used to simply describe what is or what the data shows

  
• Statistical measures of descriptive analysis

  • Frequency
  • Measures of central tendency (mean, median, mode)
  • Measures of dispersion (range, standard deviation, variance)

  
• Inferential data analysis


  Allows us to make inferences and generalizations about the population using the selected samples
• Types of inferential data analysis

  • Test of significant difference (t-test, z-test, ANOVA)

  
• Types of t-test


  • One sample t-test
  • Independent two sample t-test
  • Paired sample t-test or dependent sample test
• 1. test


  A type of inferential statistics used to determine if there is a significant difference between the means of two comparing groups, when the sample size is greater than 30

  
• ANOVA


  Analysis of variance, a type of inferential statistics

  
• 1. test


  A type of inferential statistics used to determine if there is a significant difference between the means of two comparing groups. The difference between z-test to t-test is the number of sample participants. If you are finding a significant difference between the means of two groups but your samples in each comparing group are more than 30, then the z-test is the appropriate test to use.
• 1. test


  • Your sample size must be greater than 30. Otherwise, use a t-test
  • Your data should be normally distributed. However, for large sample sizes (over 30) this doesn't always matter
  • Your data should be randomly selected from a population, where each item has an equal chance of being selected
  • The sample sizes should be equal if possible
• Analysis of Variance (ANOVA)


  Used when significance of difference of means of three or more groups are to be determined at one time. ANOVA relies on the F-ration to test the hypothesis that the two variances are equal; that is, the subgroups are from the same population. If no true variance exists between the groups, the ANOVA's F-ratio should equal close to 1.
• Types of ANOVA


  • One-Way ANOVA
  • Two-Way ANOVA
• One-Way ANOVA


  Used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups. In one-way ANOVA, you have one independent variable affecting a dependent variable.
• Two-Way ANOVA


  An extension of the one-way ANOVA. With a two-way ANOVA, there are two independent variables affecting a dependent variable.
• Spearman Rank-Order Correlation or Spearman Rho


  Used when data available are expressed in ranks (ordinal variables). Use Spearman rho when you have two ranked variables, and you want to test whether the two variables co-vary; whether, as one variable increases, the other variable tends to increase or decrease.
• Product – Moment Coefficient of Correlation or Pearson r


  Used when data are expressed in terms of ratio and interval variables. It is used to evaluate the linear relationship between two continuous variables.
• Chi-Square Test


  Used when data expressed in terms of frequencies or percentages (nominal variables). A chi-square test measures how expectations are related to actual observed data.

  
• Hypothesis


  An educated guess about something and a tentative solution to a research problem. It should be testable, either by experiment or observation.
• Types of Hypothesis


  • Null hypothesis (Ho)
  • Alternative hypothesis (Ha)
• Null hypothesis (Ho)


  The hypothesis that is always tested by the researcher. It always indicates that there is no significant relationship or difference between the variables.
• Alternative hypothesis (Ha)


  Indicates that there is a true relationship or difference between the variables.
• Results will show that: 1) There is a meaningful relationship or difference between two groups, thus reject the null hypothesis. 2) The difference or relationship between the two groups is not large enough to conclude that the groups are different or correlated, thus you fail to reject the null hypothesis.
• Hypothesis Testing


  A process in statistics by testing an assumption regarding a population parameter. It is the use of statistics to determine the probability that a given hypothesis is true.
• Statistical Significance


  The relationship of variables caused by something. Significance means probably true (not due to chance). Level of significance means that there is a chance that the finding is true.
• Type of Errors


  • Type I error
  • Type II error
• Type I error


  Committed when the researcher rejected the null hypothesis when in fact it is true.
• Type II error


  Committed when the data produce a result that fails to reject the null hypothesis when in fact the null hypothesis is false and needs to be rejected.
• Steps in hypothesis testing

  1. State the hypothesis

  2. Choose the statistical test to be used

  3. State the level of significance for the statistical test

  4. Do the computation using the chosen statistical test

  5. Decide whether to reject or accept the null hypothesis
• Tabular Form


  A table facilitates representation of even large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns.
• Components of Data Tables

  • Table Number
  • Title
  • Headnotes
  • Stubs
  • Caption
  • Body or field
  • Footnotes
  • Source
• Construction of Data Tables

  • The title should be in accordance with the objective of study
  • Comparison: If there might a need to compare any two rows or columns then these might be kept close to each other
  • Alternative location of stubs: If the rows in a data table are lengthy, then the stubs can be placed on the right-hand side of the table
  • Headings: Headings should be written in a singular form
  • Footnote: A footnote should be given only if needed
  • Size of columns: Size of columns must be uniform and symmetrical
  • Use of abbreviations: Headings and sub-headings should be free of abbreviations
  • Units: There should be a clear specification of units above the columns
• Construction of Data Tables

  1. The title should be in accordance with the objective of study

  2. Comparison: If there might a need to compare any two rows or columns then these might be kept close to each other

  3. Alternative location of stubs: If the rows in a data table are lengthy, then the stubs can be placed on the right-hand side of the table

  4. Headings should be written in a singular form

  5. Footnote: A footnote should be given only if needed

  6. Size of columns must be uniform and symmetrical

  7. Headings and sub-headings should be free of abbreviations

  8. There should be a clear specification of units above the columns
• Classification of Data and Tabular Presentation

  • Quantitative Classification
  • Temporal Classification
  • Spatial Classification

  
• Interpreting a Simple Table

  1. State the main title on top of the drawn table

  2. State the total figure involved, if possible

  3. State the breakdown of the total figure from top row to bottom row

  4. Build up the comparison in terms of highest to lowest or of other comparable data

  5. Summarize the general impact of the whole table
• Qualitative Data


  Describes a subject, and cannot be expressed as a number. These data are grouped into non-overlapping categories