IBM SPSS Statistics

It is a perfect tool for any type of dataset analysis that I need for my research project and works flawlessly when it comes to survey-related tasks. My supervised research student also used it for their survey-based research work efficiently and they got an outstanding result using IBM SPSS Statistics as well.

IBM SPSS Statistics is a great product on effective predictive analytics generation and to coordinate with other projects members via the platform is amazing. The ability to process a huge volume of different data and data cleansing functions are very useful and on my implementation of IBM SPSS Statistics the operation was easyand successful.

thanks to the fact that IBM® SPSS® Statistics is an incredible statistical software platform, we can make decisions in real time in our company. Its use is very intuitive and the price is relatively economical. Our main problem before acquiring the software was the large amount of data we had and that we did not take advantage of for decision making.

I used IBM SPSS Statistics to run a variety of statistical tests, both inferential and descriptive, for my research work.

Correlations and regressions are just two important aspects of IBM SPSS Statistics commonly used by my supervised students in their thesis papers. I quickly find insights and produce useful recommendations with the help of IBM SPSS Statistics data analysis tools.

IBM SPSS Statistics enables my organisation to address a range of data analytics challenges. We frequently use it for interrogating datasets, generating descriptive statistics, performing a range of hypothesis tests, and creating data visualisations. In my business area we tend to use it for regression modelling, particularly using the multiple linear regression and binary logistic regression approaches.

I use SPSS to analyze survey data for my org. My analyses range from simple descriptive statistics to correlations and complex models. I'm able to compare means, run regression models and create graphs to understand relationships between variables.

Run regression analysis on small and large data sets with adjustable CI. I have been using this for many years, and the modeling has been helpful in the predictors; yes, the output is basic but that is what makes this nice and clean, and it can be imported to reports easily and presented well on a slide deck.

I use IBM SPSS Statistics mainly for analysis and prediction based on my existing data for improving user experience, analysing software crashes and predicting next targets for sales and acquisitions. While I used common analytics tools like Microsoft Clarity, Google Analytics, Firebase Analytics, etc. but they just do it as basic level. My requirements were to dive deeper on those data, and wanted to derive outcomes using various methodologies. Upon researching, I found out about this, and decided to give it a try. And till now, it works perfectly for me.

I learned IBM SPSS as part of my course in college and then went on to do an internship (through the college) where I had to use this tool. We used it for market research and customer analysis, to put it broadly. It helped us to forecast trends. While my experience with SPSS was limited, I know my colleagues who use it to execute far more complex models, and they all highly recommend it.

IBM SPSS Statistics is used in working with undergrad students that I teach to have them apply their skills in research-based psychology courses. IBM SPSS Statistics is a good way to get students started with intimidating statistical analysis of data using a GUI system (point-and-click) that is more straightforward than more advanced analytical tools.

We use SPSS for all our data and statistical analysis needs. Most of our critical analysis and reporting systems are driven by SPSS programs. We also use SPSS for ad-hoc modeling and data requests. In addition, we are able to automate our systems which improves the data integrity and speed of our work.

We use IBM SPSS to view and analyze raw survey data. It's an integral tool for quantitative data analysis. It allows us to see the data simultaneously in its numeric code form and the data labels, whereas looking at raw data in [Microsoft] Excel you must choose one or the other. It allows you to run a quick analysis and easily set up the data for a more robust and user-friendly analysis in other research software.

SPSS is used as a tool for complex data cuts, data modeling, and regressions mainly. One of the primary uses is for running TURF analysis for a few clients that we run those kinds of projects for. It is also used to check our data against our online platform reports that we generate.

I have IBM SPSS Statistics for analytical purposes, where I analyze the market performance. How the market has moved and how each brand has performed month on month and periodically. Through this, we measure the market movement and contributions of each brand, which gives insights to find the pattern and plan accordingly.

We use IBM SPSS Statistics for analyzing data from various research projects in the field of HR, OD (organizational development) and UX (ranging from user profiling to brand perception).
The primary purpose of using SPSS is to analyze and interpret complex data sets, to then employ an evidence-based decision-making for our studies and studies performed for our clients which include big corporations as well as governmental institutions.

