What is Research 101?
Research 101 is the foundational knowledge component of the IARCO Research Assessment. It is designed to test whether a participant understands the basic principles used to plan, conduct, analyze, and communicate research.
The assessment does not focus only on memorizing definitions. You should be able to recognize concepts in practical research situations, identify appropriate methodologies, interpret basic statistical results, distinguish different types of variables, understand research ethics, and identify appropriate academic writing and referencing practices.
Learn the fundamental concepts of research.
Use concepts in realistic research scenarios.
Interpret basic statistical information.
Introduction to Research
Understanding what research is and how systematic inquiry works.
1.1 What is research?
Research is a systematic process of investigating and analyzing information to answer questions, solve problems, or generate new knowledge.
1.2 The research process
A typical research process begins with identifying a topic or problem and progressively develops it into a focused, feasible research study.
- Identify a research topic or problem.
- Conduct a preliminary literature review.
- Formulate research questions and/or hypotheses.
- Select an appropriate methodology.
- Design the research plan.
- Collect and analyze data.
- Interpret findings.
- Communicate the results.
1.3 Research thinking
Good researchers do not select methods first and then attempt to fit a question to them. The research question, type of evidence required, variables, design, and analysis should work together.
Research Questions & Hypotheses
Turning broad interests into focused and testable research problems.
2.1 Research questions
A research question is the central question that guides a research study. It determines what information needs to be collected and what analysis may be appropriate.
A strong research question should generally be:
- Clear
- Relevant
- Focused
- Feasible
- Researchable
- Specific enough to investigate
2.2 Zero Research Question
In early-stage research planning, a Zero Research Question can be used as a broad starting question. It helps a researcher explore an area before developing more focused research questions.
2.3 Hypotheses
A hypothesis is a testable statement about an expected relationship, difference, or effect.
A null hypothesis, commonly written as H0, usually represents no difference, no association, or no effect.
2.4 Independent and dependent variables
When forming a hypothesis, identify what is being changed, compared, or used as a predictor and what outcome is being measured.
| Concept | Meaning | Example |
|---|---|---|
| Independent Variable (IV) | The variable used as the predictor, condition, or factor being compared. | LED light spectrum |
| Dependent Variable (DV) | The outcome being measured. | Lettuce yield |
Variables, Data Types & Measurement Scales
Recognizing the structure and level of measurement of research data.
3.1 Categorical and numerical variables
Variables can represent categories or numerical quantities. Understanding the nature of a variable helps determine appropriate summaries, visualizations, and statistical tests.
3.2 Measurement scales
| Scale | Description | Example |
|---|---|---|
| Nominal | Categories without an inherent order. | Eye color |
| Ordinal | Categories with an order, but the intervals between categories are not necessarily equal. | Letter grades, educational degree |
| Interval | Numerical values with meaningful equal intervals but no true zero. | Temperature in Celsius |
| Ratio | Numerical values with equal intervals and a meaningful zero. | Number of competitions entered |
3.3 Likert-scale data
Responses such as Strongly Disagree, Disagree, Neutral, Agree, and Strongly Agree are typically treated as ordinal measurements for introductory analysis.
3.4 Primary and secondary data
| Data | Meaning | Example |
|---|---|---|
| Primary data | Data collected directly by the researcher for the study. | Observations collected in person |
| Secondary data | Existing data originally collected by another person or organization. | Government reports or census datasets |
Research Methodology & Study Design
Selecting an appropriate research approach and understanding study designs.
4.1 Quantitative research
Quantitative research focuses on measurable variables, numerical data, statistical analysis, and testing relationships, differences, or effects.
Examples include surveys with numerical responses, standardized tests, measurements, and experiments.
4.2 Qualitative research
Qualitative research explores experiences, meanings, perspectives, context, and descriptions in depth.
In-depth interviews are appropriate when the objective is to understand how participants describe and experience a phenomenon.
4.3 Experimental design
In an experiment, the researcher deliberately controls or manipulates an independent variable and observes its effect on an outcome.
4.4 Randomization
Randomization assigns participants or experimental units to treatment groups using a random process. It helps reduce selection bias and can improve baseline comparability between groups.
4.5 Sampling and external validity
Sampling determines which individuals or units are included in a study. A sample that is restricted to a narrow group may not represent the wider population.
Research Ethics
Protecting participants and maintaining responsible research practices.
5.1 Informed consent
Participants should understand what participation involves, including relevant procedures, risks, benefits, and their right to participate voluntarily.
