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Kindle Notes & Highlights
by
Andy Field
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January 9 - February 23, 2021
the evil goat-warriors of Satan force our non-mathematical brains to apply themselves to what is the very complex task of becoming a statistics expert.
Statistics is a bit like sticking your finger into a revolving fan blade: sometimes it’s very painful, but it does give you answers to interesting questions.
Scientists are curious people, and you probably are too. However, it might not have occurred to you that to answer interesting questions, you need data and explanations for those data.
to answer interesting questions you need data.
When numbers are involved, the research involves quantitative methods, but you can also generate and test theories by analyzing language (such as conversations, magazine articles and media broadcasts). This involves qualitative methods and it is a topic for another book not written by me.
People can get quite passionate about which of these methods is best, which is a bit silly because they are complementary, not competing, approaches and there are much more important issues in the world to get upset about.
Passions run high between qualitative and quantitative researchers, so its inclusion will likely result in me being hunted down, locked in a room and forced to do discourse analysis by a horde of rabid qualitative researchers.
You begin with an observation that you want to understand, and this observation could be anecdotal (you’ve noticed that your cat watches birds when they’re on TV but not when jellyfish are on)4 or could be based on some data (you’ve got several cat owners to keep diaries of their cat’s TV habits and noticed that lots of them watch birds).
First you collect some relevant data (and to do that you need to identify things that can be measured) and then you analyze those data. The analysis of the data may support your hypothesis or generate a new one, which, in turn, might lead you to revise the theory.
A lot of scientific endeavour starts this way: not by watching reality TV, but by observing something in the world and wondering why it happens.
A theory is an explanation or set of principles that is well substantiated by repeated testing and explains a broad phenomenon.
One theory of personality disorders in general links them to early attachment (put simplistically, the bond formed between a child and their main caregiver).
There is also a critical mass of evidence to support the idea.
A hypothesis is a proposed explanation for a fairly narrow phenomenon or set of observations.
Both theories and hypotheses seek to explain the world, but a theory explains a wide set of phenomena with a small set of well-established principles, whereas a hypothesis typically seeks to explain a narrower phenomenon and is, as yet, untested. Both theories and hypotheses exist in the conceptual domain, and you cannot observe them directly.
That is, we need to operationalize our hypothesis in a way that enables us to collect and analyze data that have a bearing on the hypothesis
Predictions emerge from a hypothesis (Misconception Mutt 1.1), and transform it from something unobservable into something that is.
In making this prediction we can move from the conceptual domain into the observable domain, where we can collect evidence.
Non-scientific statements can sometimes be altered to become scientific statements,
One of the things I like about mBased on what you have readany reality TV shows in the UK is that the winners are very often nice people, and the odious people tend to get voted out quickly, which gives me faith that humanity favors the nice.
falsification, which is the act of disproving a hypothesis or theory.
Variables are things that can change (or vary); they might vary between people (e.g., IQ, behavior) or locations (e.g., unemployment) or even time (e.g., mood, profit, number of cancerous cells).
Most hypotheses can be expressed in terms of two variables: a proposed cause and a proposed outcome.
Independent variable: A variable thought to be the cause of some effect. This term is usually used in experimental research to describe a variable that the experimenter has manipulated. Dependent variable: A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome. Predictor variable: A variable thought to predict an outcome variable. This term is basically another way of saying ‘independent variable’. (Although some people won’t like me saying that; I think life would be easier if we talked only about predictors and outcomes.) Outcome
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The relationship between what is being measured and the numbers that represent what is being measured is known as the level of measurement.
A categorical variable is made up of categories.
A categorical variable is one that names distinct entities. In its simplest form it names just two distinct types of things, for example male or female. This is known as a binary variable.
When two things that are equivalent in some sense are given the same name (or number), but there are more than two possibilities, the variable is said to be a nominal variable.
When categories are ordered, the variable is known as an ordinal variable. Ordinal data tell us not only that things have occurred, but also the order in which they occurred.
A continuous variable is one that gives us a score for each person and can take on any value on the measurement scale that we are using.
Interval data are considerably more useful than ordinal data, and most of the statistical tests in this book rely on having data measured at this level at least.
Ratio variables go a step further than interval data by requiring that in addition to the measurement scale meeting the requirements of an interval variable, the ratios of values along the scale should be meaningful.
A truly continuous variable can be measured to any level of precision, whereas a discrete variable can take on only certain values (usually whole numbers) on the scale.
A continuous variable would be something like age, which can be measured at an infinite level of precision
The first property is validity, which is whether an instrument measures what it sets out to measure. The second is reliability, which is whether an instrument can be interpreted consistently across different situations.
Validity refers to whether an instrument measures what it was designed to measure
Criterion validity is whether you can establish that an instrument measures what it claims to measure through comparison to objective criteria.
When data are recorded simultaneously using the new instrument and existing criteria, then this is said to assess concurrent validity; when data from the new instrument are used to predict observations at a later point in time, this is said to assess predictive validity.
The easiest way to assess reliability is to test the same group of people twice: a reliable instrument will produce similar scores at both points in time (test–retest reliability).
us. In correlational or cross-sectional research we observe what naturally goes on in the world without directly interfering with it, whereas in experimental research we manipulate one variable to see its effect on another.
In correlational research we observe natural events; we can do this by either taking a snapshot of many variables at a single point in time or by measuring variables repeatedly at different time points (known as longitudinal research).
Correlational research provides a very natural view of the question we’re researching because we’re not influencing what happens and the measures of the variables should not be biased by the researcher being there (this is an important aspect of ecological validity).
Most scientific questions imply a causal link between variables; we have seen already that dependent and independent variables are named such that a causal connection is implied
David Hume, an influential philosopher, defined a cause as ‘An object precedent and contiguous to another, and where all the objects resembling the former are placed in like relations of precedency and contiguity to those objects that resemble the latter’
This definition implies that (1) the cause needs to precede the effect, and (2) causality is equated to high degrees of correlation between contiguous events.
The first problem with doing this is that it provides no information about the contiguity between different variables:
Longitudinal research addresses this issue to some extent, but there is still a problem with Hume’s idea that causality can be inferred from corroborating evidence, which is that it doesn’t distinguish between what you might call an ‘accidental’ conjunction and a causal one.
This example illustrates an important limitation of correlational research: the tertium quid (‘A third person or thing of indeterminate character’).
These extraneous factors are sometimes called confounding variables or confounds for short.
The shortcomings of Hume’s definition led John Stuart Mill (1865) to suggest that, in addition to a correlation between events, all other explanations of the cause–effect relationship must be ruled out.