IBM SPSS Statistics addresses critical business problems for my UX research team, providing helpful capabilities for analyzing quantitative data from user research studies. SPSS allows us to process and interpret large datasets, translating insights into user behavior, preferences, and trends. It allows us to make data-driven decisions to enhance our products. Our use case encompasses various stages of the user research/design process, including survey design, data collection, and analysis. SPSS enables us to efficiently clean data, perform statistical analyses, and generate clear and insightful visualizations to communicate findings effectively to stakeholders. Leveraging SPSS, we can streamline our research workflow, accelerate the pace of insights generation, and ensure that our design decisions are validated by data.

I use IBM SPSS Statistics for research purposes. I run data analyses such as regression, t-tests, and ANOVA. I added a PROCESS module to run additional analyses in SPSS, such as moderated mediation analysis. In addition, I added the SPSS AMOS to run structural equation modeling (SEM) analysis to test relationships between latent and observed variables.

We use it for statistical analysis. it helped us to analyze and interpret data. With predictive analytics, data visualization, market and survey research, it helped our organisation to analyze, survey data, assess trends, and draw helpul results. With this our organisation has a major success and ROI. It gives a biger picture of your data and that helps you to grow

IBM SPSS Statistics was chosen to reduce the time data analytics is completed. The results has allowed us to use predictive analysis allowing teams to work more efficiently to solve issues. IBM SPSS Statistics is solid when working on complex analysis allowing for data to saved for validation testing or examples on the ability of IBM SPSS Statistics.

Analyzing large datasets from surveys we've conducted on all students. Crosstabs, frequency tables, and exploratory data.

At our institution we use IBM SPSS Statistics to support classroom learning and for insights and data analysis that support administrative decision making. In the classroom we use IBM SPSS Statistics in courses as varied as MAT 221 Probability and Statistics for the Behavioral Sciences and a variety of applied research classes within the psychology department. In addition, IBM SPSS Statistics is used frequently to support complex data analysis for both undergraduate honors and graduate business courses and research.

I am a currently an independent consultant with a contract with a nonprofit organization where I had worked for almost 30 years as a researcher or as an IT director. I also have 10 years experience as an Institutional Research Director at a large urban community college, from which I retired 4 years ago.

I work in a research project which is based on the clinical trial (RCT) in public health. This project collects data of various trial participants, especially men and women. The data includes various socio-demographic information, such as; age, gender, marital status, work status, place of residence (rural/urban), city, state and components of wealth index. In addition to socio-demographic information, the data also includes information on bio-chemical characteristics of the participants. I use IBM SPSS Statistics in order to clean, edit, labelling of variables and data analytics. Pros of IBM SPSS Statistics · Easy to handle without specific training · The visualization of data through various statistical graphs (such as; bar-diagram, pie-chart, scatter plot and other graphs) is easier . Testing of various statistical hypothesis is easier using mouse based options · The software also provides options to check and remove missing values · One can also do predictive modeling using IBM SPSS Statistics such as; linear regression, logistics regression, multinomial regression Cons of IBM SPSS Statistics · mostly works on in-built functions within the software, so an advanced analysis, such as; multivariate decomposition, non-linear decompositions and other advance analytics aren’t feasible · Some machine learning aspects, such as density based spatial clustering of applications with noise, moral plots, LISA cluster mapping etc., cannot be produced using IBM SPSS Statistics

[IBM SPSS] is used by researchers in our departments to prepare statistical analyses for publication. We take theoretical ideas and principles, collect the data beforehand that applies and uses said ideas, then use [IBM] SPSS to answer the questions using statistics as the foundation for publishable articles. In other words, it allows us to answer difficult questions in a comprehensive manner.

IBM SPSS modded is used in building a forecasting model and a customer churn propensity model. Both were originally built 5 years ago and have been used on and off. Ideally, the same models would be easily rebuilt in Python or R but [the] modeler offers efficiencies better than other tools. This could also be because the [IBM] SPSS models were built by someone with a lot of experience [with] the tool.