#Spss 16 help how to

Assumption #2: There needs to be a linear relationship between all variables.

Examples of ordinal variables commonly used in PCA include a wide range o f Likert scales (e.g., a 7-point scale from 'strongly agree' through to 'strongly disagree' a 5-point scale from 'never' to 'always' a 7-point scale from 'not at all' to 'very much' a 5-point scale from 'not important' to 'extremely important'). Examples of continuous variables (i.e., ratio or interval variables) include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth. Assumption #1: You have multiple variables that should be measured at the continuous level (although ordinal variables are very frequently used).First, letвЂ™s take a look at these four assumptions: However, even when your data fails certain assumptions, there is often a solution to try and overcome this. This is not uncommon when working with real-world data rather than textbook examples. In practice, checking for these assumptions requires you to use SPSS Statistics to carry out a few more tests, as well as think a little bit more about your data, but it is not a difficult task.Д«efore we introduce you to these four assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., not met). You need to do this because it is only appropriate to use PCA if your data 'passes' four assumptions that are required for PCA to give you a valid result. When you choose to analyse your data using PCA, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using PCA. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for PCA to give you a valid result.

#Spss 16 help how to

In this 'quick start' guide, we show you how to carry out PCA using SPSS Statistics, as well as the steps you'll need to go through to interpret the results from this test. It is also worth noting that whilst PCA is conceptually different to factor analysis, in practice it is often used interchangeably with factor analysis, and is included within the 'Factor procedure' in SPSS Statistics. These are just some of the common uses of PCA. This helps you understand whether some of the variables you have chosen are not sufficiently representative of the construct you are interested in, and should be removed from your new measurement scale (c) you want to test whether an existing measurement scale (e.g., a questionnaire) can be shortened to include fewer items (e.g., questions/statements), perhaps because such items may be superfluous (i.e., more than one item may be measuring the same construct) and/or there may be the desire to create a measurement scale that is more likely to be completed (i.e., response rates tend to be higher in shorter questionnaires). Therefore, you test whether the construct you are measuring 'loads' onto all (or just some) of your variables. If these variables are highly correlated, you might want to include only those variables in your measurement scale (e.g., your questionnaire) that you feel most closely represent the construct, removing the others (b) you want to create a new measurement scale (e.g., a questionnaire), but are unsure whether all the variables you have included measure the construct you are interested in (e.g., depression).

There are a number of common uses for PCA: (a) you have measured many variables (e.g., 7-8 variables, represented as 7-8 questions/statements in a questionnaire) and you believe that some of the variables are measuring the same underlying construct (e.g., depression). Its aim is to reduce a larger set of variables into a smaller set of 'artificial' variables, called 'principal components', which account for most of the variance in the original variables. Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis.

Principal Components Analysis (PCA) using SPSS Statistics Introduction