The literal meaning of normal is average. In psychological studies, sociological studies, and didactics, those who can reach a particular fixed level in qualification and characteristics are termed normal. At the same time, there who are above or below this point are abnormal. Nature has been kind enough to distribute quite equally most attributes like weight, height, intelligence, and the like. As a result, most of us possess average weight, height, intelligence, and such attributes. Quite a few people deviate noticeably from average, either above or below. This holds equally for information gathered through exams, surveys, and experiments in psychology, ... Read More

Item response theory (IRT), also referred to as latent trait theory, strong true score theory, or modern mental test theory, is a paradigm used in psychometrics for test questionnaires and other similar instruments: design, evaluation, and scoring used to measure skills, attitudes, or other variables. What is Item Response Theory? Before 1950, the idea of an item response function existed. The 1950s and 1960s saw the development of item response theory as theory. The psychometrician for the Educational Testing Service, Frederic M. Lord, George Rasch, and the Austrian sociologist Paul Lazarsfeld were three pioneers who conducted the parallel study separately. ... Read More

For standard meaning, a common benchmark, and comparability, raw scores are frequently converted into standard scores. Such transformations can be linear or non-linear. The zero point of the scale and the unit of measurement for the scores are both subject to linear transformations. These alter the mean and standard deviation of the raw scores, but the differences between the transformed scores correspond closely to those between the respective raw scores in relative magnitude. So, the original shape, skewness, and kurtosis of the distribution of raw scores remain unaltered in that of the transformed scores. What is the Z-Score? The simplest ... Read More

One way to understand the p-value is to consider the likelihood of discovering the difference observed in a random sample or a more extreme one in a population where the null hypothesis is true. Because hypothesis testing is so common, p values are included in almost all quantitative research papers. Before getting to the details, let us briefly talk about hypothesis testing. Hypothesis Testing Research hypotheses are predictions about the outcomes of a study based on logic from a theory or prior research. The best theories are definite and explicit, allowing them to be proved or disproven without any room ... Read More

When a researcher wants to understand if a particular therapeutic technique will work on people, will they apply it to everyone on this planet? Will it be extremely time-consuming and tedious to do so? Similarly, a market researcher who wants to understand if a particular marketing strategy works or not does not practically apply the strategy to all markets to observe the technique's effects. How do we ensure a method's workings or impact without applying it to every target audience? The field of statistics has made it relatively easy for humans to achieve the task. Statistics is a science that ... Read More

The selection Model is mainly taken into account when scores for one aspect of the sample may be affected by a nonrandom selection process. A two-stage regression analysis is used to estimate parameters and standard errors unbiasedly. Women's salaries are an example of data that could be useful for this method: Some aspects of the data could be modeled using standard regression methods, but the preponderance of zero salaries (reflecting some women's decision not to work) would also need to be considered. What is Selection Model? The selection model can be stated as selecting a statistical model from a set ... Read More

When conducting research, an important step is to ensure that the sample of the population is selected randomly and is representative of the entire population. However, the population is often very large. Therefore, statistics employ sampling techniques so that a reliable sample can be chosen. However, how do we eliminate variability from the sample? What is it called if we have picked more than one sample from a population to study different statistics? This article aims to explore these questions and others. What is Sampling Distribution? Statistics employs various methods when it comes to sampling. The main aim of the ... Read More

Let us pretend one is a sales manager tasked with forecasting the next quarter's results. One is well aware of the tens, if not dozens, of varyings that may affect the results, such as the temperature, a supplier's promo, or the rumor of a bigger and more innovative design. Individuals in one's company may have an idea of what had the greatest impact on sales. Meaning of Regression Analysis Using the graph equation from regression modeling, one may extrapolate information from one's data. For instance, if one's weight has been steadily increasing over the past few years, the calculator may ... Read More

When a researcher utilizes three or more statistical samples, an ANOVA test is used to determine the mean and statistical significance of the three. When three or more groups are separately sampled, and their means are determined, an ANOVA test can be used to determine whether or not there is a significant difference. ANOVA attempts to determine the significance of their means. However, how does the researcher determine which of the three pairs has a statistically significant difference? This article will look into it. What are Post Hoc Tests? Post hoc tests are extremely crucial when using ANOVA tests. When ... Read More

Analysis of Variance (ANOVA) is a powerful test for statistical significance, and it is used when we need to compare the difference of means of more than two samples. The basic purpose of ANOVA is to test the homogeneity of several means. N-way Analysis of Variance In ANOVA, the variation in the observed values is assumed to be due to the different levels of the independent variables, with random error accounting for the remaining variation. Data categorized according to multiple independent variables can be examined using an N-way (or factorial) Analysis of Variance. For instance, a two-way ANOVA (two-factor ANOVA) ... Read More