Toolkit for Quantitative Surveys

• SPSS Statistics is a software package used for statistical analysis. ( Wikipedia )

• Statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation. ( Wikipedia )

• In statistical inference of observed data of a scientific experiment, the null hypothesis refers to a general or default position: that there is no relationship between two measured phenomena. ( Wikipedia )

• A statistical hypothesis test is a method of statistical inference using data from a scientific study. In statistics, a result is called statistically significant if it has been predicted as unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. ( Wikipedia )

• Cross tabulation (or crosstabs for short) is a statistical process that summarizes categorical data to create a contingency table.

• A crosstab is another name for a contingency table, which is a type of table created by crosstabulation. In survey research (e.g., polling, market research), a "crosstab" is any table showing summary statistics. Commonly, crosstabs in survey research are concatenations of multiple different tables. For example, the crosstab below combines multiple contingency tables and tables of averages. ( Wikipedia )

• A scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. ( Wikipedia )

• Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and often denoted by the Greek letter rho is a nonparametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other. ( Wikipedia )

• The Pearson product-moment correlation coefficient (sometimes referred to as the PPMCC or PCC, or Pearson's r) is a measure of the linear correlation (dependence) between two variables X and Y, giving a value between +1 and −1 inclusive, where 1 is total positive correlation, 0 is no correlation, and −1 is total negative correlation. It is widely used in the sciences as a measure of the degree of linear dependence between two variables. It was developed by Karl Pearson from a related idea introduced by Francis Galton in the 1880s.