RQ-week1.Rmd

# Week 1 - Reading questions

```{r include=FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
config <- yaml::yaml.load_file("config.yml")
```

#### Reference {-}

[Smaldino, P. E. (2017). *Models are stupid, and we need more of them.* Computational social psychology, 311-331.](https://www.researchgate.net/profile/Paul_Smaldino/publication/289540152_Models_Are_Stupid_and_We_Need_More_of_Them/links/5aba571eaca2728f4fa3a89e/Models-Are-Stupid-and-We-Need-More-of-Them.pdf)

**SKIP PAGES 322 - 327**

#### Questions {-}

1. What are the differences between a “verbal model” and a “formal model”?
2. As explained in the paragraph “A Brief Note on Statistical Models”, formal models are not the same as statistical models. Still, we can learn a lot from Smaldino’s approach. Write down three insights from this paper that you would like to apply to your statistical modelling during this course, and discuss with your learning group.


```{r echo=FALSE, results = 'asis', eval = config$show_text}
cat("#### Answers {-}

1. A verbal model can be non-specific about the level of analysis and the definitions of its parts and relationships within a system. One reason verbal models can appear powerful and useful is because they employ strategic ambiguity: By being vague, a verbal model can apparently explain many phenomena, even contradictory ones (see: the Cubist chicken). A formal model seeks to define its level of analysis, parts (variables), and relationships between them. 
1. Here are several important insights:
    * You should define the level of analysis for your model
    * You should clearly operationalize the parts (variables) and explicitly define the hypothesized relationships (causal, correlational, indicator of) between these parts
    * The act of formally defining our models is productive: We can test a well-defined model using data, we can discuss it among experts with relative consensus about what the model means, and we can improve it. These things are harder to do with “vague” verbal models.
    * Every model imposes some violence upon reality, i.e., every model is wrong (but some are useful).
    * Because a model is a simplification, by necessity, we leave out many factors. Think about how this relates to the third requirement for causality: All alternative causes are ruled out.
    * Data can be used to validate a model, and to refine it to become an ever closer approximation of reality
")
```