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Flashcards by Darcey Griffiths, updated 8 months ago
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Created by Darcey Griffiths
8 months ago
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| Question | Answer |
| Longitudinal vs cross sectional studies- longitudinal | Longitudinal studies are carried out over a long period of time in order to observe long term effects of 'x' on a specific behaviour eg age related changes in behaviour- case studies are often longitudinal (but not always) eg Bowlby |
| Longitudinal strengths vs weaknesses | Strengths No issue with individual differences- uses repeated measures- same person tested on no. occasions developmental tends can be spotted easily as tests are repeated at regular intervals over many years- helps understand order in which events may occur Weakeness Attrition- people may move away or drop out over the course of the study- disrupts study- those that drop out= more likely to have particular characteristics- eg unmotivated, mental illness) Ppts more likely to become aware of research aims- repeated measures- demand characteristics Long time/ expensive compared to cross section |
| Cross sectional studies | One group of ppts representing one section of society are studied and compared with ppts from another group at the same time eg group of 5, 6 and 7 year olds could be studied at the same time to measure cognitive development Cross sectional studies may look at how other things impact behaviour rather than the effects of timeeg the behaviours of different professional groups- teachers, doctors, lawyers etc |
| Correlational strengths vs weakenesses | Strengths-cheap and quick- only need to be tested once- no follow up needed- no risk of attrition unlike longitudinal Ppts more easily obtained- less pressure than w/ cross sectional than there is for them to stick w/ longitudinal Weakenesses PPt variables- ppts may differ in more ways than behaviour being investigated - not an issue w/ longitudinal- repeated measures - Harder to identify/ analyse developmental trends -Cohort effects- group of people who are same age may store characteristics/ experiences eg covid- only consider one cohort= may not be generalisable |
| Brain scans- intro- before scans/EEG | Main focus of understanding human behaviour= brain-In past- only way to study brain was through post mortem examination eg Broca- able to identify part of brain involved in language EEG- in 1950s the only method available for studying brain activity was EEG - electrodes are placed on scalp and electrical activity in different regions of the brain can be recorded- EEG was used in class study by Dement and Kleitman to detect different stages of sleep from ppts brain waves |
| CAT scans | Involve taking a series of X rays and combining them to form comprehensive 2 or 3 dimensional picture of the area being scanned- usually dye is injected into the patient as a contrast material- then he or she is placed in the cylindrical CAT scan machine that takes the pictures |
| CAT scans- advantages/ disadvantages | Advantages- useful for revealing abnormal structures in the brain eg tumours or structural damage- high resolution- much higher than x-rays of bones, soft tissues and blood vessels Disadvantages Require more radiation than traditional x-rays & the more detailed and complex the CAT scan - the more radiation exposure. Only provide structural information - not electrical activity of the brain |
| MRI/ fMRI scans | MRI- Use of magnetic field - Use powerful magnets (strong magnetic field) that forces atoms in the body to align with that field when magnet is turned on. When magnet is turned off - emit various radio signals as move back to original position- detector reads signals to map structure of brain. USE: identify structures e.g. brain tumours Functional MRI (fMRI) provides both anatomical and functional information by taking repeated images of the brain in action as it allows blood flow in different parts of the brain to be tracked- areas with higher blood flow will appear brighter USE: Study brain function in real time |
| MRI/ fMRI advantages/ disadvantages | More detailed image of the soft tissue in the brain than CAT scans Best suited for cases when a patient is to undergo the examination several times successively in the short term - does not expose patient to the hazards of radiation like CAT scans. Disadvantages Time consuming - MRI scans take a long time Uncomfortable for patients Not suited for everyone eg claustrophobia |
| PET scans | Administering slightly radioactive glucose (sugar) to the patient. The most active areas of the brain use glucose, and radiation detectors can ‘see’ the radioactive areas, so building up a picture of activity in the brain. USE - imaging tumours |
| PET- advantages/ disadvantages | Advantages Reveal chemical information that is not available with other imaging techniques - can distinguish between benign and malignant tumours. Can show the brain in action - useful for psychological research. Disadvantages Extremely costly technique and therefore not easily available for research. Patient has to be injected with radioactive substance, the technique can be used only a few times. Less precise than MRI scans |
| Distribution overview | y axis= frequency, x axis= item of interest If we are plotting frequency data, we may start to see patterns arise in a data =distribution. Data can be "distributed" (spread out) in different ways - distributions are generally classed as normal or skewed. normal, positive (right) skewed, negative (left) skewed |
| Normal distribution | Most results cluster around the mean A few very high and a few very low. Often called a bell curve Mean, mode and median are equal and at the exact midpoint Many things closely follow a Normal Distribution:heights of people, blood pressure, marks on a test IQ, height, weight and blood pressure. The distribution is symmetrical. |
