Study Guides (238,408)
Canada (115,131)
Psychology (63)
PSYO 2000 (2)

EXAM 2.docx

25 Pages
Unlock Document

Dalhousie University
PSYO 2000
Simon Gadbois

Sensitivity measures should be sensitive enough to detect differences in a characteristic that are important to the investigator Specificity measures should be specific to the characteristics, group, etc. -too general? Accuracy based on precision of the measurements -good accuracy if averaged values = standard -standards are rare in bio science (behavior) -accuracy = specificity + sensitivity (signal detection theory) -function of systematic error/bias -extraneous/confounded variables are a source precision consistency, reliability of the data reliability consistency precision validity does a variable represent what it intended systematic error errors solution -observer/experimenter = blinding -subject/participant = blinding / unobtrusive measures -instruments/apparatus = calibration -blinding – experimenter and participants do not know the assignment of treatments and control groups random error influence of the precision of measurements (consistency and reliability) -unpredictable fluctuations -participants -experimental conditions -methods -measures TRUE RANDOMNESS sources of random error observer/experimenter reliability YOU participant/subject reliability doing what they need to be doing instrument/apparatus reliability how to asses precision measures of variability – descriptive stats standard deviation – of repeated measures coefficient of variation (sd/mean) X 100 measures of concordance correlation coefficient consistency of results of paired measurements -index of concordance -need enough data other tools- kappa, cronbach alpha reliability/consistency -consistent results over repeated measurements -reliability refers to the precision of your measures assessment methods test-retest reliability/consistency – stability of test scores over time -repeated studies alternative (parallel) forms use of two lists in a memory test may not be easy -if scores are different something is not equal (in some ways the same) -same level of difficulty – the frequency (high frequency in kids-cat, dog, low in adults) how abstract the word is word length – in processing time effective value, emotional value -recognition/recall internal consistency how consistent are the measures across items intended to measure the same concept -split-half reliability/consistency inter-rater reliability between consistency of recording and scoring betweenALL OBSERVERS (with an inter-observer reliability measure such as in index of concordance, kappa coefficient, Kendall coefficient) intra-observer/rater within consistency each observer, individuality, records, interprets, or identifies SIMILAR behaviors or events in the SAME WAY (with an intra-observer reliability measure) -impression change validity and reliability -a measure can have high reliability but no or low validity -cannot be more valid than it is reliable internal validity measures what it is suppose to -associated with the criteria for analytic experiments -no confounded variables -controlled variables -appropriate control groups -random selection sampling -random assignment (randomization) external validity generalization potential -determines the application and implications of an experiment -psychologists have low ecological validity generalize data to… -species -environment -culture -age groups -conditions criteria for external validity have the potential to influence internal validity population selection –converging evidence from different population and a representativeness of the sample example abusive drink different in France and Canada (different population) -sometimes you want a certain population type operational definitions- agreement on definitions parameter values – the values you select for each variable in your experiment should be well defined applies to control variables and independent variables demand characteristics – cues in a research produce that influence the behavior of subjects are absent or minimized -implies what experiment is about subtypes of validity face- how well the test appears to measure what it is designed to measure -it is a plausible measure of the variable we want to estimate -non scientific content-how adequately the measure address the representatives of the measured event -expert opinion can determine this construct –a measure of how well a test asses some underlying (theoretical) characteristic -depends on the operational definitions criterion –how the assay agrees with others supposed to evaluate the same event on the same criterion -based on criteria you use and the type of evaluation concurrent –how well an assay estimates a criterion in relation to another event or group of subjects at the same point -parallel in time convergent –the methods of measurement diverge upon one discriminant –methods of measurement diverge upon one another and the divergence is expected predictive –how well an assay predicts a even on a time criterion -measures predicts the future states external- applies well to other people, settings, conditions, etc. diagnosis and diagnostics: clinical psychology and neuroscience diagnosis validity idea that trying to diagnosis or detect something, use a different kind of reasoning – sensitivity and specificity content validity- refers to symptoms and diagnostic criteria concurrent validity – associated correlates or markers and response to treatment in therapy predictive validity- refers to diagnostic stability over time discriminate validity- can it discriminate between disorders –comorbidity predictor variable- test result outcome –presence or absence of disease values can be dichotomous –positive or negative / yes or no categorical –scale, 4++++, 3+++,2++, 1+, 0- continuous- milligrams of glucose ore deciliter observational vs diagnostic observational association between a predictor and outcome variable diagnostic detection or discrimination between yes and no, more than an association test phase what constitutes a positive test results example of the necessity of a pilot study, it is crucial in diagnostic studies validation phase assess the tests sensitivity and specificity -use a new sample of subjects detailed steps 1. asses the need for a new diagnostic tool 2. selection of subjects and samples 3. identify a reasonable gold standard for comparison 4. design standardized and