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Quantitative Analysis

Quantitative Analysis. Dr Zia- Ul - Ain Sabiha. Learning objectives. At the end of this session the Student will be able to Differentiate between qualitative and quantitative research Have a guide about application of quantitative test. Research: How Do I Begin???.

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Quantitative Analysis

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  1. Quantitative Analysis Dr Zia-Ul-AinSabiha

  2. Learning objectives • At the end of this session the Student will be able to • Differentiate between qualitative and quantitative research • Have a guide about application of quantitative test

  3. Research:How Do I Begin???

  4. Take It “Bird by Bird”Anne Lamott

  5. Identify topic Literature review Variables of interest Research hypothesis Design study Power analysis Write proposal Design data tools Committees Collect data Set up spreadsheet Enter data Statistical analysis Graphs Slides / poster Write paper / manuscript Let’s Take it Step by Step...

  6. Difference between qualitative and quantitative research

  7. Definition Statistics is the science of collecting, organizing, summarising, analysing, and making inference from data Descriptive stat. Includes collecting, organizing, summarising, analysing, and presenting data Inferential stat. Includes Making inferences, hypothesis testing Determining relationship, and making prediction Dr.H.Qotba

  8. Variables • Quantitative • Discrete • Continuous • Qualitative • Ordinal • Categorical Dr.H.Qotba

  9. Data Analysis • The process of inspecting ,cleaning, transforming and modeling data to answer questions and generate new information.

  10. Raw data Quantitative Analysis Information

  11. Quantitative • Convert information in to numbers • Qualitative Subjective Opinion Rating

  12. Parametric Vs. non parametric tests • Parametric: decision making method where the distribution of the sampling statistic is known • Non-Parametric: decision making method which does not require knowledge of the distribution of the sampling statistic Dr.H.Qotba

  13. t-Test • Compare the means of a continuous variable into samples in order to determine whether or not the difference between the 2 expected means exceed the difference that would be expected by chance What is probability the mean will differ? Dr.H.Qotba

  14. Requirements • The observations are independent • Drawn from normally distributed population • Sample size < 30 if it’s >30 use normal curve z test (binomial test) Dr.H.Qotba

  15. Types of t-Test • One sample t test: test if a sample mean for a variable differs significantly from the given population with a known mean • Unpaired or independent t test: test if the population means estimated by independent 2 samples differ significantly (group of male and group of female) • Paired t test: test if the population means estimated by dependent samples differ significantly (mean of pre and post treatment for same set of patients Dr.H.Qotba

  16. chi² test • Used to test strength of association between qualitative variables • Used for categorical data Dr.H.Qotba

  17. Requirements • Data should be in form of frequency • Total number of observed must exceed 20 • Expected frequency in one category or in any cell must be >5 (When 1 of the cells have <5 in observed yats correction) or if (When 1 of the cells have <5 in expected fischer exact) • The group compared must be approximately the same Dr.H.Qotba

  18. Correlation and Regression • Methods to study magnitude of the association and the functional relationship between two or more variables Dr.H.Qotba

  19. Correlation • Denote strength of relationship between variables Dr.H.Qotba

  20. Regression • Method that’s indicate a mathematical relationship between a dependant and one or more independent variables • Simple linear regression and multiple regression are appropriate for continuous variables like(BP, Weight) • Logistic regression applicable for binary response like alive/dead Dr.H.Qotba

  21. Measures • If parametric • Pearson correlation coeff. • Continuous variables • Linear relationship • If nonparametric • Spearman rank • Both variables are continuous • Kendall’s tau • Two ordinal or one ordinal one continuous Dr.H.Qotba

  22. ANOVA • is used to uncover the main and interaction effects of categorical independent variables (called "factors") on an interval dependent variable Dr.H.Qotba

  23. Types of ANOVA • One-way ANOVA tests differences in a single interval dependent variable among two, three, or more groups formed by the categories of a single categorical independent variable. Dr.H.Qotba

  24. Two-way ANOVA analyzes one interval dependent in terms of the categories (groups) formed by two independents, one of which may be conceived as a control variable • Multivariate or n-way ANOVA. To generalize, n-way ANOVA deals with n independents. It should be noted that as the number of independents increases, the number of potential interactions proliferates Dr.H.Qotba

  25. Guide for you

  26. How to select appropriate statistical test • Type of variables • Quantitative (blood pres.) • Qualitative (gender) • Type of research question • Association • Comparison • Risk factor • Data structure • Independent • Paired • matched Dr.H.Qotba

  27. Types of variable Dependent independent Test categorical categorical chi-square categorical Quantitative Log. regression Quantitative categorical 2 out come T test 3+out come ANOVA Quantitative Quantitative Spearman Correlation linear Regression Body of research question Association of 2 variable(dep, indep) Dr.H.Qotba

  28. Comparing (difference) variables Variable Number of independent variable 2 groups paired data >2groups Quantitative Ordinal Categorical T test Paired T test ANOVA Mann-Whitney Kruskal wallis Wilcoxon chi-square* McNemar chi-square • * When 1 of the cells have <5 in expected fischer exact • When 1 of the cells have <5 in observed yats correction Dr.H.Qotba

  29. Looking for Risk Factor Dr.H.Qotba

  30. Limitations of research study/data/statistical tests • Always look for these – the researchers should reflect on their study and discuss anything that did not make it perfect, for example: •  size of sample •  tests used •  initial question • It is easy to tie yourself up into knots when either doing statistics as part of your research, or when reading research papers, so remember two things: • 1. Keep things simple • 2. Statistics by themselves are meaningless, it is the analysis and discussion of statistics which makes them meaningful and brings them to life.

  31. Confused by Statistics ?

  32. Don’t Lie With Statistics !

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