Discovering statistics using IBM SPSS statistics by A. Field (5th edition) a summary
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Discovering statistics using IBM SPSS statisticsChapter 20Categorical outcomes: logistic regressionThis summary contains the information from chapter 20.8 and forward, the rest of the chapter is not necessary for the course. Logistic regression is a model for predicting categorical outcomes from categorical and continuous predictors.A binary logistic regression is when we’re trying to predict membership of only two categories.Multinominal is when we want to predict membership of more than two categories. The linear model can be expressed as: Yi = b0 + b1Xi + errorib0 is the value of the outcome when the predictors are zero (the intercept).The bs quantify the relationship between each predictor and outcome.X is the value of each predictor variable.One of the assumptions of the linear model is that the relationship between the predictors and outcome is linear.When the outcome variable is categorical, this assumption is violated.One way to solve this problem is to transform the data using the logarithmic transformation, where you can express a non-linear relationship in a linear way.In logistic regression, we predict the probability of Y occurring, P(Y) from known (logtransformed) values of X1 (or Xs).The logistic regression model with one predictor is:P(Y) = 1/(1+e –(b0 +b1X1i))The value of the model will lie between 1 and 0. ...
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Statistics
Chapter 1
Why is my evil lecturer forcing me to learn statistics?
Initial observation: finding something that needs explaining
To see whether an observation is true, you need to define one or more variables to measure that quantify the thing you’re trying to measure.
Generating and testing theories and hypotheses
A theory: an explanation or set of principles that is well substantiated by repeated testing and explains a broad phenomenon.
A hypotheses: a proposed explanation for a fairly narrow phenomenon or set of observations.
An informed, theory-driven attempt to explain what has been observed.
A theory explains a wide set of phenomena with a small set of well-established principles.
A hypotheses typically seeks to explain a narrower phenomenon and is, as yet, untested.
Both theories and hypotheses exist in the conceptual domain, and you cannot observe them directly.
To test a hypotheses, we need to operationalize our hypotheses in a way that enables us to collect and analyse data that have a bearing on the hypotheses.
Predictions emerge from a hypotheses. A prediction tells us something about the hypotheses from which it derived.
Falsification: the act of disproving a hypotheses or theory.
Collecting data: measurement
Independent and dependent variable
Variables: things that can change
Independent variable: a variable thought to be the cause of some effect.
Dependent variable: a variable thought to be affected by changes in an independent variable.
Predictor variable: a variable thought to predict an outcome variable. (independent)
Outcome variable: a variable thought to change as a function of changes in a predictor variable (dependent)
Levels of measurement
The level of measurement: the relationship between what is being measured and the number that represent what is being measured.
Variables can be categorical or continuous, and can have different levels of measurement.
A categorical variable is made up of categories.
It names distinct entities.
In its simplest form it names just two distinct types of things (like male or female).
Binary variable: there are only two categories.
Nominal variable: there are more than two categories.
Ordinal variable: when categories are ordered.
Tell us not only that things have occurred, but also the order in which they occurred.
These data tell us nothing about the differences between values. Yet they still do not tell us about the differences between point scale.
Continuous variable: a variable that gives us a score for each person and can take on any value on the measurement scale that we are using.
Interval variable: to say that data are interval, we must certain that equal intervals on the scale represents equal differences in
Statistics
Chapter 2
The spine of statistics
What is the spine of statistics?
The spine of statistics: (an acronym for)
Testing hypotheses involves building statistical models of the phenomenon of interest.
Scientists build (statistical) models of real-world processes to predict how these processes operate under certain conditions. The models need to be as accurate as possible so that the prediction we make about the real world are accurate too.
The degree to which a statistical model represents the data collected is known as the fit of the model.
The data we observe can be predicted from the model we choose to fit plus some amount of error.
Scientists are usually interested in finding results that apply to an entire population of entities.
Populations can be very general or very narrow.
Usually, scientists strive to infer things abut general populations rather than narrow ones.
We collect data from a smaller subset of the population known as a sample, and use these data to infer things about the population as a whole.
The bigger the sample, the more likely it is to reflect the whole population.
Statistical models are made up of variables and parameters.
Parameters are not measured an are (usually) constants believed to represent some fundamental truth about the relations between variables in the model.
(Like mean and median).
We can predict values of an outcome variable based on a model. The form of the model changes, but there will always be some error in prediction, and there will always be parameters that tell us about the shape or form of the model.
To work out what the model looks like, we estimate the parameters.
The mean as a statistical model
The mean is a hypothetical value and not necessarily one that is observed in the data.
Estimates have ^.
Assessing the fit of a model: sums of squares and variance revisited.
The error or deviance for a particular entity is the score predicted by the model for that entity subtracted from the corresponding observed score.
Degrees of freedom (df): the number of scores used to compute the total adjusted for the fact that we’re trying to estimate the population value.
