I'm in my mid 20s and I've never been in a relationship. I think about it CONSTANTLY. Like 24/7, all day everyday and even dream about it. Sometimes I feel like it's my only purpose in life. I have many goals and dreams but none of them have ever come into fruition so I've given up on them and this is the one thing my brain has chosen to hyperfocus on. I feel so inadequate because I've never had a boyfriend and it makes me feel so unattractive.
One the one hand, I feel like it's normal because most women my age who want relationships have had them. I feel like the odd one out. On the other hand, I feel so dependant on something that's not even in my life. I feel like my self esteem will only improve if I get into a relationship. I feel this hunger and void inside me that can only be filled with having a partner. I do have some trauma but I think this is separate from that tbh. I think it's normal to yearn for a relationship if you've never been with one but everyone online always says that this mindset is abnormal and unhealthy.
I've been yearning for a partner since I was a child and it has never happened for me. I genuinely feel like something is wrong with me. Should I try to change my mindset and find a therapist before getting into a relationship?
I've been passionate about psychology for many years. I'm currently a PCB student, so whenever I talk about pursuing psychology, almost everyone tells me to go for the medical route and become a psychiatrist instead.
The thing is, I'm genuinely more interested in psychology itself than medicine. At the same time, I keep hearing that psychology isn't a good career in a country like India, which makes me second-guess myself.
If I choose psychology, I plan to pursue higher education abroad later on.
Please friends, enlighten me and share honest advice.
Appreciate ya all!!!
I am not a huge fan of Rorschach either, but when a psychology person quotes the MBTI - that's when I know.
We in India, are taught in a very exam-centric way - with a teacher using a smartboard and a digital pen. A slide chock-full of text appears on the board and a teacher literally reads off it, while drawing golas (circles) using the smart pen.
If you did an analysis of their linguistics and intonation, you would think they were all trained by one person - the same voice inflections, the same teaching style.
But the best universities across the world put their educational materials on the internet about a decade ago. Some of you are already too deep into the system - with little time for your UGC / CUET PG exams. And for those this post won't do much.
But if you are starting, these two playlists will absolutely change the way to look at the subject.
The first is MITs Introduction to Psychology Playlist, a part of the MIT OCW initiative. The engineering students, the IIT/NIT brass is fully aware of the magical wonders of learning from the very professors - whose textbooks we read. Many professors who have won Nobel Prizes.
https://www.youtube.com/watch?v=2fbrl6WoIyo&list=PL44ABC9278E2EE706
And the second is Yale's Introduction to Psychology (PSYC 110)
https://www.youtube.com/watch?v=P3FKHH2RzjI&list=PL6A08EB4EEFF3E91F
The professor usually recommends a textbook too. And prescribes reading attached with each lecture. If you can do it this way, instead of the smartboard and circles approach, you will pave way for research papers in the future (I am confident of it.).
I am a 1st year psychology student and I have so much time because I learn it myself without going to college. The thing is except from academics I want to learn myself
So I chose
Crash course psychology in YouTube
after this what should I learn ?
My wish is to inspire others and give wings to others dream
Uplifting people when they are sad etc.
My Goal is to become a clinical psychologist.
For now can anyone guide me what to do after this also provide me free resources to learn.
I asked perplexity to build a roadmap. I feel it is one of those things that these models to really well (sometimes a little too extensively). All sources are public and you can tweak it to your convenience. Also remember that Research and Psychometrics are almost mandatory in higher level psych.
So the prompt was:
I am new to statistics and as a psychology student, I do not have a solid background. I would love to build its fundamentals from scratch and be able to use my knowledge in research projects.
Please build me a detailed curriculum starting from zero. You can do a monthly/weekly breakup with topics, references of textbooks and YouTube videos/ playlists.
And Here’s the Roadmap:
You can reach applied, research-ready competence in statistics for psychology in 12 months with 5–7 hours/week by following a structured, textbook‑plus‑YouTube curriculum and integrating R and Python gradually. The plan below starts from zero and is designed for an ambitious psychology student who wants to use stats in real research, not just pass exams.
I've gone through it and revised it suitably - written by AI, properly vetted by a Human.
***
OVERALL STRUCTURE: 12 months
- Months 1–3: Foundations
research design, measurement, descriptive statistics, probability, distributions, basic r/Python.
