Gray H2 (College 2, Boek Hoofdstuk 2 Alle Begrippen)

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Fact

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Objective statement usually based on direct observation, that reasonable observers agree is true.

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Theory

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Idea, or a conceptual model, that is designed to explain existing facts and make predictions about new facts that might be discovered.

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Hypothesis

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Any prediction about new facts that is made from a theory.

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How are facts, theories and hypotheses related to one another in scientific research?

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Facts are objective observations, a theory is trying to understand these facts through a conceptual model so you can predict the future facts arriving. Hypothesis are these predictions about future facts.

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Lessons by Clever Hans

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1. The value of skepticism

2. the value of careful observations under controlled conditions.

3. The problem of observer-expectancy effects.

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The value of skepticism

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It is very valuable to try to be skeptical at all times. Even if it is your own research, always try to prove your theory wrong. It is very important to try to disprove them since those that will survive are most likely correct. In order to find the truth about this world, we need to be skeptical about claims.

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The value of careful observations under controlled conditions

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It is very important to carefully observe what happens and try to find other explanations. The best thing to then do is to test them. This way, we can find the actual way things work. Careful observation under controlled conditions is a hallmark of the scientific method.

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The problem of observer-expectancy effects

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The people conducting the experiment may unintentionally communicate their expectations and how the subjects 'should' behave. This may influence the research. It is very important to make it a ''double blind'' procedure.

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the dimensions of types of research strategies

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1. the research design

2. the setting in which the study is conducted

3. the data-collection method

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Independent variable

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Datgene wat gemanipuleerd wordt door de onderzoeker om het effect ervan te meten.

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Dependent variable

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Datgene wat ''zou'' moeten worden beïnvloedt door de onafhankelijke variabele. We zijn geïntresseerd of dit veranderd wanneer we de onafhankelijke variabele veranderen.

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Experiment

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A procedure in which a researcher systematically manipulates one or more independent variables and looks for changes in one or more dependent variables, while keeping all other variables constant.

- Een procedure waarin een onderzoeker systematisch iets manipuleerd om het effect daarvan te bekijken terwijl hij alle andere mogelijke variabelen zo constant mogelijk houdt.

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within-subjects design

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Each subject is tested in each of the different conditions of the independent variable. Elke persoon wordt aan beide condities blootgesteld.

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Between-subjects-design

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There is a separate group of subjects for each different condition of the independent variable.

-Er zijn groepen, de ene groep is de ''experimentele groep'' die met de gemanipuleerde onafhankelijkevariabele het experiment doet en de andere groep doet dit zonder de gemanipuleerde onafhankelijke variabele.

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What were the independent and dependent variables in DiMascio's experiment on treatments for depression? Why were the subjects randomly assigned to the different treatments rather than allowed to choose their own treatment?

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The independent variable were the differen treatments and the dependent variable was the scale of ''depression'' they weren't allowed to choose their own group because it might influence of ''bias'' the groups. The only difference aside from the therapy should be by chance since it makes the chance of biason the experiment smaller. Also, staticians have ways of correcting ''change'' to see if it was really a cause.

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Correlationele study

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a study in which the researcher does not mainpulate any variable, but observes or measures two or more already existing ariables to find relationships between them. Correlational studies can identify relationships between variables, which allow us to make predictions about one variable based on the knowledge of another. These do not tell us anything about ''cause and response''

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What are the differences between a correlational study and an experiment, in procedure and in types of conclusions that can be drawn?

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In experiments you manipulate a variable. In correlational studies you don't manipulate but look at already existing variables. Also, you can conclude ''cause respons'' from expiriments and you can only conclude a ''relationship'' with a correlational study.

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How does an analysis of Baumrind's classic study of parental disciplinary styles illustrate the difficulty of trying to infer cause and effect from a correlation?

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Because there are alternative explanations possible. It could also be that the children make the parents go in a style by behaving differently, so it's the other way around. Or it could come from both sides instead of just from the parents.

It could also be that a third variable actually influences both parental style and childresns behaviour. For example, good health and an adequate income might promote an authoratitive style and so on.

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Descriptive studies

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Descriptive study is to describe the behavior of an individual or set of individuals without assessing relationships between different variables.

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How do descriptive studies differ, in method and purpose, from experiments and from correlational studies?

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Descriptive studies are to gain information about a subject, to explore multiple aspects of them. Correlational studies the relationships between 2 or more variables.

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Labaratory study

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Any research study in which the subjects are brought to a specially designated area that has been set up to facilitate the researchers collection of data and control the environment. Dit betekent dat mensen naar een plek gaan die de onderzoeker heeft uigekozen om ervoor te zorgen dat er geen andere mogelijkheden zijn die het onderzoek kunnen beïnvloeden.

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Field study

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Een onderzoek dat plaats vindt bij bv. mensen thuis, op het werk, bij winkelcentra etc. Zolang het maar onderdeel is van de omgeving van de proefpersoon.