The degrees of freedom relate to the number of observations that are free to vary.
We
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Chapter 6
The beast of bias
Bias: the summary information is at odds with the objective truth.
An unbiased estimator: one estimator that yields and expected value that is the same thing it is trying to estimate.
We predict an outcome variable from a model described by one or ore predictor variables and parameters that tell us about the relationship between the predictor and the outcome variable.
The model will not predict the outcome perfectly, so for each observation there is some amount of error.
Statistical bias enters the statistical process in three ways:
An outlier: a score very different from the rest of the data.
Outliers have a dramatic effect on the sum of squared error.
If the sum of squared errors is biased, the associated standard error, confidence interval and test statistic will be too.
The second bias is ‘violation of assumptions’.
An assumption: a condition that ensures that what you’re attempting to do works.
If any of the assumptions are not true then the test statistic and p-value will be inaccurate and could lead us to the wrong conclusion.
The main assumptions that we’ll look at are:
Additivity and linearity
The assumption of additivity and linearity: the relationship between the outcome variable and predictor is accurately described by equation.
The scores on the outcome variable are, in reality, linearly related to any predictors. If you have several predictors then their combined effect is best described by adding their effects together.
If the assumption is not true, even if all the other assumptions are met, your model is invalid because your description of the process you want to model is wrong.
Normally distributed something or other
The assumption of normality relates in different ways to things we want to do when fitting models and assessing them:
Statistics
Chapter 7
Non-parametric models
Sometimes you can’t correct problems in your data.
This is especially irksome if you have a small sample and can’t rely on the central limit theorem to get you out of trouble.
The four most common non-parametric procedures:
All four tests overcome distributional problems by ranking the data.
Ranking the data: finding the lowest score and giving it a rank 1, then finding the next highest score and giving it the rank 3, and so on.
This process results in high scores being represented by large ranks, and low scores being represented by small ranks.
The model is then fitted to the ranks and not to the raw scores.
There are two choices to compare the distributions in two conditions containing scores from different entities:
Both tests are equivalent.
There is also a second Wilcoxon test that does something different.
Theory
If you were to rank the data ignoring the group to which a person belonged from lowest to highest, if there’s no difference between the groups, ten you should find a similar number of high and low ranks in each group.
If you were to rank the data ignoring the group to which a person belonged from lowest to highest, if there’s a difference between the groups, ten you should not find a similar number of high and low ranks in each group.
The Mann-Whitney and Wilcoxon rank-sum test use the principles above.
Statistics
Chapter 8
Correlation
The data we observe can be predicted from the model we choose to fit the data plus some error in prediction.
Outcomei = (model) + errori
Thus
outcomei = (b1Xi)+errori
z(outcome)i = b1z(Xi)+errori
z-scores are standardized scores.
A detour into the murky world of covariance
The simplest way to look at whether two variables are associated is to look whether they covary.
If two variables are related, then changes in one variable should be met with similar changes in the other variable.
Covariance (x,y) = Σni=1 ((xi-ẍ)(yi-ÿ))/N-1
The equation for covariance is the same as the equation for variance, except that instead of squaring the deviances, we multiply them by the corresponding deviance of the second variable.
A positive covariance indicates that as on variable deviates from the mean, the other variable deviates in the same direction.
A negative covariance indicates that as one variable deviates from the mean, the other deviates from the mean in the opposite direction.
The covariance depends upon the scales of measurement used: it is not a standardized measure.
Standardization of the correlation coefficient
To overcome the problem of dependence on the measurement scale, we need to convert the covariance into standard set of units → standardization.
Standard deviation: a measure of the average deviation from the mean.
If we divide any distance from the mean by the standard deviation, it gives us that distance in standard deviation units.
We can express the covariance in a standard units of measurement if we divide it by the standard deviation. But, there are two variables and hence two standard deviations.
Correlation coefficient: the standardized covariance
r = covxy/(sxsy)
sx is the standard deviation for the first variable
sy is the standard deviation for the second variable.
By standardizing the covariance we end up with a value that has to lie between -1 and +1.
A coefficient of +1 indicates that the two variables are perfectly positively correlated.
A coefficient of -1 indicates a perfect negative relationship.
A coefficient of 0 indicates no linear relationship at all.
We can test the hypothesis that the correlation is different from zero.
There are two ways of testing this hypothesis.
We can adjust r so that its sampling distribution is normal:
zr = ½ loge((1+r)/(1-r))
The resulting zr has a standard error given by:
Sezr = 1/(square root(N-3))
We can adjust r
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Chapter 9
The linear model (regression)
The linear model with one predictor
outcome = (b0+b1xi) +errori
This model uses an unstandardised measure of the relationship (b1) and consequently we include a parameter b0 that tells us the value of the outcome when the predictor is zero.
