Research ProcessResearch Process

Parts of the research studyParts of the research study Aim: purpose of the studyAim: purpose of the study Target population: group whose Target population: group whose

behaviour is investigated behaviour is investigated Procedure: step-by-step process used by Procedure: step-by-step process used by

researcher to carry out the studyresearcher to carry out the study Findings: states how the researcher Findings: states how the researcher

interpreted the data collectedinterpreted the data collected

Research Research

Measurement and SamplingMeasurement and Sampling How to describe what we observe is How to describe what we observe is

the main question.the main question. The use of ratio variables The use of ratio variables

(measurements based on a continuous (measurements based on a continuous scale with an obvious zero point)scale with an obvious zero point)

Researchers want measurements to be Researchers want measurements to be reliable and validreliable and valid

MeasurementsMeasurements

Sampling and population concernsSampling and population concerns Researchers must be concerned that a Researchers must be concerned that a

sample is representative of the populationsample is representative of the population Researchers will use random sampling and cross-Researchers will use random sampling and cross-

sectional sampling for the best results (the sectional sampling for the best results (the difficulty lies in making the random sample difficulty lies in making the random sample representative)representative)

Other problems with samples are: self-selected Other problems with samples are: self-selected samples; convenience samplessamples; convenience samples

Cross-sectional sampling is a deliberate selection Cross-sectional sampling is a deliberate selection of subjects to make the sample representativeof subjects to make the sample representative

Opportunity Sampling? Okay to use? What Opportunity Sampling? Okay to use? What about for you as an IB student?about for you as an IB student?

Research designResearch design

HypothesisHypothesis

Research methodResearch method

ObservationsObservations

VariablesVariables

All of the aforementioned are part of All of the aforementioned are part of the research design. the research design.

Pitfalls in Experimental Research Pitfalls in Experimental Research

Internal validity: maintaining consistent Internal validity: maintaining consistent conditions in the experimental situation, conditions in the experimental situation, and ensure no unwanted factors creep and ensure no unwanted factors creep inin

Confounds: a situation where two variables Confounds: a situation where two variables change simultaneously making it impossible change simultaneously making it impossible to determine their relative influenceto determine their relative influence

Ways to avoid confoundsWays to avoid confounds1.1. Hold constant factors which are not of direct interestHold constant factors which are not of direct interest

2.2. Use multiple independent variables when the variables Use multiple independent variables when the variables involved are of direct interestinvolved are of direct interest

Pitfalls in Experimental ResearchPitfalls in Experimental Research

Bias: systematic errorBias: systematic error Subject biasSubject bias Experimenter biasExperimenter bias These situations can be avoided by using These situations can be avoided by using

single-blind and double-blind designs single-blind and double-blind designs (deception)(deception)

Observation to InterpretationObservation to Interpretation

StatisticsStatistics Concerned with the description and Concerned with the description and

interpretation of scientific datainterpretation of scientific data Used to describe and summarize results Used to describe and summarize results

Descriptive statsDescriptive stats Assist in understanding what the results Assist in understanding what the results

meanmean Inferential statsInferential stats

Descriptive StatsDescriptive Stats

Frequency distributionFrequency distribution Rearranging the scores in order of size Rearranging the scores in order of size

and then see how many people got each and then see how many people got each scorescore

Can provide a clearer picture of what Can provide a clearer picture of what data looks likedata looks like

Descriptive StatsDescriptive Stats

Central TendenciesCentral Tendencies Mode: most frequently occurringMode: most frequently occurring Median: the middle of the frequency dataMedian: the middle of the frequency data Mean: sum of all scores divided by the number Mean: sum of all scores divided by the number

of scoresof scores Normal and skewed distributionsNormal and skewed distributions

Bell shaped curve is a normal distribution; highest Bell shaped curve is a normal distribution; highest point occurs in the middle of the distributionpoint occurs in the middle of the distribution

Lopsided distribution is skewed, the mean is unlikely Lopsided distribution is skewed, the mean is unlikely to be representative of the majority; in most cases, to be representative of the majority; in most cases, the researcher will prefer the median as a way of the researcher will prefer the median as a way of describing the typical resultdescribing the typical result

Measures of VariabilityMeasures of Variability

VariabilityVariability tells us how the scores tells us how the scores are distributed around the centerare distributed around the center One indicator is the One indicator is the rangerange from lowest from lowest

to highestto highest The next indicator is to find the The next indicator is to find the

deviation scoresdeviation scores Squaring the deviation scores will give Squaring the deviation scores will give

the the variancevariance, however, this gives , however, this gives inflated datainflated data

Finding the square root of the mean of the Finding the square root of the mean of the variance will give the variance will give the standard deviationstandard deviation

Standard DeviationStandard Deviation

The standard deviation will provide a The standard deviation will provide a measure of variability which reflects measure of variability which reflects the position of every score within the the position of every score within the group, expressed in the same units group, expressed in the same units as the original scores.as the original scores. The larger the standard deviation, the The larger the standard deviation, the

greater the variability of scores greater the variability of scores

Normal DistributionsNormal Distributions

What do they tell researchers?What do they tell researchers? Knowing something is distributed “normally” Knowing something is distributed “normally”

tells researchers specific things, that most tells researchers specific things, that most scores are near the mean and that very few scores are near the mean and that very few scores are away from the center (very little scores are away from the center (very little standard deviation)standard deviation)