blinded trails 5. estimate sample size for accuracy 6. find subjects 7. report accuracy sensitivity and specificity data true positive= positive and present false positive= positive and absent false negative= negative and present true negative= negative and absent sensitivity = TP/TP+FN specificity = TN/FP+TN -the best test has low FP and low FN -reality accuracy is a trade off between specificity and sensitivity implications decisions: asses the implications or impact of the two possible errors in sensitivity – you have determined a cutoff point example brain tumors -if the surgery is dangerous and potentially lethal – you want to avoid FP -if the cancer is dangerous and potentially lethal you don’t want to miss it- you want to avoid FN Bayesian theorem - links the degree of belief in a proposition before and after accounting for evidence – prior knowledge influences decision Likelihood ratios- involves the odds -that a person with a disease would have a particular test result divided by the likelihood that a person without the disease would have that result -very powerful when combined with the prior probability of a disease – base rate judgments depend on this- Bayesian analyst -applies mostly to categorical and continuous data d’values, criterion, and ROC curves -covered previously (signal detection theory) -applies mostly to dichotomous data prevalence how rare the disease is in the population -if low, test needs to be specific -example HIV andAIDS - -if high, test needs to be sensitive -example high blood pressure prior probability that a specific patient has the disease –estimate before testing’’ -LOW –prior probability of heart disease in young, healthy, nonsmoker -HIGH – old, high BMI, smoker drinker -called the base rate predictive value or posterior probability -positive test -likelihood of a true positive /likelihood of TP + FP or Sensitivity X Prior probability [Sensitivity X Prior probability]
+ [(1-specificity) X (1- prior probability)]
 - negative test -likelihood of TN/ likelihood of TN + FN or Specificity X (1- Prior probability) [Specificity X (1-Prior probability)]
 + [(1-sensitivity) X Prior probability]
 errors in diagnostic studies random error>>>chance>>>quantify it with CI for specificity and sensitivity systematic error>>>biases -sampling biases -measurement bias -reporting bias binary response system 1=yes 0=no s=signal/stimulus statistical errors -no error type II error beta type I error alpha hypothesis testing H not- reject false = no error -accept false= type II error -true reject =type II error -accept true= no error summary -type one errors akin to false alarms or false positives -type 2 errors are akin to misses or false negatives experimental designs factorial – more complex -when more than one independent variable is used (independent variable= factor) more then A and B, ex. ABCD -bivalent or multivalent -each variables has 2 or more levels -ANOVA(or analysis) variance are needed for the data three main types within – all subjects go through all the conditions (one group of subjects) -also called repeated measures designs or randomized block designs -for t-tests “t-tests for dependent groups”, or paired t-test “related t-test” or “correlated t-test” -type 1: the same subject is observed under all treatment conditions -type 2: the same subject is observed before and after a treatment (pretest-posttest design) -type 3:subjects are matched on a subject variable (organismic variable or individual differences variable) -then randomly assigned to the treatments -this is a between subject design requiring a within subject analysis of variance advantages -each level of the independent variable is applied to al subjects -can evaluate how each level of independent variable affects each subject -each subject is its own control -excellent for assessing experiments on learning, transfer of training, practice effects -may help increase statistical sensitivity or statistical power –want more power, increase the number of subjects -subjects are not divided into groups, all subjects are involved in all conditions disadvantages -practice effects –if not the focus of study, it becomes a problem solution- appropriate counterbalancing procedures can counteract practice effects -also make the treatment order an independent variable -differential carryover effects- lingering effects of one or more treatment conditions -often a issue with drug studies solution- recovery periods (intervals) -violation of statistical assumptions-covered in inferential statistics course solution – use a more strict significant level practice and carryover effects -practice/learning: increase in performance via practice -sometimes considered a carryover effect -not necessarily a problem in some experiments -carryover effects -fatigue –decrease performance with time -contrast- treatments are compared by subjects -habituation (decrease) or sensitization (more reactive to it)– psychological effect -adaption – physiological effect- tolerance in drugs between – all different animals /participants -match the groups so they are all similar - type 1: completely randomized design ­ type 2: matched groups designs ­ for t-tests “t-test for independent groups” or “unpaired t-test combination of within and between mixed (between and within) factorial design -within and between components -also called split-plot designs -analysis: SPANOVA nested- factorial design try to avoid correlational- experimental quasi-experimental – may not be able to randomize subjects, no control group ex-post facto – medical world (clinical) large number of studies and participants. Did not plan it, and naturally occurring events and then plan it after it happens developmental designs –to see change in individuals in each group descriptive stats – important in describing the data -measures of central tendency -arithmetic mean –average - If there s a zero cannot use them. Use them for ratios -harmonic mean -geometric mean -mode -median -measures of variability – need to specify what measures you are using because numbers are different -standard deviation -standard error -confidence intervals -least significant difference -coefficient of variation -range -inferential statistics –confirming or not the hypothesis -using t-tests, ANOVAetc. -visual data- its not about numbers, its about what it looks like compared to the means and averages -significant differences? Effect size- very important –how strong the effect is within that group Interactions Interactions between variables: there is an interaction between two variables if the effect of one independent variable changes with different values of the second independent variable -the sleep study with melatonin and phototherapy treatments is a good example of a factorial design with two independent variables (factors) potentially interacting general information ­ the factious examples given below will now assume that a completely randomized factorial design was chosen ­ different participants are in each treatment condition ­ each group of participants is independent of every other group ­ 2X3 factorial design perfect parallel line –can conclude that there is no interaction mild interaction – trend is the same -the lines not being perfectly parallel (but close) suggest a mild interaction -possibly uncertain if it is significant strong interaction – treatment helping one group a lot -impact of one factor over the other -variance (ANOVA) will determine the significance of the interaction problem of interpretation – the main issue -dealing with 2 independent variables -this is why you limit yourselves to 3-4 variable factors -be parsimonious in choices – more likely to be straightforward complex designs – complex interactions -have to be able to explain the data –significance difference and interactions -if two lines are straight across- one main effect (therapy), and no interaction -two lines are bent in the same way – two main effects, and no interaction -two lines are on top of each other- one main effect (drug), no interaction -two lines are mirrored – two main effects with an interaction -two lines are mirrored and not touching- 2 main effects and an ordinal interaction -two lines are mirrored and cross each other – 2 main effects and a disordinal interaction antagonistic interaction -cancel out the main effect -significant interaction, no main effects (masked by the interaction) -significant interaction with equal means – no main effect -significant interaction, no main effects, disordinal interaction additive- interactive- -limit the number of levels for each factor -limit the number of factors Specialized designs Quasi experimental designs and ex post facto design Pre experimental- designs with no control groups any random assignment Acceptable for pilot research – if not avoid Experimental – control groups and random assignment ( not always) – when you take a random sample from the population it is hard to do it perfectly random (subjective idea) Quasi experiment- often control groups but no random assignment Quasi independent variables Ex post facto – special case of between subject design – extract an association or correlation between variables and not causation Often take advantage of naturally occurring events -lack of control over variables issues of quasi do not fill in the requirements for a true experimental (analytic) experiment -internal validity -external validity most likely issue -random selection is not possible or not respected (over looked as criterion) example: within subject design -useful when statistical power is an issue -useful when few measurements are possible -useful when availability of participants is not possible -typically random assignment of participants is not possible -ethical -a common mistake – pooling fallacy jet lag study jet lag travel east to west traveling as opposed to non jet lad north to south -melatonin treatment – to induce sleep -measures-rating scale on quality of sleep (onset, duration, restfulness) -issue- finding participants often involved in jet lag traveling willing to be part of this experiment -control group? Hard to find (maybe who travel in the same time zone) participants only based on: -duration and distance of travel- number of time zones -departure and arrival times -direction – east to west or west to east -sleep pattern strategy before leaving trip (sleep deprivation or over sleeping) solution -use a within subject treatment -all subjects go through of the experimental conditions -potential problem – order of effects counter balancing - main effect- is there an effect of just one on its own -ex post facto design -researcher arrives after the fact -nature has implemented the treatment group -not controlled by experimenter (dead or alive) must have 2 things -different environments -different dispositions -combination of the two people participants can not be ethical -so find some who are already sick for example and study their last 10 years, or someone who is dead -prospective -cause to effect -healthy people (in a low and high stress job for example) and wait 10 years and compare (longitudinal) -look at the frequency and compare -cohort design -retrospective -effect to cause -sample 100 people with cancer and 100 health people -investigate their past -compare what chronic stress -case control designs or criterion group designs characters of EPF designs -participants selected after the fact -no random assignment -cofounded variables typical to the groups investigated -selection of participants must be to the criterion applied -convince sampling is problematic -some have confounding variables internal validity issues -no random sampling -no random assignment -confound variable typical to the groups -selecting of subjects becomes a complicated and strict criterion and must be applied -convenience sampling is problematic – may be confound external validity issues -choice of groups MATCHING solutions Matching – making sure that the 2 groups do not differ one nay other except the one selected to study (low or high) Matching on: diet, smoking, drinking, pescprtion, etc. -subject to subject matching -distribution for distribution matching – descriptive statistics MEASURE the variables solution -the idea- try to identify the possible confounding variables and measure them -can determine if the variables are confounded or not and predict their effect -multivariate methods retrospective EPF design -very common in health research -backward information -comparative advantages -smaller number of participants -shorter time period -less money -same internal and external issues -specific issues -present context motivating the investigation -detection bias -with additional historical issues in diagnosis including awareness -likely made by different people -differential assessment on groups =less extensive records in the