- Months 4–6:
Core inferential tools – estimation, hypothesis testing, t‑tests, one‑way and factorial ANOVA, repeated‑measures.
- Months 7–9:
Correlation, regression, categorical data, nonparametric tests, reliability.
- Months 10–12:
Psychometrics basics, more advanced modelling (optional), and a full mini‑research data analysis project.
***
CORE RESOURCES:
I’ll reference these repeatedly in the monthly plan; you don’t need to buy all of them, but having at least one main textbook plus one R text is ideal.
I. Main statistics textbooks (psychology-focused)
- *Introduction to Statistics in the Psychological Sciences* (open, free; web version and downloadable PDF). Excellent, specifically written for psych students, covers descriptive stats, hypothesis testing, t‑tests, ANOVA, correlation, regression, chi‑square, with exercises.[1][2]
- *Statistics for the Behavioral Sciences* by Gravetter & Wallnau. Classic psych stats text; very clear conceptual explanations and step‑by‑step worked examples.[3][4]
- Optional: *Introductory Statistics for Psychology* (Elsevier) if you want another perspective, but the first two are enough.[5][6]
2. R + statistics for psychology
- *Learning Statistics with R: A tutorial for psychology students and other beginners* (Danielle Navarro; free online via LibreTexts). Integrates intro stats with R, exactly for psychology students.[7][8]
- *R for Psychological Science* by Danielle Navarro – a free online resource that assumes some basic research methods and teaches R for data analysis, visualization, and more in psychology contexts.[9]
- Crash‑course article “Learn R for Psychology Research: A Crash Course” – good quick orientation to what you actually need in R for research.[10]
- “Using R for psychological research” tutorials at the Personality Project – practical guides for psych researchers.[11]
ADDENDUM: YouTube channels/playlists (statistics for psychology)
- “Statistics for Psychology” YouTube channel – full course lectures for a statistics in psychology class.[12]
- Playlist “Introduction to statistics for undergraduate students of psychology” (Anna’s online lectures) – 9 lectures (~4 hours) ideal early on.[13]
- “Statistics for psychology” playlist (SPSS‑oriented, but concepts are universal; helpful if your uni uses SPSS).[14]
You can treat:
- Gravetter/Wallnau + *Learning Statistics with R* as your primary combination, or
- *Introduction to Statistics in the Psychological Sciences* + *Learning Statistics with R* if you prefer open/free materials.[2][8]
MONTH 1: FOUNDATIONS
**Goals:** Understand what statistics are for in psychology, basic research design, scales of measurement, and descriptive statistics (central tendency, variability, basic graphs).
- Week 1
- Read “Why do we learn statistics?” and “A brief introduction to research design” from *Learning Statistics with R* (Ch. 1–2).[8]
- Watch 2–3 intro lectures from Anna’s “Introduction to statistics for undergraduate students of psychology” playlist.[13]
- Take notes in Obsidian on: variables (IV/DV), experimental vs correlational designs, reliability/validity (short summaries).
- Week 2
- From Gravetter or *Introduction to Statistics in the Psychological Sciences*: chapters on **frequency distributions** and **scales of measurement**.[2][3]
- From *Learning Statistics with R*: Ch. 5 “Descriptive statistics” up to measures of central tendency.[8]
- Week 3
- Learn mean, median, mode, range, variance, standard deviation conceptually (use Gravetter/Wallnau examples).[3]
- Watch descriptive stats videos from the “Statistics for Psychology” channel or Power Within playlist.[12][15]
- Week 4
- Install R and RStudio; follow “Getting started with R” and basic commands from *Learning Statistics with R* Ch. 3–4.[8]
- In Python, set up a simple environment (e.g., Anaconda + Jupyter) and learn basic operations with lists/arrays (no stats yet).
***
MONTH 2: PROBABILITY AND DSITRIBUTIONS
**Goals:** Intuitive grasp of probability, normal distribution, z‑scores, sampling distributions.
- Week 1
- From Gravetter: chapters on **probability** and **z‑scores**.[3]
- From *Learning Statistics with R*: Ch. 9 “Introduction to probability” (focus on examples).[8]
- Week 2
- Watch YouTube lectures on the normal distribution and z‑scores (e.g., “Statistics for Psychology – normal distribution review”).[16]
- Do numeric practice: convert raw scores to z‑scores and interpret them using textbook exercises.[2][3]
- Week 3
- Read about **sampling distributions** and **central limit theorem** in Gravetter or *Introduction to Statistics in the Psychological Sciences*.[2][3]
- In R: simulate small samples from a normal distribution (rnorm()) and look at histograms; see how sample means vary.[8]
- Week 4
- In Python (with pandas and matplotlib): load a simple dataset (e.g., the iris data) and plot histograms, compute mean/SD.
