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Week 1- Introduction
Fichas del conjunto (17)
- 1Difference between population and sample?- Population: Entire collection of events in which you are interested. E.G. Every office worker in Australia
- Sample: A smaller portion of the population of interest. E.G. A small sample of Australian office workers from each state. - 2Descriptive StatisticsDescriptive statistics are used to describe the basic features of the data in a study (Means, extreme scores). They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.
- 3Inferential StatisticsInferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question.
- 4Discrete variables Vs Continuous variables-Discrete variables have only a limited number of values. E.G. Gender
- Continuous variables can take many different values. E.G. age, anxiety score. - 5Difference between categorical & measurement data- Categorical data: Used when dealing with discrete variables.
- Measurement data: Used when dealing with continuous variables. - 6Independant Variable- IV is controlled by the researcher.
-E.G. Group membership (researcher assigns participants to a particular group such as high or low anxiety, or control or treatment group)
-Dependant variable (DV) is influenced by the IV. - 7Dependant Variable-DV is the measured data.
-Variable observed is difference and changes.
- E.G. Levels of depression in controlled V Sample group where the IV is medication V counselling. - 8Nominal scalesNominal scales are labels for categories of data. There is no meaningful underlying order to these labels—we cannot order the variables or talk about distance between them. Religion is an example of a variable that is measured on a nominal scale (e.g. Muslim, Christian, Jewish, other).
- 9Ordinal scalesThe ordinal scale orders people’s responses along some dimension (e.g. number of life changes during the past six months). Importantly, the distances between scale values are unknown. That is, the difference between 20 and 25 life changes on stress is not necessarily the same as the difference between 10 and 15 life changes.
- 10Interval scalesWith an interval scale such as temperature we have meaningful differences between points on the scale. However, there is no true zero point (0 degrees is not an absence of temperature)—so we cannot say that 40 degrees is half as hot as 80 degrees. Therefore, with interval scales we cannot talk in terms of ratios.
- 11Ratio scalesThis is the ‘highest’ scale of measurement, in that it is the most informative. Ratio scales have all the characteristics of an interval scale plus a true zero point (e.g. time, weight and length).
- 12Frequency distributionsFrequency distributions report how often each score was recorded in the data set. When presented in table form, a frequency distribution lists all values for a variable along with the frequency of each
- 13HistogramsA histogram essentially provides a graphical illustration of the frequency distribution. When a data set has a large range of many different values, the data can be plotted as a grouped distribution.
- 14Stem-and-leaf displaysWith more complex data it may be better to illustrate the information using stem-and-leaf displays. Scores are presented in table form using leading digits (stem) and trailing digits (leaves).
- 15Negatively skewed distributionsWhen more people score at the high end of a scale, the distribution is said to be negatively skewed.
- 16Positively skewed distributionsWhen more people score at the low end of a scale, the distribution is said to be positively skewed.
- 17KurtosisKurtosis refers to how ‘flat’ or ‘peaked’ the distribution appears.
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- Descripción
- (Week 1 - Introduction) HPS772- Research Methods in Psychology A Fichas sobre Week 1- Introduction, creado por Simon Hudson el 14/07/2018.
- Creado por
- Simon Hudson · jul 2018
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- 14 jul 2018
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