What are the statistical analysis tools that can be used to analyze survey data?

Atac (automated tactical analysis of crime) by BAIR Analytics - now LexisNexis Used to analyze temporal, spatial, statistical and behavioral data. Unlike any other software for analysis. Used by largest corporations in the world as well as thousands of municipalities and the DOD.

Descriptive statistics measures are simple yet powerful. But the main point is knowing your data first. Then you can apply the correct measures. Take this as an example: Datasets A and B each consist of 1000 salaries of college grads in Jacksonville. Dataset A has a min of $25000 and a max of $110,000. Dataset B has a min of $23000 and a max of $600,000. But the majority in both datasets earn between $30000 to $85000. Now the median is a better representative for the dataset B than the average.

Speaking from experience, dtale package in python is an exceptional tool for descriptive statistics. with great features like charts and predictive power analysis it is my first choice to go in Descriptive!.

First of all, reliability of the survey itself must be analysed. Very important to check the reliability of the survey questionnaire specially when it consisted of Likerte scale. Here I advice the data analysit to check it and know if it represent Likrete scale or Likerte type. Knowing this will help to large extent which statistical mesearments should be used. For Likerte scale. Mean and standard deviation are both fine while for Likrete type Median is the choice. Then Box Plot is one of the powerful tool to represent the data distribution along with Histograms. Statistical analysis in not matter of data representation only, it is a matter of feeling data, understanding data, and sensing data. Dr. Eihab Dawi

Many statistical tools are available for analysing small or big data. 1. Measures like mean, median, mode, standard deviation, and variance help summarize and describe the main features of a dataset. 2. Inferential statistics: Tools such as t-tests, chi-square tests, ANOVA (Analysis of Variance), regression analysis, and correlation analysis are used to make inferences or predictions about a population based on a sample. 3. Data visualization: Graphs and charts like histograms, box plots, scatter plots, and pie charts

In order to test if two samples come from the same probability distribution or if one sample comes from a certain probability distribution, you can use the Kolmogorov–Smirnov test.

I agree with checking assumptions before deciding whether to use parametric or non-parametric tests for inferential statistics. Some of the assumptions include testing for outliers, normality, and multicollinearity. Depending on the results of the test, you can then choose to use an ANOVA for example as opposed to a Mann-Whitney or vice versa.

Inference statistics are use when we draw a conclusion true but unknown population parameter on the basis of sample data. Inference statistics are play crucial role for taking the intelligence decisions when we have not possible to collect all the data of population, in this scenario we use inference statistics. In addition that drawing the conclusion about the population on the basis of sample we recommend use parametric test like t test,Z test,F test these test base of the assumption that data must be follow normally distribution. When the data not following normally distribution we use transformation like square root, log etc. After the transformation the data not follow up the normally distribution then we move on non parametric test.

Factor Analysis is powerful for surveys but has important traps to know about. Here are the top 5 to watch out for: 1. It's based on correlations and requires a lot of data. 2. It's best for capturing things that are hard to observe directly and so best measured with multiple questions (e.g., personality traits). 3. There are many hyperparameters and settings, and so easy to make hidden mistakes if you don't know what you're doing. 4. Unlike dimension reduction techniques like PCA, the factors produced can be correlated. 5. Measures of model fit are many and complex. It can be hard to know what to optimize for and what constitutes as good.

Factor analysis is very good tool for reduction and grouping of factors based on data collected and only in few cases we are seeing different factors are grouped in one group based on statistical value.

EFA is a heuristic and explorative method. It usually requires lots of data, but can also work with smaller datasets when communalities are high and loadings quite clear. There are several common mistakes in practice: 1. The determinant is rarely considered when checking the adequacy of data for EFA 2. Often maximum likelihood method is applied without checking for multivariate normality distribution 3. Varimax rotation is used although factors will be correlated and an orthogonal rotation is needed 4. The decision for optional number of factors is either based on screeplot or Kaiser criterion and should be accompanied by parallel analysis 5. Thresholds for loadings and communalities or acceptable crossloadings differ in the literature

LLMs make text analysis much easier and faster than before. I’ve used the OpenAI API to quickly pull topics and sentiments out of open ended survey responses. Then you can aggregate the topics to quickly see the top ideas from the text based survey responses.

Ordinal logistic regression (aka proportional-odds model) and it's partial brother (relaxing the need for proportional odds) are the classic methods for dealing with surveys, questionnaires and especially the special case - Likert-type items (Likert scales can be mostly analysed parametrically). These tools can be easily extended for responses repeated over time (longitudinal studies) by employing either generalized linear mixed models or the GEE (Generalized Estimating Equations). It's worth noting, that the Mann-Whitney(-Wilcoxon) test is a special case of the OLR. For questionnaires with responses being *equidistant* (a unit must be defined for subtraction), parametric methods may used too, if only averaging responses *is meaningful*.

Multivariate methods such as correspondence analysis or multiple correspondence analysis prove useful in the analysis of survey data (categorical data). They allow visual mapping of the relationships/associations in categorical variables. Multivariate methods are less sensitive to small counts as opposed to chi-square test. Preference mapping may also be used to map a set of liking scores provided by consumers on a set of products.

The hidden but vital contribution of statistical thinking to a successful survey is in writing the questions and arranging them in a design that allows for a sensible analysis. If you don't have that, no advanced statistical methods can save you at a later stage

The first step is to ask a well-formulated question. 1. Why do I want to know? 2. What is the population I want to know about? 3. Which data will help me to address my question?

We can use cross tabulation to analyzing relationships between two or more categorical variables to find patterns or dependencies, ANOVA for comparing means across multiple groups to identify differences in survey responses. We also use Chi-Square Test to assessing the association between categorical variables. T-Tests or Z-Tests can also use for comparing means of two groups to determine if there is a significant difference between them. For analyzing the duration until an event of interest occurs, often used in longitudinal surveys, called survival analysis.