5.2 Privacy and confidentiality
Researchers should protect participant information and avoid unnecessarily exposing personally identifiable information.
5.3 Research involving minors
When research involves minors, appropriate consent procedures must be followed. Depending on the research setting and applicable requirements, parental or guardian consent may be necessary.
5.4 Voluntary participation
Participants should not be improperly pressured or forced to participate. Researchers should communicate participation conditions clearly.
5.5 Ethical research checklist
Literature Review & Research Gap
Understanding existing knowledge and identifying opportunities for research.
6.1 Purpose of a literature review
A literature review investigates relevant existing research to understand what is already known and to identify gaps, limitations, disagreements, or opportunities for further research.
6.2 What to look for in previous studies
- What questions have already been investigated?
- What methods have researchers used?
- What populations or samples were studied?
- What findings have been reported?
- Where do studies disagree?
- What limitations have been identified?
- What questions remain unanswered?
6.3 Research gaps
A research gap is an area where existing research does not adequately answer a question, where evidence is limited, or where a new context, population, method, or relationship could be investigated.
6.4 Paraphrasing and attribution
Rewriting a source in your own words does not remove the requirement for attribution. If an idea or information comes from a source, cite the source appropriately.
Research Proposals
Planning and communicating a study before conducting the full research.
7.1 What is a research proposal?
A research proposal describes what you plan to investigate, why the study matters, how you intend to conduct it, and how the resulting data will be analyzed.
7.2 Why write a proposal?
A proposal provides a clear plan of the research goals, methods, timeline, and resources before the full study is conducted.
7.3 Proposal vs. research paper
| Research Proposal | Research Paper |
|---|---|
| Plans and justifies a study. | Reports a completed study. |
| Describes proposed methods. | Reports the methods actually used. |
| Contains expected outcomes where appropriate. | Presents actual findings and interpretation. |
| Written before the full study. | Normally written after data collection and analysis. |
7.4 Major proposal components
- Title
- Abstract
- Introduction / research problem
- Research questions
- Hypotheses where appropriate
- Literature review
- Methodology
- Data analysis plan
- Timeline / project schedule
- References
7.5 Abstract
The abstract briefly summarizes the key aims, methods, and expected outcomes of a proposal.
7.6 Methodology
The methodology explains why the selected methods are appropriate and how the research will be conducted and analyzed.
7.7 Timeline
A project timeline organizes research activities and milestones. A Gantt chart is a common way of presenting such a schedule.
Descriptive Statistics
Summarizing and understanding numerical data.
8.1 Mean
The mean is calculated by adding all observations and dividing by the number of observations.
Mean
Example
8.2 Median
The median is the middle value after observations have been arranged in order.
For an odd number of observations, there is one middle observation. For an even number, the median is the average of the two middle observations.
8.3 Mode
The mode is the value that occurs most frequently in a dataset.
8.4 Mean, median and outliers
The mean is sensitive to extreme values. A large positive outlier can pull the mean toward the right side of the distribution, potentially making the mean greater than the median.
8.5 Variance and standard deviation
Variance describes the average squared deviation from the mean. Standard deviation is the square root of variance.
Sample variance
Standard deviation
Inferential Statistics & Hypothesis Testing
Using samples to evaluate evidence about populations.
9.1 Normal distribution
A normal distribution is commonly represented as a symmetric, bell-shaped distribution centered around its mean.
9.2 Checking normality
Normality assessment can help determine whether assumptions for certain parametric analyses are reasonable.
| Situation | Typical procedure emphasized in this assessment |
|---|---|
| Approximately 30 observations | Shapiro–Wilk test |
| More than 50 observations | Kolmogorov–Smirnov or other large-sample normality procedures |
9.3 p-values
A p-value describes how compatible the observed data are with the null hypothesis under the statistical model.
A p-value is not the probability that the null hypothesis is true, nor does statistical significance automatically mean that an effect is practically important.
9.4 Fail to reject the null
When p ≥ 0.05 under the stated significance level, the appropriate introductory interpretation is generally to fail to reject the null hypothesis.
This does not prove that the null hypothesis is true. Researchers may need to consider sample size, assumptions, measurement quality, effect size, and study design.
9.5 Type I and Type II errors
| Error | Meaning |
|---|---|
| Type I error | False positive: rejecting a true null hypothesis. |
| Type II error | False negative: failing to reject a false null hypothesis. |
9.6 Confidence intervals
A confidence interval provides a range of plausible values for a population parameter under the stated confidence procedure.