(Look at page 449 in your handout, figure A.6)(Look at page 449 in your handout, figure A.6) Knowing these properties of normal Knowing these properties of normal

distributions becomes very useful in making distributions becomes very useful in making predictions about scores and in our ability to predictions about scores and in our ability to interpret the results of researchinterpret the results of research

CorrelationsCorrelations

Any relationships between variables are Any relationships between variables are correlationalcorrelational and do not directly identify and do not directly identify causal factorscausal factors

Correlation or Causation Two types of correlational patternsTwo types of correlational patterns

1.1. Positive: occurs when increases in one variable Positive: occurs when increases in one variable are associated with increases in the other variableare associated with increases in the other variable

2.2. Negative: occurs when increases in one variable Negative: occurs when increases in one variable occur as the value of the other variable decreasesoccur as the value of the other variable decreases

(turn to page 453 in the handout, figure A.8)(turn to page 453 in the handout, figure A.8)

CorrelationsCorrelations

Correlational patterns are measured Correlational patterns are measured using a statistical measure called a using a statistical measure called a correlation coefficientcorrelation coefficient This is a number between 0.0 and +1.0 This is a number between 0.0 and +1.0

for positive correlations; -1.0 and 0.0 for for positive correlations; -1.0 and 0.0 for negative correlationsnegative correlations

As the value moves from 0 to the As the value moves from 0 to the maximum the degree of the relationship maximum the degree of the relationship between the variables becomes strongerbetween the variables becomes stronger

Inferential StatsInferential Stats

Inference: a logical conclusion based Inference: a logical conclusion based on what I know on what I know

““In using inferential stats, we try to In using inferential stats, we try to generalize from our sample to the generalize from our sample to the population.” (Glassman and Hadad, population.” (Glassman and Hadad, 2004)2004)

Sampling and VariabilitySampling and Variability Sampling and VariabilitySampling and Variability

The recognition that not all samples will be alike The recognition that not all samples will be alike and that any sample may differ from the and that any sample may differ from the population is the result of population is the result of sampling variability sampling variability

Therefore, nothing is set in stone. All research Therefore, nothing is set in stone. All research has sampling variability, as a consequence, has sampling variability, as a consequence, more research should be done with other more research should be done with other samples to prove theorysamples to prove theory

There are many questions that arise when There are many questions that arise when doing research, especially when a doing research, especially when a researcher is trying to interpret data. researcher is trying to interpret data.

““Inferential statistics are concerned with Inferential statistics are concerned with providing guidelines” for evaluationproviding guidelines” for evaluation

Inference with Normal DistributionsInference with Normal Distributions

The simplest situation for inferenceThe simplest situation for inference Looking at a single score in relation Looking at a single score in relation

to a set of datato a set of data

SignificanceSignificance

Results which are interpreted as Results which are interpreted as based on a real effect are referred to based on a real effect are referred to as as significantsignificant.. The statistical tests for evaluating the The statistical tests for evaluating the

chance versus the real effects are called chance versus the real effects are called significance testssignificance tests

““The conclusion one draws, expressed The conclusion one draws, expressed as the probability that the outcome is as the probability that the outcome is due to chance, is called the due to chance, is called the significance levelsignificance level of the results.” of the results.” (Glassman and Hadad, 2004)(Glassman and Hadad, 2004)

““Inferential stats use sample Inferential stats use sample data to try to make inferences data to try to make inferences

about a population which cannot about a population which cannot be known directly.”be known directly.”

(Glassman and Hadad, 2004) (Glassman and Hadad, 2004)

Null HypothesisNull Hypothesis

The The null hypothesisnull hypothesis always asserts always asserts that only chance is at workthat only chance is at work If a researcher can prove the null If a researcher can prove the null

hypothesis incorrect, it becomes more hypothesis incorrect, it becomes more likely the researcher is likely correctlikely the researcher is likely correct

Significance tests lead to Significance tests lead to probability, probability, ergo statistical ergo statistical inference is always a matter of inference is always a matter of probabilities, probabilities, nevernever certainty. certainty.

Standard of ProbabilityStandard of Probability

Commonly accepted standard is 5 in Commonly accepted standard is 5 in 100100 5 chances of being wrong makes 95 5 chances of being wrong makes 95

chances of being right chances of being right Researchers print this value as p<0.05Researchers print this value as p<0.05

Errors in Evaluation of HypothesesErrors in Evaluation of Hypotheses

False positives (Type I) and false False positives (Type I) and false negatives (Type II)negatives (Type II) Experiment works but there is Experiment works but there is

inconclusive data to support=false inconclusive data to support=false positive (Reject null hypothesis)positive (Reject null hypothesis)

Experiment fails, but researcher Experiment fails, but researcher overlooked genuine effects=false overlooked genuine effects=false negative (accept null hypothesis)negative (accept null hypothesis)

There is no certainty is There is no certainty is inferential stats. This is why inferential stats. This is why

there are no absolutes in there are no absolutes in science. For most science. For most

psychologists, living with psychologists, living with uncertainty is part of the uncertainty is part of the

challenge of understanding challenge of understanding behavior.behavior.