Understanding and interpreting quantitative results: Foundation before data analysis

About Course

Instructor Profile

Dr. Rolina Dhital, MBBS, MHSc, PhD

Global Health Researcher | Founder, Health Action and Research

Dr. Rolina Dhital is a global health researcher and educator with extensive experience in quantitative and qualitative research. She is the founder of Health Action and Research. Blending scientific rigor with clarity and compassion, Dr. Dhital makes complex research concepts simple and relatable. Her teaching focuses on helping learners truly understand the “why” behind the data, bridging analysis with insight and purpose.

Course Language Feature

📌 The course is offered in two parallel versions:
– English version for the current version
– Regular spoken Nepali will be updated soon with a natural English mix (not formal/high-grade Nepali)

📌 All core resources, slides, and readings are in English.
📌 For now, learners can access the English version, but soon will be able to choose whichever version they are most comfortable with or use both for better understanding.
📌 This ensures that quantitative concepts feel accessible, relatable, and easier to grasp.

Learning Outcomes

By the end of the course, learners will be able to:

📌 Identify variable types and measurement scales
📌 Summarize and interpret descriptive statistics
📌 Understand associations through bivariate analysis
📌 Read and interpret regression outputs (OR, β, CI, p-values)
📌 Communicate results in clear, evidence-based language
📌 Be ready to analyze data using Stata or other statistical software

Course Content

Module 1: Foundations and Descriptive Statistics

1.1 What is quantitative research?

01:36
1.2.1 Counts/frequencies

01:03
1.2.2 Percent/proportion/rate

02:31
1.2.3 Mean and standard deviation

02:26
1.2.4 Median

01:27
1.3.1 What is a variable?

01:09
1.3.2 Types of variables- Continuous variable

02:09
1.3.3 Types of variables -Categorical variables

02:24
1.3.4 Measures of continuous variable

03:21
1.3.5 Measures of categorical variables

01:25
1.4 Summary of variables and their descriptive statistics

03:24
1.5 Interpretation of a descriptive table

04:06
1.6 Exercise

00:52
QUIZ1

Module 2: Basics of inferential statistics

2.1 What is inferential statistics?

01:50
2.2 Hypothesis testing

02:58
2.3 Sample

01:29
2.4.1 Underlying statistical principles (Jury analogy)

02:02
2.4.2 The analogy between jury decisions and statistical tests

02:50
2.5 Type I and Type II errors

03:29
2.6 Alpha (level of significance)

02:04
2.7 P-value

03:17
2.8 Effect size

04:08
2.9 Confidence interval

04:51
2.10 Point of no difference in continuous outcomes

01:31
2.11 Point of no difference (null effect) in Binary/Dichotomous outcomes

02:28
QUIZ 2

Module 3: Bivariate Analysis and Interpretation

3.1 Bivariate analysis

01:34
3.2.1 Chi square test

02:45
3.2.2 Interpretation of a Chi-square result

05:16
3.2.3 Fisher’s exact test

02:25
3.3.1 Independent sample t-test

02:58
3.3.2 Interpretation of Independent sample t-test result

02:49
3.3.3 Paired t-test

03:20
3.3.4 Interpretation of paired t-test
3.3.5 Mann-Whitney U test and its interpretation of the result

