Quasi-experimental research designs have partial control over independent variables, do not use randomized populations, and are categorical in nature. They have advantages of being more natural, provoking fewer concerns about external validity, and being more resource-efficient and realistic, but have disadvantages of lower internal validity, less manipulation, and increased confounding variables. The main types are person-by-treatment quasi-experiments, which maintain partial control and use random assignment in laboratories, and natural experiments, which do not use true random assignment but instead utilize phenomena for comparison.
This presentation is for educational purpose only. I do not own the rights to written material or pictures or illustrations used.
This is being uploaded for students who are in search of, or trying to understand how a quasi-experimental research design should look like.
This presentation is for educational purpose only. I do not own the rights to written material or pictures or illustrations used.
This is being uploaded for students who are in search of, or trying to understand how a quasi-experimental research design should look like.
Variables: Types and their Operational Definitions
Unit III: Problem identification formulation of research objectives and hypothesis (as part of M.Optom Curriculum of Pokhara University, Nepal)
Variables: Types and their Operational Definitions
Unit III: Problem identification formulation of research objectives and hypothesis (as part of M.Optom Curriculum of Pokhara University, Nepal)
Research Methods in Psychology | Cambridge AS Level | Cambridge Assessment In...Abhinav Gaur Kaptaan
This PowerPoint presentation, titled "Research Methods in Psychology for Cambridge AS Level Students," provides a comprehensive overview of essential research methodologies in psychology. It covers fundamental concepts such as experimental, correlational, and observational methods, highlighting their advantages and limitations. The presentation delves into the design of experiments, including independent and dependent variables, control groups, and random assignment. It also addresses ethical considerations, data collection techniques, and statistical analysis. Emphasizing practical application, the presentation includes examples of classic psychological studies and offers tips for designing and conducting research projects. It concludes with a discussion on interpreting results and the importance of critical evaluation, preparing students for both theoretical understanding and practical application in their AS Level psychology coursework.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
