Chapter 1 Flashcards
Between-Groups Design
another name for independent design
independent design: an experimental design in which different treatment conditions utilize different organisms (e.g., in psychology, this would mean using different people in different treatment conditions) and so the resulting data are independent (a.k.a. between-groups or between-subjects designs).
Between-Subjects Design
another name for independent design
independent design: an experimental design in which different treatment conditions utilize different organisms (e.g., in psychology, this would mean using different people in different treatment conditions) and so the resulting data are independent (a.k.a. between-groups or between-subjects designs).
Bimodel
a description of a distribution of observations that has two modes .
Binary Variable
a categorical variable that has only two mutually exclusive categories (e.g., being dead or alive).
Boredom Effect
refers to the possibility that performance in tasks may be influenced (the assumption is a negative influence) by boredom or lack of concentration if there are many tasks or the task goes on for a long period of time. In short, what you are experiencing reading this glossary is a boredom effect.
Categorical Variable
any variable made up of categories of objects/entities. The university you attend is a good example of a categorical variable: students who attend the University of Sussex are not also enrolled at Harvard or UV Amsterdam, therefore, students fall into distinct categories.
Central Tendency
a generic term describing the center of a frequency distribution of observations as measured by the mean , mode and median .
Concurrent Validity
a form of criterion validity where there is evidence that scores from an instrument correspond to concurrently recorded external measures conceptually related to the measured construct.
Confounding Variable
a variable (that we may or may not have measured) other than the predictor variables in which we’re interested that potentially affects an outcome variable .
Content Validity
evidence that the content of a test corresponds to the content of the construct it was designed to cover.
Continuous Variable
a variable that can be measured to any level of precision. (Time is a continuous variable, because there is in principle no limit on how finely it could be measured.)
Correlational Reseach
a form of research in which you observe what naturally goes on in the world without directly interfering with it. This term implies that data will be analyzed so as to look at relationships between naturally occurring variables rather than making statements about cause and effect. Compare with cross-sectional research , longitudinal research and experimental research .
Counterbalancing
a process of systematically varying the order in which experimental conditions are conducted. In the simplest case of there being two conditions (A and B), counterbalancing simply implies that half of the participants complete condition .
Criterion Validity
evidence that scores from an instrument correspond with ( concurrent validity ) or predict ( predictive validity ) external measures conceptually related to the measured construct.
Cross-Sectional Research
a form of research in which you observe what naturally goes , longitudinal research .
Dependent Variable
another name for outcome variable . This name is usually associated with experimental methodology (which is the only time it really makes sense) and is used because it is the variable that is not manipulated by the experimenter and so its value depends on the variables that have been manipulated. To be honest, I just use the term outcome variable all the time – it makes more sense (to me) and is less confusing.
Deviance
the difference between the observed value of a variable and the value of that variable predicted by a statistical model.
Discrete Variable
a variable that can only take on certain values (usually whole numbers) on the scale.
Ecological Validity
evidence that the results of a study, experiment or test can be applied, and allow inferences, to real-world conditions.
Experimental Research
a form of research in which one or more variables are systematically manipulated to see their effect (alone or in combination) on an outcome variable . This term implies that data will be able to be used to make statements about cause and effect. Compare with cross-sectional research and correlational research .
Falsification
the act of disproving a hypothesis or theory.
Frequency Distribution
a graph plotting values of observations on the horizontal axis, and the frequency with which each value occurs in the data set on the vertical axis (a.k.a. histogram ).
Histogram
a frequency distribution .
Hypothesis
a proposed explanation for a fairly narrow phenomenon or set of observations. It is not a guess, but an informed, theory-driven attempt to explain what has been observed. A hypothesis cannot be tested directly but must first be operationalized as predictions about variables that can be ).
Independent Design
an experimental design in which different treatment conditions utilize different organisms (e.g., in psychology, this would mean using different people in different treatment conditions) and so the resulting data are independent (a.k.a. between-groups or between-subjects designs).
Independent Variable
another name for a predictor variable . This name is usually associated with experimental methodology (which is the only time it makes sense) and is used because it is the variable that is manipulated by the experimenter and so its value does not depend on any other variables (just on the experimenter). I just use the term predictor variable all the time because the meaning of the term is not constrained to a particular methodology.
Interquartile Range
the limits within which the middle 50% of an ordered set of observations fall. It is the difference between the value of the upper quartile and lower quartile .