health group some specific issues with retrospective EPF designs ­ present context motivating the investigation ­ detection bias o with addition of historical issues in diagnosis (related to progress in the ability to diagnosis some disease) – including awareness ­ diagnosis was likely made by different parties (same criteria?) ­ differential assessment or perspective on stress between – healthy and cancer group ­ less extensive records in the healthy group solutions ­ same options as for the prospective EPF designs o measure o matching ­ in all cases matching or identification of measureable variables is more difficult in this case as the design requires you to search back in time e ­ you rely on the memory of the subjects, their family and friends, and their physicians, including the accuracy and completeness of the medical records change -studying changes in time of structures and processes -ontogenetic change- developmental research -change in the individual neuron, brain, animal, person -historical change- cross generation research -changes during across generations -phylogenetic change- evolutionary research -changes in species and populations, speciation, natural selection specific needs for specific areas ­ this section introduced different research strategies ­ more exists depending on your specific field of study ­ ex. longitudinal or cross sectional longitudinal disadvantages -test conditions become well known by child -are performance and behavioral changes die to experience or maturation -can take a lot of time and money cross sectional disadvantages -individual differences can account for some effects (not age) and normal differences in developement solution- hybrid version – cohort sequential design or cross sequential comparative evolutionary common in -comparative and evolutionary neuroscience -comparative and evolutionary psychology -ethology, behavioral etc. -cross cultural -linguistics -guiding principal – compare and contract -theoretical foundations – natural selection and or culture -comparative- synchronic (static in time) -evolutionary – diachronic (history) combining within and between manipulation -mixed designs or slip plot designs -most common cases of mixed within and between subject manipulation -nested designs or hierarchical designs -more economical than mixed designs -less information (some interactions cannot be evaluated) nested designs: between example comparing fish from clean and contaminated water objectives -the nested design allows us is to test 2 things -difference between control and study areas (quasi- experimental) -the variability of the sites within areas (sites 1,2,3 in control, and 4, 5,6, in the study) -if we fail to find a significant variability among the sites within areas then a significant difference between areas would suggest that there is an environmental impact in other words, the validity is due to differences between areas and not to variability among the sites -but in this kind of research (environment and species) it is likely that you will find validity within the sites (within an area) -but, even if you find significant difference between the site you can still test to see whether the difference between the areas is significantly larger than the viability among the sites with areas nested designs: mixed time is example comparing spring and fall movements of snakes it depends what you are trying to accomplish -nest tasks -nest groups -nesting locations, sites, areas -nesting times (years, seasons, months, days etc.) time series designs- a lot of precision -follow the change in the system over time -show how dynamically the system changes observations and time basic time goal: compare before and after the treatment by cell or collapsing interrupted time similar but with naturally occurring events example The “baseline” (before T )5 On T th5 event (naturally occurring) or treatment (experimental induction) is introduced. Observations or measures inT to6 are9influenced. Variation- is to give a treatment early, but then cancel it and see the effects -follow the typical ABAdesign (that will be described in the next chapter) -Atreatment B no treatment A non equilivant control group design -similar to pervious designs -two independent groups -one control and one experimental -make sure both groups are similar and comparable –matching is a solution pretest/posttest designs true experimental designs, similar to a within subject design -two levels pre and post =problem and solution -carryovers and possibility o counterbalance -how to increase internal validity? -control groups (necessary) -random assignment of subjects to conditions (if possible) design 2 mixed design (2X2) – within treatment and no treatment is the between factor -group one- pre test treatment and posttest -group two – pre test and post test design 3 -other problem – effect of having experienced there test two solutions -eliminating the pre test completely simple two group experiment -better solution four group design- partial removal tests this helps you evaluate the effect of pretest (if any) -group 1 – pre test and treatment and posttest -group 2 – pretest and posttest -groups 3 – treatment and posttest -group 4 – posttest (pretest removed for groups 3 and 4) measuring patterns and dynamic processes: behavior and neurophysiology observational methods quantitative -quantified and systematic observations: objective (frequency, duration, etc.) -our focus qualitative -qualified and interpretive: subjective (descriptive (verbal) accounts) -not addressed in this course measuring.. events (dynamic): electrophysiological activity, behavior, hormonal release, etc. states (static): signs, symptoms, postures, traits, etc. (binary information- yes/no, on/off, presence/absence, etc.) -both at the same time types of observations research naturalistic (field, can be in a lab) systematic (field or lab) describing dynamic processes: behavior or physiology ­ studying the causes or the triggers of specific processes ­ studying the process itself (events, actions): spatial and temporal structure, expression, etc. ­ studying the consequences (effects, reactions) of the process on the organism, the environment, and (other) individual, cells, tissue, etc. ­ finding a pattern will help with prediction the s
More Less

Related notes for PSYO 2000

Log In


Don't have an account?

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.