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Research Methods: Reliability and Validity in Experimental Designs, Exams of Psychology

Harvard UniversityPsychology

An overview of key concepts in research methods, focusing on reliability and validity in experimental designs. Topics include randomization, matched samples, experimenter bias, descriptive and inferential statistics, reliability measures, and validity types. Learn about the importance of these concepts in producing accurate and unbiased research results.

Typology: Exams

2023/2024

Available from 04/12/2024

CarlyBlair
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Statistics (Research & Data Analysis in Psychology)
Exam 1
independent variable -
has at least two levels that we either manipulate or observe (quasi independent) to determine
its effects on the dependent variable
- participants in each level are thought to either display or be exposed to the conditions of this variable
in a consistent manner
- ex: caffeine vs. no caffeine, gender (quasi independent)
dependent variable -
the outcome variable that we hypothesized to be related to, or caused by, changes in the
independent variable
- dependent variables are only in experimental studies
quasi-independent variable -
the variable that has been manipulated, though there was no random assignment into groups
quasi-dependent variable -
the variable that we think was impacted by the quasi-independent variable
characteristics of an ideal experiment -
1. the participants in each of your conditions (groups) are the same
2. all conditions go through the same procedure, except for what you are manipulating
3. sample is representative of population
4. reliable and valid measure of DV
randomization -
assigning participant to conditions with no visible pattern
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Statistics (Research & Data Analysis in Psychology)

Exam 1

independent variable - has at least two levels that we either manipulate or observe (quasi independent) to determine its effects on the dependent variable

  • participants in each level are thought to either display or be exposed to the conditions of this variable in a consistent manner
  • ex: caffeine vs. no caffeine, gender (quasi independent) dependent variable - the outcome variable that we hypothesized to be related to, or caused by, changes in the independent variable
  • dependent variables are only in experimental studies quasi-independent variable - the variable that has been manipulated, though there was no random assignment into groups quasi-dependent variable - the variable that we think was impacted by the quasi-independent variable characteristics of an ideal experiment -
    1. the participants in each of your conditions (groups) are the same
  1. all conditions go through the same procedure, except for what you are manipulating
  2. sample is representative of population
  3. reliable and valid measure of DV randomization - assigning participant to conditions with no visible pattern
  • the best way to assign participants to an experimental condition matched sample - match conditions (groups) based on particular characteristics (e.g. age, income, gender)
  • useful if true randomization isn't possible experimenter bias - if the experimenter is aware of the hypothesis and knows which condition he/she is in, he or she might bias the experiment by acting different in one condition (e.g. smiling more) than in other conditions
  • the individual may not be aware of their biased actions descriptive statistics - information about a sample of everyone of interest in the study
  • summarizes and describes your data
  • ex: mean, standard deviation, range
  • ex: the average grade on the test was 85.4 out of 100 inferential statistics - information about a population based on information from a smaller set of information
  • uses the results of your data to make predictions or generalize about a larger population
  • ex: dancers have a higher IQ than golfers; private school graduates earn more than public school graduates reliability - consistency in measurement
  • your measure gives the same result even if measured at different times, in different ways inter-rater reliability - consistency in scores between observers/measurers
  • aggression questionnaire versus old aggression questionnaire internal validity - validity established if the study produces a single, unambiguous explanation for the relationship between variables
  • sometimes has issues with third/confounding variables external validity - validity established if the study's results can be generalized to the population of interest
  • issues with bias confounding/third/extraneous variables - variables that are in a study that are not part of the hypothesized relationship
  • can cause relationships to emerge
  • can remove a relationship
  • can strengthen a relationship
  • internal validity issue solutions to internal validity issues - standardize your presentation to subjects
  • attempt to control for subject differences when assigning conditions generalizability - the extent that sample performance represents that of the population selection biasing - greater selection probability of specific individuals representativeness issues -

available samples might not be representative of the population

  • ex: college student effect volunteer bias - specific individuals might be more apt to volunteering responses
  • ex: Kinsey effect species issues - ethical concerns might make human research necessary, but how generalizable are the results to the population? experiment design issues - real-world applications
  • sample's prior exposure effects (rats running in previous experiments) reliability and validity - you can have reliability without validity
  • you cannot have a valid measurement without it being reliable
  • if you're measuring the same variable, it's reliability
  • if you're looking at the relationship between two different variables, it's validity nominal/categorical variables - variables that have no numerical meaning
  • values are categories
  • ex: religion (atheist = 1, christian = 2, jewish = 3)
  • ex: gender, favorite ice cream
  • qualitative numerical/quantitative variables -

statistic - the measurable characteristic of the sample of the population that we're interested in