Interval Variable
data measured on a scale along the whole of which intervals are equal. For example, people’s ratings of this book on Amazon.com can range from 1 to 5; for these data to be interval it should be true that the increase in appreciation for this book represented by a change from 3 to 4 along the scale 5.
Journal
in the context of academia a journal is a collection of articles on a broadly related theme, written by scientists, that report new data, new theoretical ideas or reviews/critiques of existing theories and data. Their main function is to induce learned helplessness in scientists through a complex process of self-esteem regulation using excessively harsh or complimentary peer feedback that has seemingly no obvious correlation with the actual quality of the work submitted.
Kurtosis
this measures the degree to which scores cluster in the tails of a frequency distribution. Kurtosis is calculated such that no kurtosis yields a value of 3. To make the measure more intuitive, SPSS Statistics (and some other packages) subtract 3 from the value so that no kurtosis is expressed as 0 and positive and negative kurtosis take on positive and negative values, respectively. A distribution with positive kurtosis ( leptokurtic , kurtosis > 0) has too many scores in the tails and is too peaked, whereas a distribution with negative kurtosis ( platykurtic , kurtosis < 0) has too few scores in the tails and is quite flat.
Leptokurtic
A distribution with positive kurtosis ( leptokurtic , kurtosis > 0) has too many scores in the tails and is too peaked.
Level of Measurement
the relationship between what is being measured and the numbers obtained on a scale.
Platykurtic
a distribution with negative kurtosis ( platykurtic , kurtosis < 0) has too few scores in the tails and is quite flat.
Longitudinal Research
a form of research in which you observe what naturally goes on in the world without directly interfering with it, by also correlational research , cross-sectional research .
Lower Quartile
the value that cuts off the lowest 25% of the data. If the data are ordered and then divided into two halves at the median, then the lower quartile is the median of the lower half of the scores.
Mean
a simple statistical model of the center of a distribution of scores. A hypothetical estimate of the ‘typical’ score.
Measurement Error
the discrepancy between the numbers used to represent the thing that we’re measuring and the actual value of the thing we’re measuring (i.e., the value we would get if we could measure it directly).
Median
the middle score of a set of ordered observations. When there is an even number of observations the median is the average of the two scores that fall either side of what would be the middle value.
Mode
the most frequently occurring score in a set of data.
Multimodal
description of a distribution of observations that has more .
Negative Skew
when the frequent scores are clustered at the higher end of the distribution and the tail points towards the lower or more negative scores, the value of skew is negative.
Skew
a measure of the symmetry of a frequency distribution . Symmetrical distributions have a skew of 0. When the frequent scores are clustered at the lower end of the distribution and the tail points towards the higher or more positive scores, the value of skew is positive. Conversely, when the frequent scores are clustered at the higher end of the distribution and the tail points towards the lower or more negative scores, the value of skew is negative.
Positive Skew
When the frequent scores are clustered at the lower end of the distribution and the tail points towards the higher or more positive scores, the value of skew is positive.
Nominal Variable
where numbers merely represent names. For example, the numbers on sports players shirts: a player with the number 1 on her back is not necessarily worse than a player with a 2 on her back. The numbers have no meaning other than denoting the type of player (full back, center forward, etc.).
Nonile
a type of quantile ; they are values that split the data into nine equal parts. They are comonly used in educational research.
Normal Distribution
a probability distribution of a random variable that is known to have certain properties. It is perfectly symmetrical (has a skew of 0), and has a kurtosis of 0.
Ordinal Variable
data that tell us not only that things have occurred, but also the order in which they occurred. These data tell us nothing about the differences between values. For example, gold, silver and bronze medals are ordinal: they tell us that the gold medallist was better than the silver medallist, but they don’t tell us how much better (was gold a lot better than silver or were gold and silver very closely competed?).
Outcome Variable
a variable whose values we are trying to predict from one or more predictor variables .
Percentile
a type of quantile ; they are values that split the data into 100 equal parts.
Practice Effect
refers to the possibility that participants’ performance in a task may be influenced (positively or negatively) if they repeat the task because of familiarity with the experimental situation and/or the measures being used.
Predictive Validity
a form of criterion validity where there is evidence that scores from an instrument predict external measures (recorded at a different point in time) conceptually related to the measured construct.
Predictor Variable
a variable that is used to try to predict values of another variable known as an outcome variable .
