Empirical Research
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Empirial Research Methods Lecture 1 MeasurementLevel of Measurement: relationship between the numbers and waht is being measured → Why is it important? Eg. Age → Adult (yes/no) ,Age groups ( 1-12, 11-20…)Measurement ScalesBinary: Two distinct categories (Male/female, yes/no)Nominal: – More than two categories Number only tells categoryNo rankingE.g. colours, means of transportation Pointless to arithmeticOrdinal: – Ordered categories (logical order) -Tells nothing about differences between values -E.g. 1st, 2nd, and 3rd prizeInterval: – Information about differences between points on a scale Equal intervals represent equal differencesE.g. Celsius scaleRatio: -Same, but with absolute zero -e.g. weight→ What matters: you cannot perform all calculations on all variablesBasic Issues in Measurement Validity:Extent to which a measure correctly represents the concept of studyInternal: How well the study was doneExternal: Generalize results to other situationsAccuracy: Measure close to actual valueGetting the ‘right’ answer on averageReliability: Extent to which a variable is consistent in what it is intended to measureLOOK AT IT AGAIN, JUST HALF OF THE LECTURELecture 2The Research ProcessResearch QuestionObserve the worldTheory/literatureHypothesis/predictionVariablesCollect DataMeasurement (Lecture 1)Analyze DataGraphically/ descriptively ← L2Fit a modelAnd back to the beginningDescriptive/summary statisticsStep #1Quantitative description of main features of dataJust a summaryBefore actual analysis→ Which are the players of the game? Which is the nature of the variables? (Discrete, continuous) Do you see any problems? Things to keep in mind? (e.g. range, negative values)→ Get a feel for your dataSummary StatisticsNumber of observationsMeasures of central tendency- Mean: arithmetic average (influenced by extreme observations- Median: middle point when values are ranked in order of magnitude (Relatively unaffected by extreme scores) – Mode: most frequent value (More than one mode, e.g. bimodal, multimodal)→ Which measure of central tendency to use? Type of VariableBest measure of central tendencyNominalModeOrdinalMedianInterval/Ratio (not skewed)MeanInterval/ Ratio (skewed)MedianSkewness- Says something about the shape of the distribution- Deviation from normal (Normal: skew=0)- Symmetry of a distribution, compared to a normal- Kind of skew labelled according to the longer tail- Values outside the -1 to +1 range indicate a substantially skewed distribution[pic 1]Kurtosis- Says something about the shape of the distribution- Deviation from normal (normal/mesokurtic: Kurtosis=3)- Degree to which scores cluster at the tails and how pointy a distribution is (peakedness of flatness), compared to a normal- Leptokurtic: heavy tails and pointy (>3)- Platykurtic: light tails and flatter (<3)[pic 2] [pic 3]5. Minimum and Maximum -Range Dispersion Defined as: maximum value – minimum value Affected by extreme score -Interquartile Range Dispersion Q1: lower quartile (25%) Q2: upper quartile (75%)Variance and standard deviation DispersionVarianceStandard deviationSquare root of varianceHow spread out the data are from the meanHeterogeneity of the sample‘normal’ data (bell-shaped)[pic 4] Measure of association-Sometimes part of summary statistics-Correlation/Correlation coefficient - Strength of the relationship between two variables -Positive or negative(sign) - (-1, +1) -Magnitude says something about the strength of the relationship (size) -Coefficient of +1 (-1): -Two variables are perfectly positively (negatively) correlated -As one increases, the other increases (decreases) by a proportionate amount -Coefficient of 0: -No linear relationship between two variables - As one variable changes, the other stays the same -Significance
Essay About Negative Values And Age Groups
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