05:59
3.3.6 ANOVA

03:12
3.4.1 Pearson correlation

02:17
3.4.2 Spearman’s correlation

02:01
3.4.3 Interpretation of correlation

02:05
3.5.1 Interpretation of bivariate table 1

04:01
3.5.2 Interpretation of bivariate table 2

04:00
3.6.1 Summary of bivariate analysis 1

03:25
3.6.2 Summary of bivariate analyses 1

03:25
3.7.1 (Optional) Summary of bivariate tests for repeated observations 1

04:09
3.7.2 (Optional) Summary of bivariate tests for repeated observations 2

04:05
QUIZ 3

Module 4: Multivariable Analysis: basic concepts

Module 5 Basics of Logistic regression

5.1 Basics of logistic regression and odds ratio

03:15
5.2 Unadjusted Odds Ratio

01:26
5.3 Adjusted Odds Ratio

03:21
5.4 Interpreting 95%CI in logistic regression

05:40
5.5.1 Interpreting logistic regression table 1

02:22
5.5.2 Interpreting logistic regression table 2

02:48
5.5.3 Interpreting logistic regression table 3

05:07
5.5.4 Interpreting logistic regression table 4

02:59
5.5.5 Interpreting logistic regression table 5

02:19
5.6 Exercise to interpret logistic regression table

00:32
QUIZ 5

About Course

Instructor Profile

Course Language Feature

Learning Outcomes

What Will You Learn?

Course Content

Module 1: Foundations and Descriptive Statistics

1.1 What is quantitative research?

1.2.1 Counts/frequencies

1.2.2 Percent/proportion/rate

1.2.3 Mean and standard deviation

1.2.4 Median

1.3.1 What is a variable?

1.3.2 Types of variables- Continuous variable

1.3.3 Types of variables -Categorical variables

1.3.4 Measures of continuous variable

1.3.5 Measures of categorical variables

1.4 Summary of variables and their descriptive statistics

1.5 Interpretation of a descriptive table

1.6 Exercise

QUIZ1

Module 2: Basics of inferential statistics

2.1 What is inferential statistics?

2.2 Hypothesis testing

2.3 Sample

2.4.1 Underlying statistical principles (Jury analogy)

2.4.2 The analogy between jury decisions and statistical tests

2.5 Type I and Type II errors

2.6 Alpha (level of significance)

2.7 P-value

2.8 Effect size

2.9 Confidence interval

2.10 Point of no difference in continuous outcomes

2.11 Point of no difference (null effect) in Binary/Dichotomous outcomes

QUIZ 2

Module 3: Bivariate Analysis and Interpretation

3.1 Bivariate analysis

3.2.1 Chi square test

3.2.2 Interpretation of a Chi-square result

3.2.3 Fisher’s exact test

3.3.1 Independent sample t-test

3.3.2 Interpretation of Independent sample t-test result

3.3.3 Paired t-test

3.3.4 Interpretation of paired t-test

3.3.5 Mann-Whitney U test and its interpretation of the result

3.3.6 ANOVA

3.4.1 Pearson correlation

3.4.2 Spearman’s correlation

3.4.3 Interpretation of correlation

3.5.1 Interpretation of bivariate table 1

3.5.2 Interpretation of bivariate table 2

3.6.1 Summary of bivariate analysis 1

3.6.2 Summary of bivariate analyses 1

3.7.1 (Optional) Summary of bivariate tests for repeated observations 1

3.7.2 (Optional) Summary of bivariate tests for repeated observations 2

QUIZ 3

Module 4: Multivariable Analysis: basic concepts

4.1 Multivariable vs multivariate analysis

4.2 Dependent and independent variables

4.3 What is multivariable analysis

4.4 Adjusting for confounding

4.5 Common types of multiple variable analysis

QUIZ 4

Module 5 Basics of Logistic regression

5.1 Basics of logistic regression and odds ratio

5.2 Unadjusted Odds Ratio

5.3 Adjusted Odds Ratio

5.4 Interpreting 95%CI in logistic regression

5.5.1 Interpreting logistic regression table 1

5.5.2 Interpreting logistic regression table 2

5.5.3 Interpreting logistic regression table 3

5.5.4 Interpreting logistic regression table 4

5.5.5 Interpreting logistic regression table 5

5.6 Exercise to interpret logistic regression table

QUIZ 5

Module 6 Basics of linear regression

6.1 Basics of linear regression and standardized beta coefficient

6.2 Interpreting 95%CI in linear regression

6.3.1 Interpretating Linear regression table 1

6.3.2 Interpreting linear regression table -2