9.7 Confidence interval for a population mean
One-sample t confidence interval
Where:
- x̄ = sample mean
- t* = appropriate critical t value
- s = sample standard deviation
- n = sample size
9.8 Effect size and statistical significance
Statistical significance and practical importance are different concepts. A small p-value provides evidence against the null hypothesis under the chosen model, but researchers should also consider the magnitude and practical meaning of the effect.
Choosing Statistical Tests
Matching research questions and data structures with appropriate analyses.
10.1 The basic decision process
Do not choose a statistical test simply because it is familiar. Consider the research question, variable types, number of groups, whether observations are independent or paired, distributional assumptions, and the desired comparison or relationship.
- Identify the research question.
- Identify the variables and measurement scales.
- Check relevant assumptions and distributions.
- Select an appropriate statistical procedure.
- Run the analysis.
- Interpret the p-value and effect size where appropriate.
Core Test Selection Guide
10.2 Independent-samples t-test
Used to compare the means of two independent groups when the assumptions for the test are reasonably satisfied.
Example: comparing the average number of competitions entered by two independent groups of students.
10.3 Paired-samples t-test
Used when the observations are naturally paired, such as measurements from the same students before and after an intervention.
10.4 Mann–Whitney U test
A nonparametric approach used for comparing two independent groups when the assumptions required for a standard independent-samples t-test are not appropriate.
10.5 Correlation does not imply causation
An observational association between two variables does not by itself establish that one variable causes the other.
10.6 Parametric vs. nonparametric tests
Parametric tests generally make stronger assumptions about the data and, when those assumptions are reasonably met, can have greater statistical power.
Nonparametric procedures can be useful when the assumptions of common parametric methods are not appropriate.
Data Visualization & Academic Referencing
Presenting research data clearly and documenting sources correctly.
11.1 Choosing a visualization
| Data / Objective | Suitable visualization |
|---|---|
| Categorical proportions | Bar chart or pie chart |
| Many categorical groups | Ordered/horizontal bar chart or frequency table |
| Two continuous variables | Scatter plot |
| Distribution of numerical data | Histogram or other distribution-focused plot |
11.2 High-cardinality categorical data
Pie charts become difficult to interpret when there are many categories. A pie chart with a very large number of slices is generally a poor visualization choice.
11.3 IEEE referencing
IARCO Research 101 includes basic recognition of IEEE reference formatting.
Participants should understand the structure of an IEEE journal reference, including author information, article title, journal name, volume, issue, article/page information, year, and DOI when available.
11.4 DOI
When a DOI is available, the assessment expects participants to recognize the DOI presentation used in the provided IEEE-style references.
11.5 References section
Sources cited in the research work should appear in the reference list according to the required citation style. A peer-reviewed journal article cited in the literature review is an example of a source that belongs in the References section.
Reliability & Validity
Understanding consistency and whether a measure measures what it intends to measure.
Reliability
Reliability refers to the consistency of a measurement. A reliable instrument should produce reasonably consistent results under consistent conditions.
Validity
Validity concerns whether an instrument or measurement actually measures the concept it is intended to measure.
Research 101 Quick Reference
Research
Systematic investigation used to answer questions, solve problems, or generate knowledge.
Research Question
The foundation that guides the entire research study.
Hypothesis
A testable statement about an expected relationship, difference, or effect.
Independent Variable
Predictor, factor, or condition used to explain or influence an outcome.
Dependent Variable
Outcome that is measured.
Primary Data
Data collected directly by the researcher.
Secondary Data
Existing data collected by another source.
Mean
Sum of observations divided by the number of observations.
Median
Middle value after ordering observations.
Mode
Most frequently occurring value.
Standard Deviation
Measure describing the dispersion of observations.
p-value
Measures compatibility of observed data with the null hypothesis under the statistical model.
Type I Error
False positive: rejecting a true null hypothesis.
Type II Error
False negative: failing to reject a false null hypothesis.
Reliability
Consistency of measurement.
Validity
Whether the measure captures what it intends to measure.
Scatter Plot
Used to display the relationship between two continuous variables.
Literature Review
Examines existing research and identifies knowledge gaps or opportunities.
Before taking the Research 101 Assessment
You should be able to explain the concepts in this curriculum without relying only on memorized definitions. The assessment includes conceptual questions, practical research scenarios, basic calculations, statistical interpretation, methodology decisions, and academic writing/reference recognition.