Probability Distribution
a curve describing an idealized frequency distribution of a particular variable from which it is possible to ascertain the probability with which specific values of that variable will occur. For categorical variables it is simply a formula yielding the probability with which each category occurs.
Qualitative Methods
extrapolating evidence for a theory from what people say or write (contrast with quantitative methods ).
Quantile
values that split a data set into equal portions. Quartiles , for example, are a special case of quantiles that split the data into four equal parts. Similarly, percentiles are points that split the data into 100 equal parts and noniles are points that split the data into nine equal parts (you get the general idea).
Quantitative Methods
inferring evidence for a theory through measurement of variables that produce numeric outcomes (cf. qualitative methods ).
Quartile
a generic term for the three values that cut an ordered data set into four equal parts. The three quartiles are known as the first or lower quartile , the second quartile (or median ) and the third or upper quartile .
Randomization
the process of doing things in an unsystematic or random way. In the context of experimental research the word usually applies to the random assignment of participants to different treatment conditions.
Range
the range of scores is the value of the smallest score subtracted from the highest score. It is a measure of the dispersion of a set of scores.
Ratio Variable
an interval variable but with the additional property that ratios are meaningful. For example, people’s ratings of this book on Amazon.com can range from 1 to 5; for these data to be ratio not only must they have the properties of interval variables , but in addition a rating of 4 should genuinely represent someone who enjoyed this book twice as much as someone who rated it as 2. Likewise, someone who rated it as 1 should be half as impressed as someone who rated it as 2.
Reliability
the ability of a measure to produce consistent results when the same entities are measured under different conditions.
Repeated-Measures Design
an experimental design in which different treatment conditions utilize the same organisms (i.e., in psychology, this would mean the same people take part in all experimental conditions) and so the resulting data are related (a.k.a. related design or within-subject design).
Second Quartile
another name for the median .
Standard Deviation
an estimate of the average variability (spread) of a set of data measured in the same units of measurement as the original data. It is the square root of the variance .
Sum of Squared Errors
another name for the sum of squares .
Sum of squares (SS): an estimate of total variability (spread) of a set of observations around a parameter (such as the mean ). First the deviance for each score is calculated, and then this value is squared. The SS is the sum of these squared deviances.
Systematic Variation
variation due to some genuine effect (be that the effect of an experimenter doing something to all of the participants in one sample but not in other samples or natural variation between sets of variables). We can think of this as variation that can be explained by the model that we’ve fitted to the data.
Tertium Quid
the possibility that an apparent relationship between two variables is actually caused by the effect of a third variable on them both (often called the third-variable problem ).
Test-Retest Reliability
the ability of a measure to produce consistent results when the same entities are tested at two different points in time.
Theory
although it can be defined more formally, a theory is a hypothesized general principle or set of principles that explain known findings about a topic and from which new hypotheses can be generated. Theories have typically been well-substantiated
Unsystematic Variation
this is variation that isn’t due to the effect in which we’re interested (so could be due to natural differences between people in different samples such as differences in intelligence or motivation). We can think of this as variation that can’t be explained by whatever model we’ve fitted to the data.
Upper Quartile
the value that cuts off the highest 25% of ordered scores. If the scores are ordered and then divided into two halves at the median, then the upper quartile is the median of the top half of the scores.
Validity
evidence that a study allows correct inferences about the question it was aimed to answer or that a test measures what it set out to measure conceptually.
Variables
anything that can be measured and can differ across entities or across time.
Variance
an estimate of average variability (spread) of a set of data. It is the sum of squares divided by the number of values on which the sum of squares is based minus 1.
Within-Subject Design
another name for a repeated-measures design .
Repeated-measures design: an experimental design in which different treatment conditions utilize the same organisms (i.e., in psychology, this would mean the same people take part in all experimental conditions) and so the resulting data are related (a.k.a. related design or within-subject design).
Z-Scores
the value of an observation expressed in standard deviation units. It is calculated by taking the observation, subtracting from it the mean of all observations, and dividing the result by the standard deviation of all observations. By converting a distribution of observations into z -scores a new distribution is created that has a mean of 0 and a standard deviation of 1.
What are (broadly speaking) the five stages of the research process?
Generating a research question: through an initial observation (hopefully backed up by some data).
Generate a theory to explain your initial observation.