- Summarize in notes: how sampling distributions link to confidence intervals and hypothesis testing.
***
MONTH 3: HYPOTHESIS AND TESTING
**Goals:** Understand confidence intervals, null hypothesis significance testing, p‑values, Type I/II errors, and effect size conceptually (without heavy math).
- Week 1
- Gravetter: chapters “Introduction to hypothesis testing” and “Estimating unknown quantities from a sample” (if using the book).[3]
- *Learning Statistics with R*: Ch. 10 “Estimating unknown quantities from a sample” (confidence intervals) and start Ch. 11 “Hypothesis testing”.[8]
- Week 2
- Watch playlist lectures on hypothesis testing (Power Within + Statistics for Psychology channel).[15][12]
- Write your own explanation of p‑value, Type I and Type II errors, and power in your notes.
- Week 3
- In R, follow *Learning Statistics with R* examples for basic one‑sample tests and interpreting output (even if you haven’t formally done t‑tests yet).[8]
- In Python, learn basic use of SciPy for simple tests (scipy.stats.ttest_1samp) just as a preview.
- Week 4
- Read about **effect sizes** (Cohen’s d, r) in *Introduction to Statistics in the Psychological Sciences* or Gravetter.[2][3]
- Create small “cheat sheets” summarizing: hypothesis testing steps; how to report results in APA style.
***
MONTH 4: t-TESTS AND NONPARAMETRC EQUIVALENTS - PART 1
**Goals:** Use and interpret one‑sample and independent‑samples t‑tests in psychology contexts; understand assumptions.
- Week 1
- Gravetter: chapters on **t statistic** and **t test for two independent samples**.[3]
- *Learning Statistics with R*: Ch. 13 “Comparing two means” (one‑sample and independent‑samples sections).[8]
- Week 2
- R: reproduce examples from Ch. 13 using your own small dataset or textbook data; learn to interpret output (t, df, p, confidence interval, effect size).[8]
- Watch a YouTube tutorial running an independent‑samples t‑test in R or SPSS (from stats‑for‑psychology playlists).[14][12]
- Week 3
- Python: use pandas + SciPy to run an independent‑samples t‑test; compare R and Python outputs for the same data.
- Read about assumptions: normality, homogeneity of variance; see examples in Gravetter.[3]
- Week 4
- Briefly read about nonparametric alternatives (e.g., Wilcoxon rank‑sum) in *Learning Statistics with R* (section on non‑normal data).[8]
- Practice interpreting outputs and writing results sections: “t(df) = value, p = …, d = …”.
***
MONTH 5: PAIRED t-TESTS AND ONE-WAY ANOVA
**Goals:** Understand dependent/paired designs, one‑way ANOVA, and basic post‑hoc comparisons.
- Week 1
- Gravetter: chapter on **t test for two related samples**.[3]
- *Learning Statistics with R*: continue Ch. 13 for paired‑samples t‑test.[8]
- Week 2
- R: analyze a simple pre‑post psychology dataset using paired t‑test; interpret changes and effect size.
- Python: replicate with SciPy and pandas.
- Week 3
- Gravetter: introduction to **analysis of variance** (one‑way ANOVA).[3]
- *Learning Statistics with R*: Ch. 14 “Comparing several means (one‑way ANOVA)”.[8]
- Week 4
- R: run one‑way ANOVA (aov() or equivalent) on a small dataset, examine F, p, and post‑hoc tests.[8]
- Watch YouTube lectures explaining ANOVA logic (between‑groups vs within‑groups variability) in psych contexts.[12][13]
***
MONTH 6: FATORIAL ANALYSIS AND REPEATED MEASURES ANOVA
**Goals:** Understand interactions, basic factorial designs, and repeated‑measures ANOVA.
- Week 1
- Gravetter: chapters on **repeated‑measures ANOVA** and **two‑factor ANOVA (independent measures)**.[4][3]
- Review sections in *Introduction to Statistics in the Psychological Sciences* on ANOVA extensions if present.[2]
- Week 2
- R: follow *Learning Statistics with R* Ch. 16 “Factorial ANOVA” for basic examples.[8]
- Focus on interpreting interaction plots and understanding how interactions are reported.
