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Psychology AU Amsterdam: Assortmentpointer for summaries and study assistance with the Bachelor and Masters

Psychology AU Amsterdam: Assortmentpointer for summaries and study assistance with the Bachelor and Masters

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Bachelor 1:

  • Introductory Psychology; Brain and Cognition; Research Methods and Statistics; Developmental Psychology; Work and Organisational Psychology; Social Psychology; Clinical Psychology; Neuropsychology; First year thesis

Bachelor 2 Shared Program:

  • Scientific and Statistical Reasoning; Practical training: Psychological Communication; Practical training: Psychological Research; Fundamentals of Psychology

Specialisations:

  • various courses, a.o.: Current Topics: Introduction to Cultural Psychology; Youth Interventions: Theory, Research and Practice; Clinical Skills: Developmental Psychology; Adolescence: Developmental, Clinical and School Psychology; KNP Diagnostiek; Psychotherapy and Therapeutic Skills; Teams in Organisations; Emotion

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Borsboom et al. (2016). Kinds versus continua: a review of psychometric approaches to uncover the structure of psychiatric constructs.

The danger of using a dichotomous system when it comes to mental disorders is not treating people who require treatment or treating people who do not require treatment. It is unclear where the boundary between disorder and no disorder is and this is not progressive for science and research as a whole.

Equivalence classes refers to sets of individuals who are exchangeable with respect to the attribute of interest. Measurement starts with categorization. The continuity hypothesis states that in between any two positions lies a third that can be empirically confirmed (1) and that there are no gaps in the continuum (2).

In a continuous interpretation, the distinction between people that have a disorder and do not have a disorder depends on the imposition of a cut-off score that does not reflect a gap in the inherent attribute itself (e.g. difference between average length

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Borsboom & Cramer (2013). Network analysis: An integrative approach to the structure of psychopathology.

The disease model states that problems are symptoms of a small set of underlying disorders. This explains observable clinical symptoms by a small set of latent variables (e.g. depression). A network is a set of elements (nodes) connected through a set of relations. In network models, disorders are conceptualized as systems of causally connected symptoms rather than effects of a latent disorder.

Mental disorders cannot be identified independently of their symptoms. In medicine, the medical condition can be separated from the symptoms. In psychology, this is not possible. In order to separate this, it must be possible that a person has symptoms without the disorder (e.g. depression without feeling down is not possible). In mental disorders, it is likely that there is symptom-symptom causation. One symptom causes another symptom and this leads to a mental disorder.

With network systems, it might be unclear where

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Discovering statistics using IBM SPSS statistics by Andy Field, fifth edition – Summary chapter 19

It is possible to predict categorical outcome variables, meaning, in which category an entity falls. When looking at categorical variables, frequencies are used. The chi-squared test can be used to see whether there is a relationship between two categorical variables. It is comparing the observed frequencies with the expected frequencies. The chi-squared test standardizes the deviation for each observation and these are added together.

The chi-squared test uses the following formula:

The expected score has the following formula:

The degrees of freedom of the chi-squared distribution are (r-1)(c-1). In order to use the chi-squared distribution with the chi-squared statistic, there is a need for the expected value in each cell to be greater than 5. If this is not the case, then Fisher’s exact test can be used.

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Discovering statistics using IBM SPSS statistics by Andy Field, fifth edition – Summary chapter 18

Factor analysis and principal component analysis (PCA) are techniques for identifying clusters of variables. These techniques have three uses: understanding the structure of a set of variables (1), construct a questionnaire to measure an underlying variable (2) and reduce a dataset to a more manageable size while retaining as much of the original information as possible (3).

Factor analysis attempts to achieve parsimony by explaining the maximum amount of common variance in a correlation matrix using the smallest number of explanatory constructs (latent variables). PCA attempts to explain the maximum amount of total variance in a correlation matrix by transforming the original variables into linear components.

A factor loading refers to the coordinate of a variable along a classification axis (e.g. Pearson correlation between factor and variable). It tells us something about the relative contribution that a variable makes to a factor.

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Discovering statistics using IBM SPSS statistics by Andy Field, fifth edition – Summary chapter 17

A multivariate analysis is used when there is more than one dependent (outcome) variable. It is possible to use several F-tests when there are several dependent variables, but this inflates the type-I error rate. A MANOVA can detect whether groups differ along a combination of dimensions. MANOVA has a greater potential power to detect an effect.

