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27 Cards in this Set
- Front
- Back
- 3rd side (hint)
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The three main features that science shares: |
1. Systematic Empiricism 2. Empirical questions 3. Public Knowledge |
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Pseudoscience |
Claims to be scientific but lacks one or more of the three features of science: a. Lack of systematic Empiricism b. Lack of public knowledge (i.e no scientific peer reviewed publications) c. It does not address empirical questions (the problem of falsifiability) |
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Folk Psychology (Common sense) |
Is a human capacity to explain and predict the behavior and mental state of other people. BUT: 1. It makes anyone feel like they can be a psychologist 2. It makes anyone feel like they can be a researcher 3. It is influenced by a number of biases 4. It negatively influences a skeptic attitude 5. It does not tolerate uncertainty |
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Research in Psychology |
Refers to 5 main phases: 1. Formulation of an adequate research question (falsifiability, etc.) 2. Design and implementation of an empirical study 3. Data collection and analysis 4. Conclusion (answer to the research question) 5. Publication |
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Design and implementation of the research |
Identify the correct design to answer the experimental question. Different kinds of designs: 1. Experimental designs 2. Quasi-experimental designs 3. Non-experimental designs |
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Implémentation of a research |
1. Select the adequate tools and equipment for the research 2. Sampling (a control technique differentiating experiments from quasi-experiments) 3. A feasibility study 4. The preparation of the experimental materials, procedures, etc. 5. The conduction of a "pilot study" |
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Data collection and analysis |
Must allow for having at hand all the information needed to answer the research question. Must follow the research hypothesis. Results are aimed at either confirming or falsifying the previously defined hypothesis |
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Conclusion |
This is represented by two main parts: 1. Interpretation, conceptualization of the results (this part also identifies the limitations of the research that may explain part of the observes results) 2. Open issues and further research questions raised by the observes results |
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Publication |
Crucial for 2 reasons: 1. The research is evaluated by peer-reviewers. 2. The results are shared with the scientific community |
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Confirmation Bias |
When we tend to focus on cases that confirm our intuitive beliefs and not in cases that disconfirm them |
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Variable |
A quantity or quality that varies across people or situations. For example: the height of the students in a psychology class is a variable because it varies from student to student. |
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Quantitative Variable |
Is a quantity, such as height, that is typically measured by assigning a number to each individual. For example: quantitative variables include people's level of talkativeness, how depressed they are, and the number of siblings they have |
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Categorical variable |
Is a quality such as sex, and is typically measured by assigning a category label to each individual. Other examples: people's nationality, their occupation, and whether they are receiving psychotherapy |
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Convenience sampling |
In which the sample consists of individuals who happen to be nearby and willing to participate. The obvious problem with convenience sampling is that the sample might not be representative of the population |
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Score |
This is the result of a variable that has been measured for a particular individual |
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Data (plural) Datum (singular) |
A set of scores |
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Types of sampling |
Random sampling Convenience sampling |
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Statistical Relationship |
When the average score on one differs systematically across the level of the other between two variables.
A statistical relationship exists if a change in one variable (X) results in a systematic increase in another (Y). The systematic increase doesn't have to be exact, but is should be approximately the same.
It is important because it tells us about the causes, consequences, development, and organization of those behaviors and characteristics |
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Two basic forms of Statistical Relationship |
1. Differences between groups 2. Correlations between quantitative variables |
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Four different levels of measurement/ "scales of measurement" |
1. Nominal level 2. Ordinal level 3. Interval level 4. Ratio level
By understanding the scale of measurement of their data, data scientists can determine the kind of statistical test to perform |
Developed by Stanley Stevens (Psychologist) |
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Nominal scale of measurement |
Defines the identity property of data. This scale has certain characteristics, but does not have any form of numerical meaning. Eg. Eye color, country of birth, male or female, etc. |
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Interval scale of measurement |
Contains properties of nominal and ordered data, but the difference between data points can be quantified. This type of data shows both the order of the variables and the exact differences between the variables. They can be added to or subtracted from each other, but not multiplied or divided. Eg. 40 degrees is not 20 degrees multiplied by two Zero exists as a variable, it has a meaning. Eg. If you measure degrees, zero is a temperature |
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Ratio scale of measurement |
This measurement includes properties from all four scales of measurement. The data is nominal and defined by an identity, can be classified in order, contains intervals and can be broken down into exact value. Weight, height and distance are all examples of ratio variables. Data in the ratio scale can be added, subtracted, divided and multiplied. |
Differs from interval scales in that the scale has a 'true zero' |
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Differences Between Groups |
A difference between the mean scores of two distinct groups (eg. males vs. females; psychotherapy A vs. psychotherapy B) on some variable of interest. Differences between groups are usually described by giving the mean score and standard deviation for each group. This information can be presented in a bar graph. |
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Correlations between Quantitative Variables |
Where the average score on one variable differs systematically across the levels of the other. These are often presented using scatterplots |
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Positive relationship |
In which higher scores on one variable tend to be associated with higher scores on the other. Eg. A person whose stress score was 10 and who had three physical symptoms |
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Negative relationship |
When higher scores on one variable tend to be associated with lower scores on the other |
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