- Week 3
- Python: learn to run a simple factorial ANOVA with statsmodels (e.g., using ols + anova_lm), focusing on interpretation rather than code sophistication.
- Watch at least one lecture on repeated‑measures ANOVA in psychology (Power Within playlist or similar).[15]
- Week 4
- Summarize in notes: when to use one‑way vs factorial vs repeated‑measures designs; common pitfalls (sphericity, etc., high‑level intuitive understanding).
***
MONTH 7 CORRELATION AND SIMPLE REGRESSION
**Goals:** Understand correlation, simple linear regression, and their use in psychological research.
- Week 1
- Gravetter: chapters on **correlation** and **regression**.[4][3]
- *Learning Statistics with R*: Ch. 15 “Linear regression” (start with correlation section first).[8]
- Week 2
- R: compute Pearson and Spearman correlations; draw scatterplots and regression lines, interpret r, R², and regression coefficients.[8]
- Python: replicate regression using statsmodels (ols) and compare outputs.
- Week 3
- Watch YouTube lectures on correlation and regression in psychology; focus on conceptual issues (correlation ≠ causation, outliers, range restriction).[13][12]
- Read sections on reporting correlation/regression in APA style in your chosen textbook.[3]
- Week 4
- Practice writing short results paragraphs for correlation and regression analyses, including effect sizes and confidence intervals where possible.
***
MONTH 8 MULTIPLE REGRESSION AND LOGISTIC REGRESSION
**Goals:** Move from simple regression to multiple regression; get a high‑level feel for logistic regression used in psych/clinical contexts.
- Week 1
- *Learning Statistics with R*: deeper parts of Ch. 15 on multiple linear regression and model interpretation.[8]
- Read Gravetter sections on multiple regression if available in your edition, or use online resources.[3]
- Week 2
- R: run a multiple regression (two or more predictors) on a psychology dataset (e.g., personality scales predicting well‑being), check coefficients, p‑values, R².[11]
- Python: run the same model with statsmodels and compare results.
- Week 3
- Read a brief intro to logistic regression (binary outcome, e.g., clinical diagnosis yes/no) from an online psych methods resource or chapter if available.[8]
- In R or Python, run a simple logistic regression example and interpret odds ratios informally (conceptual focus).
- Week 4
- Summarize model diagnostics at a high level: residuals, multicollinearity, influential points; you don’t need deep theory, just awareness.
***
MONTH 9 CATEGORICAL DATA, CHI-SQUARE AND NON PARAMETRIC TESTS
**Goals:** Analyze categorical data (contingency tables), understand chi‑square tests, and main nonparametric tests used when assumptions fail.
- Week 1
- Gravetter: chapter on **chi‑square statistic** (goodness‑of‑fit and independence).[17][3]
- *Learning Statistics with R*: Ch. 12 “Categorical data analysis”.[8]
- Week 2
- R: run chi‑square tests of independence on 2×2 or larger tables, interpret χ², df, p, and effect size (e.g., Cramer’s V).[8]
- Python: replicate using SciPy (chi2_contingency).
- Week 3
- Read about nonparametric tests: Mann–Whitney U, Wilcoxon signed‑rank, Kruskal–Wallis, etc., using *Learning Statistics with R* and textbook sections.[3][8]
- Watch at least one lecture explaining when nonparametrics are useful (ordinal data, severe non‑normality, small samples).[12]
- Week 4
- Practice deciding which test to use for small example scenarios and write brief analysis plans in your notes.
***
MONTH 10: RELIABILITY, BASIC PSYCHOMETRICS AND FACTOR ANALYSES
**Goals:** Link statistics to measurement: reliability, validity, scale construction, and basic factor analysis concepts.
- Week 1
- Review measurement chapters: reliability (Cronbach’s alpha), validity, scales of measurement from *Learning Statistics with R* Ch. 2 and related sections.[8]
- Read psychometrics basics from a supplemental source if available (many notes online linked from psych stats textbooks).[8]
- Week 2
- R: compute Cronbach’s alpha for a multi‑item scale (using psych package), interpret reliability.[11]
- Python: compute alpha manually or using a library (if convenient) for understanding.
