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RESEARCH MADE SIMPLE! I just went through a book on Research Methodology that every student, academic, or curious mind should check out! Super easy to understand Covers everything from basics to real applications Perfect for thesis, dissertation, or any research project Whether you're just starting or want to sharpen your research skills - this book is a game changer. It breaks down complex ideas in the simplest way possible. If research ever felt confusing, this is the guide you've been waiting for! Let's make research easier for everyone! #ResearchMadeEasy #ResearchMethodology #Students #EducationForAll #ThesisWriting #DissertationHelp #AcademicLife #StudySmart #LearnAndGrow #KnowledgeSharing #LinkedInLearning

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RESEARCH MADE SIMPLE! I just went through a book on Research Methodology that every student, academic, or curious mind should check out! Super easy to understand Covers everything from basics to real applications Perfect for thesis, dissertation, or any research project Whether you're just starting or want to sharpen your research skills - this book is a game changer.

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https://drive.google.com/drive/folders/1yxldljKkfXH8DVMDqzBNCnQrCefHhZTj

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Quick statistics guide | Ali MK Hindi 🇮🇶🇵🇸 | 54 comments

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Which statistical test to use? Many students find it challenging to decide. This PDF is a fantastic quick reference guide for commonly used statistical tests. It breaks down: ✅ How to determine the right test based on your data type ✅ Key assumptions (like normality and independence) ✅ Parametric vs. non-parametric alternatives ✅ Effect size, confidence intervals, and hypothesis testing The “Which test should I use?” section (page 10) is a great go-to when making decisions on statistical methods. 💬 What’s the most confusing part of choosing a statistical test?
♻️ Repost to help fellow researchers! Want research success tips?
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Links in comments! 👊😎 | 54 comments on LinkedIn

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Choosing the right statistical test can be tricky. Some tests may seem… | Asad Naveed | 42 comments

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Choosing the right statistical test can be tricky. Some tests may seem ideal but don’t fit all data types. Use this chart to guide you: 🔽 ________ Liked this post? I’m also launching a beginner's guide on "How to Publish a Paper Using AI ethically" on March 29th, 2025. If interested, join the waitlist here: https://lnkd.in/gUd3hGyq | 42 comments on LinkedIn

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Cora Yang on LinkedIn: #ailiteracy #ibcurriculum #edtech #atlskills #educationinnovation… | 16 comments

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Recently, I’ve been looking for practical ways to bring AI literacy into the International Baccalaureate MYP curriculum for my local community. I believe that… | 16 comments on LinkedIn

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750 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

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Ensuring clear and specific hypotheses is crucial for achieving accurate and effective results in research. It is essential to establish well-defined goals and outline the assumptions that will guide your analysis. Remember, the analyst's role is not to magically confirm or predict hypotheses but to propose suitable hypotheses and analyze data accordingly.

Here are key points to consider:
- Start with clear hypotheses and objectives.
- Formulate hypotheses accurately and specifically.

Analyzing provided data without influencing hypothesis formulation is also a valid approach. By adhering to these principles, you can enhance the quality and validity of your research outcomes.

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Looking through a whole new lens at IB Psychology IA
Have you ever realised that as IB learners, you are motivated to go a step further and become knowledge constructors instead of knowledge acquirers? As knowledge architects, you gain a deeper insight by partially replicating an existing research study based on the existing theoretical premise. You behave like mini researchers deploying an experimental design and validating your results in your study context, developing a new interpretation through your experimental observation that context influences results, which might vary from the original study. Kudos! You are now close to becoming a credible knowledge producer!!

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Green Organic Quote Poster (1080 x 1080 mm)

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Parametric and non-parametric tests are statistical methods used primarily in quantitative research to analyze numerical data. The distinction between parametric and non-parametric does not directly align with quantitative versus qualitative research methodologies but rather with the type of quantitative data you're analyzing and the assumptions you can make about that data. Here's a clearer breakdown:

Parametric Tests

Used in Quantitative Research: Parametric tests are designed for analyzing numerical (quantitative) data that meet certain assumptions.

Assumptions: The most common assumptions for parametric tests include the normal distribution of data and equal variances among groups. These tests are also typically used with interval or ratio data, where the measurements have a known and consistent relationship.

Examples: T-tests, ANOVA, and linear regression are examples of parametric tests. They are used when you're confident your data meets the necessary assumptions, allowing for more powerful and precise statistical analysis.

