Perfectionism - Science topic

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Regression doesn’t require linearity in the sense that it requires linear x-y relationships. It requires that the y variable can be modelled as a linear function of predictors. This permits curvilinear x-y relationships to be modelled - for example through interactions. You can also include polynomials x + x^2 etc. as well as transformations of x and y.

For a relationship between x and y that is curvilinear a transformation or polynomial might capture this. For further looking at interactions with a polynomial it gets trickier but the main options are polynomial regression and GAMs.

For polynomials see

To conduct moderator analysis on a Structural Equation Model, you will need to use a specialized program such as the supplemental AMOS module in SPSSS. If instead, you want to stay with ordinary regression, then you need to compute scale scores for your core independent and dependent variables. Then you would use standard procedures for adding interaction effects to your regression model.

It is not necessary (and often not desirable) to have your demographic variables coded as categories. For variables that are inherently categorical, such as gender, this makes senses. For continuous variables such as age, education, income etc. you can recode each of them to the mid-point of the separate categories to recapture their continuous nature.

1. participants will have more negative behavioural intentions and attitudes towards children in the chaotic environment than in the calm environment.

--- because there were only two conditions (levels of your factor), you can use a paired t-test (or wilcoxon if nonparametric) to compare the behavioral intentions/attitudes between the calm and chaotic environment where the same participants were subjected to both environments.

2. these differences (highlighted above) will be magnified in participants high in child-oriented perfectionism compared to participants low in child oriented perfectionism.

--- indeed this is a simple linear regression (not multiple one), you can start with creating a new dependent variable (y) as the difference in behavioral intentions/attitudes between the calm and chaotic environment, then you run a regression on the independent variable of a perfectionism score (x).

yes, the moon is not always full. Up and down in life coexists.

IMO the analysis doesn't support adding the covariant at the present time. Best wishes David Booth

Strongly disagree Disagree Neutral Agree Strongly agree

all items seem to be coded like this: 1 2 3 4 5

Sounds like a worthy project, but can you clarify what you mean by "I want to take this a step further than just showing the similarities to looking at the proximity of one to the other"? I don't see how you would distinguish significantly between similarity of ideas and proximity of ideas unless by "proximity" you literally intend a geographical distance that enabled influence, yet I don't think that's your intention either.

You can use this questionnaire to measure perfectionism.

Thank you dear Kais Kara for your discussion

The monk in the movie The Impenetrable Monk says:

Well, an ANOVA would be appropriate if you are comparing groups, and many stats programs will include an interaction effect (but so would regression). Regardless, it appears you are interested first in how perfectionism and self-esteem relate to your attribution outcome. Second, you are interested in whether the strength of these associations depend on level of self-esteem and perfectionism - which is an interaction effect.

I tend to use Mplus, but assuming you are using a platform like SPSS, a regression approach would look like this: Fist, create your interaction term. Do this by going to Transform at the top of the screen, then create a product term (simply multiply self-esteem with perfectionism variable - you can center them if you wish but isn't always required. This will create a new variable for you. Then, go to analyses, then linear regression. Enter your attribution variable as outcome, and self-esteem, perfectionism, and new interaction term as IVs. You can include covariates in the Covariate box if you wish to control for them as well. The output will show you whether the interaction was significant after controlling for the main effects of IVs and covariates. If interaction variable is significant, you will interpret that instead of the main effects.

Hope that helps.

Well, my point is that at the end if i want to come up with results in form of as an example: good/ bad or poor-moderate and excellant. In this case i guess it would be better if those estimates were based on quantitative scores, right? So can i formulate this scores for each question or variable?

Dear Rachel,

good luck!

Béatrice

S. Béatrice Marianne Ewalds-Kvist thank you for the feedback. I conducted the standard multiple regression and found the same result - that maladaptive perfectionism is a significant predictor of stress but avoidant coping is not. I will report the findings as you suggested. I appreciate your time and consideration.