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Correlationeel en experimenteel vs field en labaratory

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Mostly correlational and field, and experimental and labaratory go together. This is however not always the case!

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What are the relative advantages and disadvantages of laboratory studies and field studies?

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Field = more open to other factors and in the labaratory you can limit the other factors that might influence your research.

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Data collection methods

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Self-report methods and observational methods

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Self report methods

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This might be done through a written questionnaire, a structured interview or an unstructured interview.

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Observational methods

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Naturalistic observation and tests. Naturalistic is in their own environmen, watching how people behave. A test is where the observator manipulates a part of the environment of a the test persons to see how they react to it.

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How do self report methods, naturlistic observations and tests differ from one another? What are some advantages and disadvantages of each?

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Self report can provide information that researchers could not obtain through observation but the validity is limited by the subjects ability to observe and remember their own behaviour or moods and franklyness. Naturalistic observations allow researchers to learn firsthand about their subjects natural behaviours, but the practicality of such mehtods is limited by the great amount of time. Then there are test which are convenient and easily scored but are by nature artifical. This means that the relationship between behaviour in the test and in real life might not be clear.

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Statistical procedures use for the purpose of summarizing data and calculating the likelyhoofd of chance.

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descriptive staistics and inferential statistics

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Descriptive statistics

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Summarizing sets of data

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Inferential statistics

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Help researchers decide how confident they can be in judging that the results observed are not due to chance.

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The mean

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The average

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The median

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the center score, rank all the scores from highest to lowest and pick the middle one.

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Variability

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The degree to which the numbers in the set differ from one another and from their mean.

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Common measure of variability

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Standard deviation

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Correlation coefficient

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number between -1 and 1 to determine how much 2 variables are related to each other.

1 = 100% relatie en 0 = 0%. -1 = het een meer, het ander minder 100%.

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How do the mean, median and standard deviation help describe a set of numbers?

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They tell something about the average and the variability of numbers. They tell wether people are close to each other in a correlation, wether they are far away from each other in scores and how they usually score.

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How does a correlation coefficient desribe the direction and strength of a correlation? How can correlations be depicted in the scatter plots?

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They describe wether ''if one of the variables is higher, the other one also gets higher, or if one of the variables is lower, the other one is higher or wether there is no relationship at all'' They can be depicted through seeing if there is some kinde of ''strong'' line upwards or downwards or a ''weak'' line upwards or downwards'' if its upwards its positive and if it's downwards it is negative. If you don't see any kind of ''line'' and just random dots, there is little to zero correlation.

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Why is it necessary to perform inferential statistics before drawing conclusions from the data in a research study?

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So we can be sure that the chance of the research results being ''chance'' are low enough.

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Introspection

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the personal observations of one's thoughts, perceptions and feelings. Je eigen observaties van jezelf.

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What does it mean to say that a result from a research study is statistically significant at the 5 percent level?

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It means that there is a p of lower than .05 This would mean that there is a 1 in 20 chance that the research could be attributed to chance. That is low enough for the modern research world to say it is ''significant'' though it might vary a bit in different researches, it usually is around this level.

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How is statistical significance affected by the size of the effect, the number of subjects or observations, and the variability of the scores within each group?

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3 elemtents that go in determining wether something is significant or not.

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1. The size of the observed effect

2. The number of individual subjects or observations in the study.

3. The variability of the data within each group.

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The size of the observed effect

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A large effect is more likely to be significant than a small one. Larger differences found between th emean scores compared to another, or the larger the correlation, the more likely it is that the effect is statistically significant.

Wanneer iets een groter effect heeft is de kans groter dat dit door het onderzoek en niet door kans komt.

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The number of individual subjects or observations in the study.

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Een grotere steekproef betekent meer zekerheid over de uitslagen die je hebt gevonden. 10 mensen observeren is veel minder betrouwbaar dan 1000.

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The variability of the data within each group.

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Bv. de standaarddeviatie. Als er een grotere diversiteit in de depressiescores van beide groepen zit is de kans groter dat er kans is omdat het willekeurigere resultaten zijn. Minder diversiteit = meer significantie. Wel is het belangrijk om een zo divers mogelijke groep te hebben om het effect wel te kunnen meten en een goede representatie te hebben.

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Descriptive statistics

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Samenvatten van data. De mediaan, het gemiddelde, de standaarddeviatie en de correlatiecoefficient zijn hier voorbeelden van.

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Inerential statistics

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Help us to asses the likelyhood of chance in our research. Significant results are those with a small possibility of chance (5% chance usually). P.05.

The size of the effect, the number of scores and the variability of data are taken into account when calculating this.

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What is the difference between random variation in behaviour and bias, and why is bias the more serious problem?