| Positive skew | Not all data will be a normal distribution - scores are not equally distributed around the mean. Data may cluster around high or low scores. When this is the case, the mode and mean do not fall in the centre of the distribution curve. Positive= A few extreme high scores, a skew towards low scores. Long tail on the right - positive side of the peak. The mean is greater than the mode. Example: how long people admitted to hospital stay before being discharged. Very few patients stay for an extended period. Most are discharged after a few days, meaning the distribution is skewed to the right. |
| Left skew | A few extreme low scores A skew towards high scores. Long tail on the left - negative side of peak The mode is greater than the mean. Example - scores on a very easy exam, few people got a low mark. In this situation, people would obtain a higher score and therefore, most scores would sit to the right side of the x axis with fewer scores sitting to the left side of the x axis. |
| Brief recap- inferential statistics | Nominal: You can categorise your data by labelling them in mutually exclusive groups, but there is no order between the categories. eg Gender, Ethnicity, Car brands Ordinal: You can categorise and rank your data in an order, but you cannot say anything about the intervals between the rankings- Although you can rank the top 5 Olympic medallists, this scale does not tell you how close or far apart they are in number of wins. |
| brief recap- levels of measurement | You can categorise, rank, and infer equal intervals between neighboring data points, but there is no true zero point. The difference between any two adjacent temperatures is the same: one degree. But zero degrees is defined differently depending on the scale – it doesn’t mean an absolute absence of temperature. The same is true for test scores and personality inventories. A zero on a test is arbitrary; it does not mean that the test-taker has an absolute lack of the trait being measured. You can categorise, rank, and infer equal intervals between neighboring data points, and there is a true zero point. A true zero means there is an absence of the variable of interest. In ratio scales, zero does mean an absolute lack of the variable. For example, in the Kelvin temperature scale, there are no negative degrees of temperature – zero means an absolute lack of thermal energy. |
| why use inferential statistics | Inferential statistics allow us to state whether our results are meaningful , or just due to chance. We say that results that are not due to chance are significant. If significant we accept the alternative and reject the null. |
| deciding which test | Test of difference: Nominal Unrelated (independent groups) design -chi squared Nominal Related (RM/ matched) design- sign test (AT LEAST) Ordinal Unrelated design- Mann Whitney Ordinal Related design- Wilcoxon Correlation Nominal- Chi Squared Correlation Ordinal- Spearman's rho |
| Inferential statistics- calculated value | All statistical tests end with a number – the calculated or observed value. This number is crucial in determining whether the researcher has found a result that is statistically significant (suggests a difference - cause and effect) or results have occurred by chance. Statistical tests determine which hypothesis is true – can we accept the alternative hypothesis? Or do we have to accept the null? RESULTS SIGNIFICANT - Accept Alternative Hypothesis RESULTS NOT SIGNIFICANT - Accept Null Hypothesis |
| Probability | Probability = likelihood that results are due to a real difference/correlation. Can never be 100% sure that difference/correlation isn’t due to chance (extraneous/confounding) Psychology - accept a probability value of 95% where results are due to chance in a maximum of 5% of cases. This converts to a significance level of 0.05- 5% probability that it has occurred by chance (Written as P≤ 0.05) , 95% confidence that our IV has caused our DV. Significance level - tells you the margin of error that could occur in your results e.g. 0.05 = 5% probability results are due to chance whereas 1 = 100% are due to chance. |
| Probability p2 electric boogaloo | Too strict - unlikely to get a significant result Too lenient - results would be less meaningful A more stringent level of significance may be used (0.01) in studies when there may be human cost EG drug trials. In all research if there is a large difference between the calculated and critical values, the researcher will check more stringent levels as the lower the p value the more statistically significant the result. |
| Correlation coefficients | Strength and direction of relationships in a correlation 1/-1= strongest correlation both ways 0= weakest correlation Spearman's rank order correlation coefficients produce Rho which is the observed value of correlation- closer Rho is to 0= weaker correlation- more likely results are to be statistically significant when compared to critical values table |
| observed and critical values | Each inferential test involves taking data collected in a study and doing some calculations to produce a single no. called the calculated value - for Spearman's/ Mann whitney- test statistic= Rho and U Rhop or U value - these values are called observed values and sometimes calculated value as it is based on observations/ value calculated |
| observed and critical values p2 | To decide if observed value is significant- calculated value is compared to critical value- different tables of critical values for each different statistical test critical value= number that a test statistic must reach for the null hypothesis to be rejected- to find critical value- need to know df= usually get by looking at no.ppts in |
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