Generate hypotheses: break your theory down into a set of testable predictions.
Collect data to test the theory: decide on what variables you need to measure to test your predictions and how best to measure or manipulate those variables.
Analyse the data: look at the data visually and by fitting a statistical model to see if it supports your predictions (and therefore your theory). At this point you should return to your theory and revise it if necessary.
What is the fundamental difference between experimental and correlational research?
In a word, causality. In experimental research we manipulate a variable (predictor, independent variable) to see what effect it has on another variable (outcome, dependent variable). This manipulation, if done properly, allows us to compare situations where the causal factor is present to situations where it is absent. Therefore, if there are differences between these situations, we can attribute cause to the variable that we manipulated. In correlational research, we measure things that naturally occur and so we cannot attribute cause but instead look at natural covariation between variables.
What is the level of measurement of the following variables? -The number of downloads of different bands’ songs on iTunes
This is a discrete ratio measure. It is discrete because you can download only whole songs, and it is ratio because it has a true and meaningful zero (no downloads at all).
What is the level of measurement of the following variables? -The names of the bands that were downloaded
This is a nominal variable. Bands can be identified by their name, but the names have no meaningful order. The fact that Norwegian black metal band 1349 called themselves 1349 does not make them better than British boy-band has-beens 911; the fact that 911 were a bunch of talentless idiots does, though.
What is the level of measurement of the following variables? -Songs on ITunes positions in the download chart
This is an ordinal variable. We know that the band at number 1 sold more than the band at number 2 or 3 (and so on) but we don’t know how many more downloads they had. So, this variable tells us the order of magnitude of downloads, but doesn’t tell us how many downloads there actually were.
What is the level of measurement of the following variables? -The money earned by the bands from the downloads of songs on Itunes
This variable is continuous and ratio. It is continuous because money (pounds, dollars, euros or whatever) can be broken down into very small amounts (you can earn fractions of euros even though there may not be an actual coin to represent these fractions).
What is the level of measurement of the following variables? -The weight of drugs bought by the bands with their royalties
This variable is continuous and ratio. If the drummer buys 100 g of cocaine and the singer buys 1 kg, then the singer has 10 times as much.
What is the level of measurement of the following variables? -The type of drugs bought by the bands with their royalties
This variable is categorical and nominal: the name of the drug tells us something meaningful (crack, cannabis, amphetamine, etc.) but has no meaningful order.
What is the level of measurement of the following variables? -The phone numbers that the bands obtained because of their fame
This variable is categorical and nominal too: the phone numbers have no meaningful order; they might as well be letters. A bigger phone number did not mean that it was given by a better person.
What is the level of measurement of the following variables? -The gender of the people giving the bands their phone numbers
This variable is categorical and binary: the people dishing out their phone numbers could fall into one of only two categories (male or female).
What is the level of measurement of the following variables? -The instruments played by the band members
This variable is categorical and nominal too: the instruments have no meaningful order but their names tell us something useful (guitar, bass, drums, etc.).
What is the level of measurement of the following variables? -The time they had spent learning to play their instruments
This is a continuous and ratio variable. The amount of time could be split into infinitely small divisions (nanoseconds even) and there is a meaningful true zero (no time spent learning your instrument means that, like 911, you can’t play at all).
Say I own 857 CDs. My friend has written a computer program that uses a webcam to scan the shelves in my house where I keep my CDs and measure how many I have. His program says that I have 863 CDs. Define measurement error. What is the measurement error in my friend’s CD-counting device?
Measurement error is the difference between the true value of something and the numbers used to represent that value. In this trivial example, the measurement error is 6 CDs. In this example we know the true value of what we’re measuring; usually we don’t have this information, so we have to estimate this error rather than knowing its actual value.
Based on what you have read in this section, what qualities do you think a scientific theory should have?
A good theory should do the following:
Explain the existing data. Explain a range of related observations. Allow statements to be made about the state of the world. Allow predictions about the future. Have implications.
What is the difference between reliability and validity?
Validity is whether an instrument measures what it was designed to measure, whereas reliability is the ability of the instrument to produce the same results under the same conditions.
Why is randomization important?
It is important because it rules out confounding variables (factors that could influence the outcome variable other than the factor in which you’re interested). For example, with groups of people, random allocation of people to groups should mean that factors such as intelligence, age and gender are roughly equal in each group and so will not systematically affect the results of the experiment.