  • a measure of some attribute of a sample
  • samples can be one element or a large collection of elements why not just test a population - size, time, money/expense, and ethical issues what do I need to worry about with my sample? - is it representative of the population?
  • is my sample large enough? statistics - the science of collecting, analyzing, and interpreting data variables - characteristics or conditions that change in values from individuals or situations
  • often arbitrary (e.g. happiness scales)
  • measurements of some variables are not perfectly consistent correlation - relationship comparison - difference change - influence

target population - the group of individuals that are interested in studying accessible population - the group of individuals that have access to in your attempts to conduct an experiment

  • time, location, availability representative sample - an experiment sample that represents the target population biased sample - a sample with characteristics that are different from the target population assignment techniques - methods of distributing participants into different groups of a study random assignment - creating groups by giving each participant an equal chance of being in the experimental conditions/levels convenient assignment - assignment of individuals based on experimenter discretion
  • this can be very bad if it's a biased convenient assignment sampling bias - a method of selection that increases the likelihood of a biased sample
  • ex: grabbing your friends
  • availability bias (taking who will volunteer)

constructs - a hypothetical mechanism or attribute that a researcher is interested in exploring

  • aka variables operational definition - the systematic process of obtaining or measuring a construct levels - the values that a construct can take on based on its operational definition
  • conclusions about theories are limited by the accuracy of the operational definitions being used
  • most psychological concepts already have operational definitions for a lot of the variables that we might want to use, researching your topic is a good thing observational studies - behavioral reports or self-report studies
  • or a combination of those two experimental studies - research experiments designed to discover casual relationships between various factors ways data can lie - measurement error, unreliability of measurement tools, and randomness in the data expected probability - a measure of the actual probability of an outcome if the outcomes were random and repeated many times
  • we are looking to find data that allows us to reject the null hypothesis (accept the alternative) or retain the null (fail to reject the null hypothesis) hypothesis testing -

statistically verifying that the probability of an outcome is so unlikely, that it has to be more than just chance null hypothesis - a statement that implies no effects, differences, or similarities on or between variables within a population of interest

  • basically states that the results were obtained merely due to chance or that there is no relationship
  • we are always trying to disprove the null hypothesis, but we assume its true then find evidence that it is false
  • identified as Ho
  • ex: coin flip exercise, lucky days, hot streaks statistical questions related to the null hypothesis - differences in a variable between individuals and/or groups
  • similarities between variables
  • differences in outcomes based on another variable alternative hypothesis - a statement that implies that the null hypothesis is false (untrue)
  • the opposite of the null hypothesis
  • identified as Ha type I error - null hypothesis is true in reality, but your data leads you to reject it
  • random chance
  • oversensitive tests
  • unethical behavior (intentional demand characteristics, biased scoring, or non-random assignment) type II error - null hypothesis is false in reality, but your data leads you to retain it

class 3

  • reliability and validity and how we can look for and improve upon them in our studies
  • be able to differentiate between the different types of validity
  • be able to describe how the different techniques to examine reliability work and are different
  • know what confounding variables are and how they can impact your studies
  • know how internal and external validity work for studies, how to increase them, and how adjusting one might impact the other class 4
  • know how to calculate the mean, median, and mode
  • know when each measure of central tendency is useful and when each isn't very useful
  • understand the idea of sample size and how it impacts the movement of measures of central tendency
  • understand the idea behind each of the measures of variability
  • know how to calculate out the range, SS, variance, and SD class 5
  • understand the concept of inferentiial statistics and how they play a role in research
  • be able to describe how the target and accessible population relate to each other in research
  • understand the different types of sampling that exist and how they can impact generalizability and/or effects
  • be able to discuss the challenge between the ideals of sampling and the reality of sampling that sometimes follows
  • understand how different assignment techniques help researchers find unbiased effects or could cause biased results in the study
  • know the logic and terms associated with inferential st variability - the fact that levels of variables obtained through measurement often differ from one another
  • aka spread
  • variation in the data

good variability - individual differences (variation due to the participants themselves) bad variability - measurement error and unreliability measurement error - variation due to the inability to measure something accurately unreliability - variations due to differences in responses to the same situation

  • ex: teenage popularity measures of central tendency (MCT) - tell us about the middle of the distribution, or the point(s) around which the distribution is centered
  • mode, median, mean mode - most frequently occurring variable/score in a group
  • there can be multiple modes
  • works with nominal, ordinal, interval, and ratio data median - the point in the distribution where 50% of the scores are lower and 50% are higher than that point
  • the middle from least to greatest
  • average of two numbers if there are two middle scores

standard deviation - sx = โˆš(sx^2)

  • = โˆšโˆ‘(xi - xbar)^2/n
  • = โˆšssx/n outliers - extreme values
  • always relative to the rest of the distribution
  • a data point that drastically skews all the measures of central tendency
  • mean is affected by outliers
  • median and more are resistant to outliers
  • variance and standard deviation are very sensitive to outliers because they are based on the mean