Non-Parametric Tests

Also Used in Quantitative Research: Non-parametric tests are used for quantitative data that do not meet the assumptions required for parametric tests. They can handle ordinal data (which have a ranked order but not a consistent interval between ranks) or nominal data (categorical data without any order), as well as interval or ratio data that does not follow a normal distribution.

Fewer Assumptions: These tests do not assume a normal distribution of the data and are more flexible in terms of the types of data they can analyze.

Examples: Chi-square tests, Mann-Whitney U tests, and Kruskal-Wallis tests are common non-parametric tests. They are useful for analyzing data that are skewed, ranked, or otherwise not suitable for parametric testing.

Application to Qualitative Research

While parametric and non-parametric tests are tools of quantitative research for analyzing numerical data, qualitative research focuses on non-numerical data such as words, images, or objects. Qualitative data analysis involves categorizing and thematically analyzing data, rather than using statistical tests. However, qualitative researchers may use quantitative methods, including non-parametric tests, in mixed-methods research to analyze survey results or other quantifiable elements within a primarily qualitative study.

Summary

Parametric and non-parametric tests are both used in quantitative research depending on the nature of the data and whether it meets certain statistical assumptions.

Qualitative research primarily involves non-numerical data and does not typically use parametric or non-parametric tests. However, mixed-methods approaches can incorporate quantitative analyses, including non-parametric tests, alongside qualitative analyses.

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Interpreting results

Interpreting results from Grubbs' Test is straightforward. Once the value of Z is calculated for each data point, Grubbs' considers the largest value of Z in the dataset and calculates its P value.

The P value is interpreted like normality testing: If the P value corresponding to this Z is less than the alpha value chosen (such as .05), it is considered a significant outlier. The results page will then mark this data point as an outlier. If that P value is greater than alpha, the test concludes there is no evidence of an outlier in your dataset.

Notice that although the Grubbs' Test only determines if the most extreme value is an outlier, the entire dataset is used to calculate the mean and standard deviation for the test.

Limitations of Grubbs' Test

There are two main assumptions of Grubbs' Test that limit its practical usage.

First, Grubbs' only looks for one outlier in the dataset. It gives a general answer to the question "Is there at least one outlier in this data?". This has a couple blindspots:

If you wish to see if there is more than one outlier after the first test, it is not appropriate to remove the first outlier and run Grubbs' again to look for more.

If there are multiple outliers close together, these "neighbors" can result in Grubbs' not labeling either an outlier. This is called masking.

The second main limitation is that Grubbs' assumes the data was sampled from a normal (or gaussian) distribution. However, it's rare to observe "normal" data in the world. For example, in the biological sciences, data often follows a lognormal distribution, which looks at first to have obvious outliers if the pattern is not recognized appropriately. See our example that uses Grubbs' Test on a lognormal distribution.

There are additional outlier identification tests available in Prism. The most popular alternative is called ROUT. See a comparison of these two methods here.

Graphing outliers

Outliers lend themselves to graphics perhaps more than any other aspect of statistics. Scatter plots, box plots, and violin plots are common ways to see where your dataset clumps together and which values are the extremes.

That said, visual inspection of outliers is still tricky. If done arbitrarily the human eye may conclude that any extreme value is an outlier, which is not always the case!

This calculator does not include a chart for outliers, but Prism allows you to create fully-customizable charts and graphics of all kinds.

Next steps

Enjoying this calculator? Prism offers more capabilities for outlier detection, including methods like Grubbs' Test, ROUT, and more. We offer a free 30-day trial of Prism and its publication-ready graphic creation.

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Free web calculator provided by GraphPad Software. Performs Grubbs' test Extreme Studentized Deviate (ESD) method. Clear results with links to extensive explanations.

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THE BEST UNDERSTANDING OF STATS IN PSYCH

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THE BEST UNDERSTANDING OF STATS IN PSYCH

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An Introduction to Psychological Statistics (Foster et al.)

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We are constantly bombarded by information, and finding a way to filter that information in an objective way is crucial to surviving this onslaught with your sanity intact. This is what statistics, …

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Inferential Statistics Definition & Examples

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Inferential statistics are procedures that allow researchers to use samples to draw conclusions about populations.

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The art of writing research report in IB

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The art of writing research report in IB

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title page

appendices

graphs

tables

section headings

works cited page (references)

are not included in the final word count.

