e-resources on building-blocks of statistical analysis for research project
Basic concepts for revision
a. Descriptive Statistics, Part 1
b. Descriptive Statistics, Part 2
c. Descriptive vs Inferential Statistics
I. Building blocks at the initial stage:
* constructs.
* variables and operationalization in quantitative methods.
* conceptualization and operationalization.
* theories and operational definitions.
* true, quasi, pre, and non-experiment.
* density curves and their properties.
* on variables.
* Some different types of relationships
* on mediator and moderator variables. (also watch mediation, moderation and the third variable problem). [again on Regression: Mediator vs. Moderator]
* steps to formulate a strong hypothesis.
* on standard normal distribution.
* the normal distribution rule.
* normal distribution explained - part 1.
* normal distribution explained - part 2.
* understanding the central limit theorem.
* On statistical significance.
* On confidence and significance level
* P-values and critical values.
* on one tail and two tail tests.
* Representative vs Biased Samples
* On sampling in research methods study.
II. Specific statistical techniques
Technique 1: chi-squared test
* simple explanation of chi-squared test.
* a briefing on chi-squared test.
* An illustration of calculating p-value for ch-squared test with Excel.
* a tutorial on the chi-squared test.
Technique 2: Excel pivot table
* on multidimensional data analysis - a conceptual note. [about using Excel pivot table].
* using Excel pivot table to study homelessness.
Videos: (1) how to create pivot tables. (2) pivot table tutorial.
Also see blog note on pivot table as a research tool.
Technique 3: correlation analysis
* Linear equation: introduction.
* understanding correlation. (also on the basic steps to calculate correlation coefficient).
* an introduction to linear regression analysis.
* introduction to simple linear regression. (also take a look at linear vs exponential for some clarification of the linear concept).
* How to calculate linear regression using least square method.
* Correlation and causation (also study causal inference and causality).
* coefficient of determination. (r squared). (on how to calculate r squared).
* correlation coefficient. (also on calculating r and r squared).
* on standard error of the estimate in regression analysis. (more importantly on SSE, SSR, SST and R-squared)
* (i) Excel scatter diagram and calculation of coefficient of correlation (r). And then on showing (ii) trend [regression] line and the linear equation (also coefficient of correlation). This one is on the regression line and show more equation info on the line.
* multiple linear regression. part 1
* multiple linear regression part 2.
* multiple linear regression - evaluating basic models.
* Correlational research design. [another one on the same topic]
* Correlation hypothesis testing.
* on control variables; mediator and moderator. (further discussion on mediator under the topic of intervening variable).
* Comparing Descriptive, Correlational, and Experimental Studies
* using Excel for multiple regression analysis./ Excel 2016 regression analysis. Another video on demonstration (covering how to add on the function of regression analysis)
* interpreting Excel regression report: video 1; video 2; note the info on "adjusted R square" and the meaning of the major figures of the report.
** note that in Excel regression report, the p-value is a measure on 1 corner of the p-value curve; for a two-tailed test, the alpha value is (5%/2 = 2.5%). In this case the p-value is to be compared with 2.5% (for a two-tailed test).
*** also study this blog note related to correlation.
Technique 4: ANOVA
* basic ideas on one-way ANOVA.
* Introduction to the F-statistic.
* one-way ANOVA with manual calculation. [also take a look at the F-value calculator and a video on F-test calculation]
* A note on explanation of F-value.
