Dependent variable: frequency of drinking (a28 and b28). whether the relationship between the within factor (time) and frequency of drinking is linear, quadratic, or cubic. whether there is a significant change on frequency of drinking over time (3 months) before and after treatment 2. Violation is serious Multivariate normality Fairly robust against violation Sphericity Not necessary for the multivariate approach The variance-covariance matrices are the same across the cells formed by the between-subjects effects.Ī simplest design One within-subjects factor One dependent variable A group of subjects measured at different points in timeĮxample: sample is from high school students. Within factors: is the one with multiple measures from a group of people such as time.ĥ Repeated measures Assumptions Independence of the observations Interested in the performance trends over time: is it linear, quadratic, or cubic?Ĥ GLM Repeated Measures Between and within factorsīetween factors: a grouping or classification variables such as sex, age, grade levels, treatment conditions etc. Comparing the same subjects under several different treatments.
One-way within-subjects ANOVA with SPSS One between and one within mixed design with SPSS Repeated measures MANOVA with SPSS How to interpret SPSS outputs How to report resultsģ GLM Repeated Measures When the same measurement is made several times on each subject or case, such as Same group of people are pretested and post-tested on a dependent variable.
PS I don't think I have time to learn how to use another program with regards to my deadline, so if it can be done in SPSS that would be best for me.1 SPSS Series 3: Repeated Measures ANOVA and MANOVAīy Hui Bian Office for Faculty ExcellenceĢ List of topics Repeated measures ANOVA with SPSS Lastly, I am a fairly inexperienced SPSS user, so I would ask that any explanations be extremely simple. Is a factorial MANOVA the way to go? If so, please explain how. This tells me that SPSS can do what I need it to, but I have no idea how to do this.Ĭould someone explain to me a) how I could calculate utilities, allowing for interaction between two of the independent variables, and b) how I can generate significance tests for these conjoint utilities? MANOVA outputs seem to give a lot of data on significance and also allow for interactions. Looking into how the CONJOINT PLAN syntax works, I found out it uses a (two-way/factorial) MANOVA. It doesn't show statistical significance tests for the respondents' utilities. It doesn't allow for interaction between independent variables, something that is key to my research.
This worked nicely, but leaves me with two problems: I have used SPSS's CONJOINT PLAN syntax like this:ĬONJOINT PLAN='C.sav' (plan file with card numbers and corresponding attribute levels) In both experiments, the 20 cards encompassed 4 holdouts. Participants rated 20 'cards' with information on multiple variables presented on each card.
Full factorial plans would result in 20 respectively 96 cards, but I used a fractional factorial plan.īoth experiments use rating (SCORE) data from 7 point Likert scales. I'm using full profile conjoint analysis for my master dissertation and have encountered some problems regarding interaction of independent variables and statistical significance tests in SPSS.Ībout my research: I conducted two experiments, one with 3 attributes (respectively 2, 2 and 5 levels) and one with six attributes (respectively 2, 2, 2, 2, 2, and 3 levels). R-bloggers - blog aggregator with statistics articles generally done with R software.
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