A matrix is a grid of numbers arranged in columns and rows. The values within a matric are called components or elements and the rows and columns are vectors. A square matrix has an equal number of columns and rows. An identity matrix is a matrix where the diagonal numbers are ‘1’ and the non-diagonal numbers are ‘0’. The sum of squares and cross-products (SSCP) matrices are a way of operationalize multivariate versions of the sums of squares. The matrix that represents the systematic variance (model sum of squares) is denoted

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Discovering statistics using IBM SPSS statistics by Andy Field, fifth edition – Summary chapter 14

Factorial designs are used when there are more than one independent variables. There are several factorial designs:

  1. Independent factorial design (between groups)
    There are several independent variables measured using different entities.
  2. Repeated-measured (related) factorial design
    There are several independent variables using the same entities in all conditions.
  3. Mixed design
    There are several independent variables. Some conditions use the same entities and some conditions use different entities.

INDEPENDENT FACTORIAL DESIGNS AND THE LINEAR MODEL
The calculation of factorial designs is similar to that of ANOVA, but the explained variance (between-groups variance) consists of more than one independent variable. The model sum of squares (between-groups variance) consists of the variance due to the first variable, the variance due to the second variable and the variance due to the interaction between the first and the second variable.

It uses

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Discovering statistics using IBM SPSS statistics by Andy Field, fifth edition – Summary chapter 15

Repeated-measures refers to when the same entities (e.g. people) participate in all conditions of an experiment or provide data at multiple points of time.

One of the assumptions of the standard linear model is that residuals are independent, which is not true for repeated-measures designs. The residuals are affected by both between-participant factors and within-participant factors. There are two solutions to this:

  1. Model within-participant variability
  2. Apply additional assumptions to make a simpler, less flexible model fit

One of these assumptions is sphericity (circularity). This assumption states that the relationship between scores in pairs of treatment conditions is similar (e.g. the level of dependence between means is roughly equal). It states that variation within conditions are similar and that no two conditions are any more dependent than any other two. Local sphericity refers to when some conditions do have equal variance and

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Discovering statistics using IBM SPSS statistics by Andy Field, fifth edition – Summary chapter 13

Covariates are characteristics of the participants in an experiment. These are characteristics outside of the actual treatment. If a researcher wants to compare means of multiple groups using the additional predictors, the covariates, then the ANCOVA is used. Examples of covariates could be love for puppies, softness of puppy fur.

Covariates can be included in an ANOVA for two reasons:

  1. Reduce within-group error variance
    The unexplained variance is attributed to other variables, the covariates, which reduces the total error variance. This allows for a more sensitive test for the difference of group means.
  2. Elimination of confounds
    By adding other variables, covariates, in the analysis, confounds are eliminated.

If there are covariates, the b-values represent the differences between the means of each group and the control adjusted for the covariate.

ASSUMPTIONS AND ISSUES WITH ANCOVA
There

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Discovering statistics using IBM SPSS statistics by Andy Field, fifth edition – Summary chapter 12

The overall fit of a linear model is tested using the F-statistic. The F-statistic is used to test whether groups are significantly different and then specific model parameters (the bs) are used to show which groups are different.

The F-statistic gives an associated p-value as well. A p-value which is smaller than 0.05 (or any set alpha) stands for a significant difference between the group means. The downside of the F-test is that it does not tell us which groups are different. Associated t-tests can show which groups are significantly different.

The null hypothesis if the F-statistic is that the group means are equal and the alternative hypothesis is that the group means are not equal. If the null hypothesis is true, then the b-coefficients should be zero. The F-statistic can also be described as the ratio of explained to the unexplained variation.

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Icon Vellutino, Scanlon, Zhang, & Schatschneider (2008). Using response to kindergarten and first grade intervention to identify children at-risk for long-term reading difficulties.” – Article summary
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The IQ-discrepancy definition for problematic readers (e.g. dyslexia) is subpar as they often show average scores on measures of word level skills. Intelligence tests do not differentiate the atypically achieving children from a group of typically achieving children when it comes to reading performa...

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Hulp nodig bij Statistiek en Onderzoeksmethoden? Check out de Methodologiewinkel van de Universiteit van Amsterdam! Op deze wiki vind je informatie over de statistische aspecten van wetenschappelijk onderzoek. Deze wiki is gemaakt door researchmaster studenten van de opleiding psychologie. Zij hebbe...

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The Bayes factor (B) compares the probability of an experimental theory to the probability of the null hypothesis. It gives the means of adjusting your odds in a continuous way.

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What can I find on this page? On this page, you can find a summary for all the study materials you need for the developmental specialization of the Psychology bachelor's programme at the University of Amsterdam. There is a link for all the separate courses. The courses have been organized into ...

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  • On this page we discuss questions students have with the 2022/2023 course "Developmental Psychology" at the University of Amsterdam
  • You have the opportunity to ask any questions you might have and I will try and answer them
  • On 30/1/2023 you can ask questions!

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