Ok that makes sense. Thank you very much for your response

Hi Sidney,

if you plan to create a composite anyway than I don't think that you need a CFA beforehand, as you move from "measurement of a (or two) latent variables" to a composite/aggregate construct. It may be, however, that future reviewers will see this differently.

An alternative would be to stick with the original factors and hypothesize effects of the original perfectionism facets. This requires you to think about whether the facets of unique effects, or whether they represent an underlying second-order factor. An aggregate/composites avoids these questions. You get "an effect" without learning something important of the underlying mechanism.

Best,

Holger

For assesing reliabilty of the scales it's sufficient number of participants. But to get valid results " on the effect perfectionism and athletic coping skills have on injury occurrence and frequency" I think it's not enough, especially to assess injury frequency. You may use effect sizes indexes which take into account the sample size to assess your results. The choice of effect size index depends on the statistical criterion used in the study

Dragendorff"s (potassium iodide-bismuth nitrate) solution is very cheap, Just prepare small volumes and keep them in tightly closed glass flask in the dark.

Perfectionism - low self-esteem - insecure attachment - jealosy.... I imagine it's a complex relationship. Interesting though.

I can't help you. Sorry. Good luck

Dear Gabriel, you may be interested in the following article:

Bateson, John EG, and Michael K. Hui. "The ecological validity of photographic slides and videotapes in simulating the service setting."Journal of consumer research 19.2 (1992): 271-281.

Persons embedded in organizations,communities, culture are involved in teleogical scenarios which give content and structure to those organizations,communities, culture.these personal teleogies are influenced by specific contexts organizational,communal, cultural immersion.

thank you for your replies.

Thank You Béatrice. These are wonderful resources and will read through them. 

Hi Rayele,

What data analysis to use also depending on your conceptual framework / research model and their hypotheses.  Once you have decided the data analysis, you can choose the relevant statistical software.  Generally on the surface you can use data analyses like normality test (deciding to use parametric / non-parametric statistics), descriptive statistics, reliability test (Cronbach Alpha / Composite Reliability), Pearson / Spearman correlational test etc.

Based on information you'd provided, looks like is a correlational research. 

1) If e.g. both perfectionism and parenting style are independent variables and academic achievement is dependent variable, then you might use multiple regression analysis in which you can use software like SPSS base-module, R, SAS etc.

2) If e.g. each perfectionism, parenting style & academic achievement includes sub-components of latent constructs, evaluation of the first level and second level orders of Confirmatory Factor Analysis model & testing the prior theory with well-tested survey questionnaire e.g. MPS & PAS etc, you can use Covariance-based Structural Equation Modeling (SEM) software like SPSS AMOS, LISREL etc.

3) If e.g. each perfectionism, parenting style & academic achievement are newly discovered constructs for exploratory research & with small sample size etc, then you can use Variance-based SEM / Partial Least Squares SEM software like SmartPLS, PLS Graph etc.

To differentiate when to use the Covariance-based and Variance-based SEMs, you can refer to this RG Q&A:

4) In case your research is non-correlational but rather is a cause & effect experimental research  with experimental & control groups e.g. by changing parenting style, how this can impact academic achievement etc., may be you need to use data analysis like t-test, one-way ANOVA, two-way ANOVA etc. whereby you can use software like SPSS, R, SAS etc.

Regards,

Fung

I am assuming that you have read the original article in the Journal of Personality and Social Psychology, and the scale was not included in the article. (If not, check that out first!) The scale in question has been copyrighted and commercially published. It is available at http://www.mhs.com/product.aspx?gr=cli&id=overview&prod=mps in either paper or on-line forms. At this time, the publisher sells the Technical Manual for $83; hand-scored forms cost about $2 each and computer administrations $3. You get a small price break on the first batch of hand-scored forms if you buy them along with the Manual.

Thanks for your suggestion Hans. I think engagement is an important variable to consider. I will go deeply on this concept in order to organize my research model!. Thanks again!.