- Week 3
- Read an introductory section on exploratory factor analysis (EFA) from online resources or a psychometrics chapter; focus on idea of latent variables and loadings.[8]
- Watch a YouTube tutorial explaining EFA conceptually in psych.
- Week 4
- R: run a simple EFA on a small dataset (e.g., personality scale), look at loadings and decide on number of factors in a rough, intuitive way.[11]
- Note how this links back to reliability and construct validity.
***
MONTH 11: APPLIED RESEARCH PROJECT
**Goals:** End‑to‑end data analysis for a small psychology research question using R and optionally Python.
- Week 1
- Choose a dataset: open psych dataset (e.g., from Personality Project or R’s built‑in datasets) with variables of interest.[9][11]
- Formulate 2–3 hypotheses you can test with t‑tests/ANOVA/correlation/regression.
- Week 2
- In R, perform full analysis: data cleaning, descriptive statistics, visualizations, appropriate inferential tests, effect sizes, and assumptions checks.[8]
- Keep a detailed analysis script (this becomes a template for future projects).
- Week 3
- In Python, replicate at least part of the analysis (descriptives, one inferential test, one regression) for comparison.
- Watch a lecture or tutorial on writing a results section for a psychology paper (APA style).
- Week 4
- Write a mini “results” and “methods” section for your project, including descriptions of statistical procedures and key findings.
- Use textbooks and *Learning Statistics with R* examples of reporting for guidance.[3][8]
***
MONTH 12 CONSOLIDATION, PAPERS AND ADVANCED OVERVIEW
**Goals:** Consolidate, read research articles comfortably, get an overview of advanced topics (multilevel models, Bayesian, meta‑analysis) without needing mastery.
- Week 1
- Revisit any weak areas (e.g., ANOVA or regression) using textbook chapters and YouTube lectures.[12][3]
- Make “concept maps” linking design → test → assumptions → interpretation for all main tests you learned.
- Week 2
- Read *Learning Statistics with R* Ch. 17 “Bayesian statistics” at an intuitive, non‑technical level.[8]
- Watch one overview video on multilevel/hierarchical models in psychology (just to know what they are).
- Week 3
- Select 2–3 published psychology papers and carefully read their methods/results sections; for each paper, identify: design, tests used, effect sizes, and interpretations.
- Use your textbooks to look up any unfamiliar tests.[2][3]
- Week 4
- Create a personal “psych stats handbook” in Obsidian: one note per major test, with when to use, assumptions, R and Python commands, and reporting template.
- Plan next steps: deeper regression/modelling, multilevel models, or more psychometrics depending on your interests.
***
Sources
[1] Introduction to Statistics in the Psychological Sciences https://open.umn.edu/opentextbooks/textbooks/709
[2] Introduction to Statistics in the Psychological Sciences - IRL @ UMSL https://irl.umsl.edu/oer/25/
[3] Statistics for the behavioral sciences : Gravetter, Frederick J., author https://archive.org/details/statisticsforbeh0000grav_s3u5
[4] Statistics for The Behavioral Sciences|Paperback - Barnes & Noble https://www.barnesandnoble.com/w/statistics-for-the-behavioral-sciences-frederick-gravetter/1122970749
[5] Introductory Statistics for Psychology - 1st Edition | Elsevier Shop https://shop.elsevier.com/books/introductory-statistics-for-psychology/levine/978-0-12-445480-4
[6] Introductory Statistics for Psychology - ScienceDirect.com https://www.sciencedirect.com/book/monograph/9780124454804/introductory-statistics-for-psychology
[7] Learning Statistics with R: A tutorial for psychology students and ... https://open.umn.edu/opentextbooks/textbooks/learning-statistics-with-r-a-tutorial-for-psychology-students-and-other-beginners
[8] Learning Statistics with R - A tutorial for Psychology Students and ... https://stats.libretexts.org/Bookshelves/Applied_Statistics/Learning_Statistics_with_R_-_A_tutorial_for_Psychology_Students_and_other_Beginners_(Navarro)
[9] R for Psychological Science - Danielle Navarro https://psyr.djnavarro.net