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Calculations on Descriptive and Inferential Statistics in IB Psych IA

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Calculations on Descriptive and Inferential Statistics in IB Psych IA

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No need to include calculations of descriptive statistics in the appendix

A hand written or screenshot of inferential statistics calculation is a MUST in the appendix

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Whether you go for the exact materials as per the original research or very close set of material choices, justify for the inclusion of each material.

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Is it important for a student to access the original study because he/she has to cite it for the IB Psych IA?

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Is it important for a student to access the original study because he/she has to cite it for the IB Psych IA?

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Original study is important NOT for you to cite but for you to be aware of what the original researchers talked about on the theoretical framework of the study. The original study is to know about the origibnal content and context.

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quasi-experiments, natural experiments, or correlational studies - e.g. gender, age, or culture may not be the IV

conformity and obedience studies

animal research

placebo experiments

experiments involving ingestion (e.g., food, drink, smoking, drugs)

experiments involving deprivation (e.g. sleep, food)

sample with participants under 16 years

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At least 20 data points in their study - that is, two groups of 10 (independent samples) or one group of 10 (repeated measures) as a sample size <10 in a group makes no sense for valid statistical analysis

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Method and procedure in IB Psych IA-a must includes

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Method and procedure in IB Psych IA-a must includes

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identify the design

describe how it was done in their study

and then explain why the method waschosen

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Why adopt a simple experimental study in IB Psych IA?

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Why adopt a simple experimental study in IB Psych IA?

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Simple experimental study involves:

The manipulation of one independent variable and measurement of one dependent variable, while other variables are kept constant. Controlled setup

Thus studies like correlational studies, quasi-experiments, and natural experiments (that is, any research undertaken without control over the independent variable and without a controlled sampling procedure) are not acceptable for the simple experimental study.

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1. A blank copy of your letter of consent.
2. Briefing notes/standardized directions
3. Materials
4. Debriefing notes
5. Raw data
6. Calculations of inferential statistics

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An alphabetized list of the sources cited in your introduction.

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1. Link the findings to the theory in your introduction.
2. Discuss the strengths and limitations of your design, sample, and procedure/materials.
3. Suggest modifications for future replications to address the limitations in your investigation.
4. End with a final statement of conclusion with regard to your hypothesis.

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Interpret your original study based on your IA research results

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Interpret your original study based on your IA research results

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1. Apply descriptive statistics.
2. Apply inferential statistics.
3. Include one graph which clearly reflects your results in light of your hypothesis.
4. State the significance of your data with regard to your hypothesis.
5. Explain your statistical findings with regard to the hypothesis.

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Type I Error (False Positive):

You reject a true null hypothesis. (Null hypothesis is true, but you reject it.)

e.g- I detect danger (when the danger is not there=Null hyp present)

Type II Error (False Negative):

You fail to reject (accept) a false null hypothesis. (Null hypothesis is false, but you fail to reject/ accept it.)

e.g. I fail to detect danger (when the danger is there+Null hyp absent)

Remember for null hypothsis,, e do not use the word accept fo null hypothesis but use as fail to reject

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Key Insights from the Table

1% Level of Significance is ideal for research that cannot afford false positives, such as health-related studies or legal decisions. Requires larger sample sizes to mitigate the risk of missing real effects (Type II error).

5% Level of Significance is the default choice for balanced exploratory and confirmatory studies. Works well across small to moderate sample sizes and diverse fields.

10% Level of Significance is suitable for exploratory research or noisy data sets. It detects weak effects but comes at the cost of more false positives (Type I error). Ideal for small sample sizes when detecting weak relationships is more important than being strictly accurate.

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1. Identify and explain the design of your study.
2. Identify and explain the sampling technique.
3. Describe the characteristics of your sample.
4. Explain the choice of participants.
5. Explain controls that were used.
6. Explain how materials were developed and why you made the choices that you did.
7. Describe your procedure, including how ethical standards were met.

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1. An explanation of the theory upon which your investigation is based.
2. Definitions of any terminology relevant to your study.
3. An explanation of how your study is linked to the theory.
4. A statement of the aim of your investigation and why it is worth studying. 5. An operationalized null and research hypothesis. Be sure to include the statistical measure in your hypothesis.

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Psychology Sorted - conducting your Psychology Internal Assessment experiment remotely