SPSS

The posts here follow the order you actually meet the procedures in: entering and cleaning the data, coding and reverse-coding items, descriptive statistics, Cronbach's alpha, exploratory factor analysis, Pearson correlation, linear regression, then the difference tests, independent samples t-test and one-way ANOVA. Each post stops at one procedure. It gives the menu path, walks through every table the procedure prints, and states the cut-off it uses together with the source that cut-off comes from, because a threshold with no citation is the first thing a reader asks about.

A good share of the posts deal with things going wrong, since that is where the time goes. An item that has to be dropped because its factor loading sits under the acceptable value. A rotated component matrix that will not settle into the structure your model assumed. A significance value above 0.05 on the one hypothesis the whole study was built around. Those are the points a supervisor asks about at the defence, and they are also the points a paid analysis service tends to leave undocumented. The aim is that you can run the analysis yourself, explain the output, and not pay for a table of numbers you cannot defend.

If your model has mediators, moderators, or more latent constructs than a regression will carry, read the SmartPLS category as well. If you do not have data yet, the Surveys category covers sample size and instrument design, which are decisions no amount of work in SPSS will fix afterwards.

Where to start. If your data is entered but nothing has been run, read the post on cleaning and coding first, then descriptive statistics, which gives you the sample profile table your results chapter opens with. After that, follow the real run order: Cronbach's alpha, EFA, correlation, regression. Read the post on the Coefficients table and the one on what a significance value does and does not tell you before writing a single sentence about a hypothesis. The troubleshooting posts are worth reading when you hit the specific problem they describe, and not before.