[10] Learn R for Psychology Research: A Crash Course https://nmmichalak.github.io/nicholas_michalak/blog_entries/2018/nrg03/nrg03.html
[11] Using R for psychological research - The Personality Project http://personality-project.org/r/r.guide.html
[12] Statistics for Psychology - YouTube https://www.youtube.com/@statisticsforpsychology
[13] Introduction to statistics for undergraduate students of psychology https://www.youtube.com/playlist?list=PLNRmHCgvp2PG5xkIT7Zt8Va-kJFw6XOf4
[14] Statistics for psychology - YouTube https://www.youtube.com/playlist?list=PLjx4h06KYipLlp8Rd0T4ddrJsz5ntkznU
[15] Statistics in Psychology- Normal Probability Curve in Psychology and Standard Deviation https://www.youtube.com/watch?v=SS7u0Vv-hac
[16] Statistics for Psychology - YouTube https://www.youtube.com/watch?v=DWv-4rVY_L8
[17] Statistics for the behavioral sciences - 9th edition https://archive.org/details/statisticsforbeh0000grav_w2e9
[18] R for Psychological Science? - YouTube https://www.youtube.com/watch?v=xFkEbYk0C0Q
[19] [PDF] STATISTICS-IN-PSYCHOLOGY-AND-EDUCATION-Garrett.pdf https://arunodayauniversity.ac.in/wp-content/uploads/2025/01/STATISTICS-IN-PSYCHOLOGY-AND-EDUCATION-Garrett.pdf
[20] [PDF] An Introduction to Psychological Statistics - LibreTexts https://batch.libretexts.org/print/Letter/Finished/stats-7077/Full.pdf
[21] [PDF] Discovering Statistics Using IBM SPSS Statistics 4th c2013 Andy Field https://sadbhavnapublications.org/research-enrichment-material/2-Statistical-Books/Discovering-Statistics-Using-IBM-SPSS-Statistics-4th-c2013-Andy-Field.pdf
[22] [PDF] Statistics for the Behavioral Sciences, 10th Edition http://ndl.ethernet.edu.et/bitstream/123456789/29095/1/Frederick%20J%20Gravetter_2017.pdf
[23] Discovering Statistics Using IBM SPSS Statistics - Atlantic Books https://atlanticbooks.com/products/discovering-statistics-using-ibm-spss-statistics-9781529630008
[24] Learning Statistics with R by Daniel Navarro - BookFusion https://www.bookfusion.com/books/513488-learning-statistics-with-r
[25] Discovering Statistics using IBM SPSS Statistics: | Guide books https://dl.acm.org/doi/10.5555/2502692
[26] Statistics for the Behavioral Sciences by Frederick J. Gravetter ... https://www.goodreads.com/book/show/4184092
[27] Learning Statistics With R, by Danielle Navarro https://onlinebooks.library.upenn.edu/webbin/book/lookupid?key=olbp88932
[28] Discovering Statistics Using IBM SPSS Statistics - Google Books https://books.google.com/books/about/Discovering_Statistics_Using_IBM_SPSS_St.html?id=83L2EAAAQBAJ
[29] Statistics for the Behavioral Sciences https://books.google.com/books/about/Statistics_for_the_Behavioral_Sciences.html?id=ZM_9owzdfi4C
[30] Learning statistics with R - Danielle Navarro https://tidylsr.djnavarro.net
So I did 4 ba in psychology and now I have enrolled in 1 year masters clinical psychology program at amity but still preparing for the rci approved program but my preparation is not at the best and I don't think that I will get in this year.
While talking to some people I got to know they never did an rci approved program so was wondering how important it is for jobs with a good pay as I thought I could do the 1 yr amity program and prepare for the rci program to try next year or get a job.
I don't know how realistic this plan is and would really appreciate it if anyone has any advice
Tests like MCMI (and MMPI) are solid and validated inventories for determining personality types based on clinical diagnoses. But they are proctored by companies like Pearson (who hold exclusive rights to it).
In India one such test works cost 65000
Since the theory is out in the public domain, why can’t we build our own.
I know the frivolity of this thought too, but I feel we need east-centric tests too; and secondly the world could use cheaper inventories.
It's more easy to say than doing it. Some situations are more easier than others. Some guilts are real and some others not. I don't mind comments being vague. How yo let go the past? How to move on? What do you do when you don't like your hobbies anymore or your anxiety can't let you get distracted? When meditation is harsh to do? I want to read your stories!
