Psyda is known for its commitment and dedication to groundbreaking research and finding profound insights. Very little do people know how we accomplish our goals. In this post, we invite you to join us for a walk through Psyda’s corridors and research pipelines to discover how we are making a difference.
Data is the mother of all! We at PSYDA undergo various steps to gather data from numerous sources for your project. Our main tools for survey data collection are Qualtrics and Surveymonkey. Upon request, we scrape data using different tools like BeautifulSoup and Scrapy etc to extract the information from various websites and databases into usable data frames. Our scientists are experts in acquiring real-time user-generated and offline SQL data.
For large datasets, WorldBank Open Dataset, WHO, Google Public Datasets, RODA, EU Open Data Portal, FiveThirtyEight, US Census Bureau, UNICEF, Kaggle, UCI Machine Learning Repository, and Data.gov are used. Projects that need research design we use different methods for sample size calculation using Gpower to calculate power, significance levels and effect sizes. This sets the stage for the reliability and validity of the research.
This pipeline is considered one of the most important steps before the feature engineering phase. After all, ‘garbage in, garbage out’ phrase isn’t there without reason. The first step is to handle the missing data using different imputation techniques depending on the datasets. After that, Outliers are detected through the cook’s distance so that extreme values do not skew our results.
To examine each column’s distribution, statistical analysis is conducted by our statistics experts to analyze the skewness and kurtosis and apply log transformations, min/max scaling and various other techniques to the data if needed. We then determine the heteroscedasticity to find the variance of data along the axis. Means and Standard Deviations are calculated to study the distribution of data. We use multiple approaches to study the multicollinearity of the features to determine parametric vs non-parametric techniques to be applied.
EDA provides important patterns in the data. Our data scientists use numerous techniques and statistical approaches to find the structure and correlation of the data to develop a theory. It plays a vital role in preparing the data for structural equation modelling. A great challenge for any scientist is to find irrelevant and problematic independent variables in the dataset and through our smart algorithms, we never fail to ace it.
By doing confirmatory factor analysis we confirm the validity of an already postulated theory using different software and packages. Some of them are SmartPLS 3, SPSS, Power BI, IBM Watson, R packages, Lavaan, Psych and semTools, AMOS, LISREL, and MPLUS. We go through various steps in measurement model invariance to validate the factor structure across different groups. We ensure that there is no built-in bias against a group in the dataset during this phase.
SEM is a strong substitute over linear regression for different techniques like time series analysis and path analysis. At this stage, our skilled scientists measure and study different models of nonlinearities, measurement error, correlated independents and correlated error terms by different indicators.
Furthermore, Moderation and Mediation in SEM confirm or rejects the relationships in the hypothesis. We put great effort in designing and updating these methodologies and keeping the latest research into consideration. We do consummate path analysis to determine the best course of action and best path.
We at PSYDA are adamant about our focus on impeccable datasets for reliable predictions. Our scientists identify the curse of dimensionality in datasets to analyze and classify highly dimensional data to study the sparsity in space. After applying several techniques, we filter the most relevant features to train the model accurately.
In this phase, we select the best performing algorithm for the processed data. We split the data into multiple parts in different ratios to determine the accuracy of the machine learning models using different techniques like bootstrapping and K-fold cross-validation.
After model training, we continue tuning the model in order to achieve the highest possible accuracy. In this pipeline, we use several techniques e.g. GridSearchCV and Randomized Search CV. It is a technical step to extrapolate which of the machine learning algorithms fits the best. Our use of Neural Network libraries like TensorFlow, Keras, Pytorch and NLTK boosts the accuracy of your data tremendously.
Our talented data scientists collaborate with our wordsmith writers to build a narrative from the output of machine learning algorithms. Instead of using confusion matrix, epoch and bias, we convert these ideas into a story that takes your readers on a riveting journey. Our description of your results creates a logical and compelling narrative for the readers.
It doesn’t end here. We create interactive dashboards for your readers to play with. They can create insights from your data in realtime using easy to navigate interfaces. We use several approaches to create pickle files and deploy them through Flask and Django apps, Plotly dash, Shiny dashboards, and mobile apps. Let the world see your work.
At this final step, our creative research writers and data scientists collaborate to generate policy papers, reports, and case studies with stunning visualizations of your data. We help you prepare immersive presentations as well as ground-breaking research papers with a proven record of success.