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Bias means that the results are beïng influenced by an other factor than chance. Random variation is chance. Bias will be off for a bit but won't randomly enter results, it will takethe results off from what we're actually trying to find out. Bias is a serious problem because we can't correct it through statistics.

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Bias

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Nonrandom (directed) effects caused by some factors or factors extraneous to the research hypothesis. Externe factoren die geen kans zijn die het onderzoek beïnvloedden.

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Voorbeeld van Bias?

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Boogschieten, als alles perfect bij elkaar zit maar iets naar rechts is het Bias. Als het random is is gaat het alle kanten op.

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Biased sample

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A sample which is biased because it doesnt represent the populations you want to be able draw conclusions to. For instance, college-students do not represent the whole population of a country even though you might want to generalize to that population.

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How can a nonrepresentative selection of research subjects introduce bias into (a) an experiment and (b) a descriptive study?

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Bij een experiment zorgt dat ervoor dat je je data niet representatief is voor de populatie waardoor je misschien verkeerde uitspraken doet. Bij een descriptieve studie is het hetzelfde probleem. Als je bijv. een samenhang vind bij een biased sample dan kan je deze samenhang niet generalizeren naar een grotere populatie dan die ''sample''.

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Reliability

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Betrouwbaarheid is meetfouten, geen bias. Een meting is betrouwbaar als het dezelfde resultaten geeft elke keer als het met een bepaalde proefperson onder een bepaalde set van condities is. Dit gaat over replicatie bijvoorbeeld wat in interobserver kan (bij verschillende observeerders) en intraobserver (dezelfde opnieuw).

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Operational definition

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Exact voorbeeld van wat je meet en op welke manier je dit meet.

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Validity

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There are many forms of validity. Face validity, criterion validity and so on. It means ''to measure what you want to measure''. Is your operationalization correct? Are there any other biasses or factors that might influence our research and help us to make mistakes?

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Face validity

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Face validity is the extent to which a test is subjectively viewed as covering the concept it purports to measure. It refers to the transparency or relevance of a test as it appears to test participants. In other words, a test can be said to have face validity if it "looks like" it is going to measure what it is supposed to measure

- Face validity is dus of het eruitziet alsof het meet wat het wil meten voor proefpersonen. Dus of het er professioneel uitziet.

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Criterion validity

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Een criterium dat je kiest (je operationaliseert je begrip) om te kijken of je begrip meet wat je wilt meten door het ermee te vergelijken. Dit kan je onderverdelen in voorspellende (predictieve) validiteit wat inderdaad met andere criteria vergelijkt en concurrent validity waarmee je 2 tests op hetzelfde moment afneemt om te kijken of iets overeenkomt of niet.

- Predictieve is dus verschillende tijdstippen vergelijken met verschillende tests en concurrent is dat je ze meteen 2 tests laat maken. Dit zijn 2 vormen van criterion of criteriumvaliditeit

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observer-expectancy effects

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unintentionally communicating the expectation of a research and thereby influencing the subjects behaviour.

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Autism

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Deficit in the ability to form emotional bonds and to communicate with other people

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How is the supposed phenomenon of facilitated comunication by people with autism be explained as an observer-expectancy effect?

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Because the facilitator who was holding the hand of the autist expected him to say certain things so he or she untentionally guided the hand to certain keyboard keys.

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What are two ways in which an observer's expectations can bias results in a typical experiment? How does blind observation prevent such bias?

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By behaving differently towards the groups and by observing things subjectively, example: if you gotta measure smiling and you think one group will smile more than the other, you might interpret ambigue facial expressions as a smile whereas you didnt do interpret it that with with the other group. Blind observation prevents such bias by making sure you don't know which group is the experimental or the controlgroup. This way you cant influence them because you simply don't know.

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Subject expectancy effects

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Subjects who''take a drug'' might think it will help and behave differently. Since this is a problem you can do a double blind experiment where the observers don't know what group has the real drug and where you also give the other group a drug which is a ''placebo'' to make sure that just the perception of taking a drug is not influencing your results.

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3 ethical considerations

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1. The persons right to privacy

2. The possibility of discomfort or harm

3. The use of deception

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The persons right to privacy

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Informed consent and ensuring anonimity is very important to respect this.

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The possibility of discomfort or harm

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Humans must be free to quit at any time and where the experiments might be harmfull it must always outweigh the benefit for humanity and be considered to do these tests in less harmfull ways.

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The use of deception

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This is a big problem. Sometimes studies are made to study a ''belief'' and they need to be told a ''lie'' in order to study it. Some people are against it but as long as people are debriefed about the actual nature after the experiment, may quit at any time and also know that some information cannot be given until the end of the experiment it is usually okay to do such research.

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Animal subjects

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Many procedures that would be unethical with humans are performed with animals.

- The benefits are that those findings might guide us to apply findings in the studies of humans.

- Animals in those research must be well cared for and must have their suffering balanced against the potential value of the knowledge gained.