I do not understand this double standard between everyone claiming they have one of these conditions yet at the same time it still feels hugely stigmatised and get treated differently, especially if the symptoms are more perceivable?
Full disclosure: I’m personally quite fond of it. I find it offers a therapeutic release. But I’m articulate. And I knows how to vent myself.
Do you think it’s a good technique for most people?
I’ll also share an interesting experience. Suffering my internship I had a case that I was assisting with. I told this articulate college kid that he should journal. And a fortnight later he scheduled again and told me he did journal. He offered to share it. And I read them.
It was all performative.
His journal was rarely a thing he’d have written to himself. He’d written it out as a note to me, as if secretly leaving me crumbs to pick. And I don’t blame him. It’s fine. But what’s the point then?
Hey guys. So I’m a pretty anti AI person in general, which I think is important to note to begin with.
I’ve had the same therapist for six years. She’s helped me through a lot, but our sessions have decreased over the past couple of years because I can’t afford it anymore.
Generally, I can only speak to her if I have a paid appointment, but she generally replies when there’s an emergency.
Last week, my cat passed away, and I entered a really horrible dissociative/ derealization state. I messaged her about this, only for her to reply with a very generic, very obviously AI generated message. My episode kinda got worse after that, because in my already vulnerable state, I felt dismissed and regretted messaging her.
Should I bring this up during our next session, or pretend I didn’t notice?
I recently graduated with a BSc in Computer Science, but over the past year I've realized I'm much more interested in psychology and becoming a therapist.
I'm based in Pune, India, and I'm trying to figure out if this career switch is realistic. From what I've read, not all universities accept non-psychology graduates for a master's, which has me a bit confused.
I have a few questions:
Has anyone here switched from a completely different field into psychology?
Which colleges in or around Pune accept students from non-psychology backgrounds?
How's the job market for therapists/counseling psychologists in India right now?
Is the pay enough to make a stable career, or is it something that takes many years to build?
I'd really appreciate hearing from people who have actually gone through this or are currently working in the field. I want honest opinions—the good, the bad, and whether you'd do it again.
Thanks in advance!
I'm a 22 year old psychology student. I was a medical aspirant and gave 3 attempts for neet alongside my college. I didn't make it and took it hard. Before I could even heal from my previous entrance suddenly cuet pg came into picture. My score is 227/300 which isn't bad but won't land me anywhere either. I had applied to various colleges for different courses but I haven't made my mind as to what I genuinely want to do. Earlier I was more into clinical so I missed the deadline for the payment for all the counselling colleges I got into. Now I don't feel like doing clinical at all if it's not rci. I feel so doomed what do I even do.
Loser got bills to pay
After spending a year preparing, I was waitlisted for the IIT Delhi MSc Cognitive Science program. I was first on the waitlist in my category, but the coordinators have now confirmed that admissions are full.
It hurts. I genuinely wanted to pursue academia.
A huge source of mental pressure right now is money. My mother constantly reminds me that life would be easier if I simply started earning. I understand why she says it, but it’s difficult to hear after putting so much into this.
So I’m looking at a few small side hustles—not as a full-time career, just to earn some pocket money and become a little more financially independent while I figure out my next steps.
Some ideas I’m working on:
Reworking and upcycling clothes.
Embroidery, crochet, and handmade designs.
Small art commissions.
Reselling accessories or handmade items.
Occasional tiffin or baking orders.
I’m also applying for a few small government job openings here and there in my state. It isn’t what I genuinely want to do, but bills don’t wait. College applications, entrance exam fees, books, and travel all cost money, and I need a way to pay for them.
My family would rather I stopped trying to study abroad, but I don’t think I’m ready to let that dream go. So while I work and earn what I can, I’ll keep preparing and applying for opportunities abroad next year.
Academia is still the goal. Right now, I’m just trying to find a way to afford the journey.
I’m 20(F) planning to apply for a BSc in Psychology and later pursue a Master’s in Clinical Psychology. I gave NEET twice but have officially given up on that path. My ultimate goal is to work in a hospital or as a university professor. However, I’ve heard many people say that a career in psychology is useless, and now I’m torn. I'm terrified I’ll be jobless after hearing everyone's opinions. Has anyone else made this made this switch ? Am I setting myself up for unemployment or is this a viable career path?
i am in final year of masters and want to start my net preparation by myself. What 2-3 books can I start with?