Any straight line can be defined by two things:
These parameters are regression coefficients.
The linear model with several predictors
The linear model expands to include as many predictor variables as you like.
An additional predictor can be placed in the model given a b to estimate its relationship to the outcome:
Yi = (b0 +b1X1i +b2X2i+ … bnXni) + Ɛi
bn is the coefficient is the nth predictor (Xni)
Regression analysis is a term for fitting a linear model to data and using it to predict values of an outcome variable form one or more predictor variables.
Simple regression: with one predictor variable
Multiple regression: with several predictors
Estimating the model
No matter how many predictors there are, the model can be described entirely by a constant (b0) and by parameters associated with each predictor (bs).
To estimate these parameters we use the method of least squares.
We could assess the fit of a model by looking at the deviations between the model and the data collected.
Residuals: the differences between what the model predicts and the observed values.
To calculate the total error in a model we square the differences between the observed values of the outcome, and the predicted values that come from the model:
total error: Σni=1(observedi-modeli)2
Because we call these errors residuals, this is called the residual sum of squares (SSR).
It is a gauge of how well a linear model fits the data.
The least SSR gives us the best model.
Assessing the goodness of fit, sums of squares R and R2
Goodness of fit: how well the model fits the observed data
Total sum of squares (SST): how good the mean is as a model of the observed outcome scores.
We can use the values of SST and SSR to calculate how much better the linear model is than the
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Chapter 10
Comparing two means
Categorical predictors in the linear model
If we want to compare differences between the means of two groups, all we are doing is predicting an outcome based on membership of two groups.
This is a linear model with one dichotomous predictor.
Independent t-test: used when you want to compare two means that come from conditions consisting of different entities (this is sometimes called the independent-measures or independent-means t-test)
Paired-samples t-test: also known as the dependent t-test. Is used when you want to compare two means that come from conditions consisting of the same or related entities.
Rationale for the t-test
Both t-tests have a similar rationale:
Most test statistics have a signal-to-noise ratio: the ‘variance explained by the model’ divided by the ‘variance that the model can’t explain’.
Effect divided by error.
When comparing two means, the model we fit is the difference between the two group means. Means vary from sample to sample (sampling variation) and we can use the standard error as a measure of how much means fluctuate. Therefore, we
Statistics
Chapter 11
Moderation, mediation, and multi-category predictors
The conceptual model
Moderation: for a statistical model to include the combined effect of two or more predictor variables on an outcome.
This is in statistical terms an interaction effect.
A moderator variable: one variable that affects the relationship between two others.
Can be continuous or categorical.
We can explore this by comparing the slope of the regression plane for X ad low and high levels of Y.
The statistical model
Moderation is conceptually.
Moderation in the statistical model. We predict the outcome from the predictor variable, the proposed variable, and the interaction of the two.
It is the interaction effect that tells us whether moderation has occurred, but we must include the predictor and moderator for the interaction term to be valid.
Outcomei = (model) + errori
or
Yi = (b0 + b1iX1i + b2iX2i + … + bnXni) + Ɛi
To add variables to a linear model we literally just add them in and assign them a parameter (b).
Therefore, if we had two predictors labelled A and B, a model that tests for moderation would be expressed as:
Yi = (b0 + b1Ai + b2Bi + b3ABi) + Ɛi
The interaction is ABi
Centring variables
When an interaction term is included in the model the b parameters have a specific meaning: for the individual predictors they represent the regression of the outcome on that predictor when the other predictor is zero.
But, there are situation where it makes no sense for a predictor to have a score of zero. So the interaction term makes the bs for the main predictors uninterpretable in many situations.
For this reason, it is common to transform the predictors using grand mean centring.
Centring: the process of transforming a variable into deviations around a fixed point.
This fixed point ca be any value that you choose, but typically it’s the grand mean.
The grand mean centring for a given variable is achieved by taking each score and subtracting from it the mean of all scores (for that variable).
Centring the predictors has no effect on the b for highest-order predictor, but will affect the bs for the lower-order predictors.
Order: how many variables are involved.
When we centre variables, the bs represent the effect of the predictor when the other predictor is at its mean value.
Centring is important when your model contains an interaction term because it makes the bs for lower-order effects interpretable.
There are good reasons for not caring about the lower-order effects when the higher-order
Statistics
Chapter 12
Comparing several independent means
ANOVA: analysis of variance
the same thing as the linear model or regression.
In designs in which the group sizes are unequal, it is important that the baseline category contains a large number of cases to ensure that the estimates of the b-values are reliable.
When we are predicting an outcome from group membership, predicted values from the model are the group means.
If the group means are meaningfully different, then using the group means should be an effective way to predict scores.
Predictioni = b0 + b1X + b2Y + Ɛi
Control = b0
Using dummy coding ins only one of many ways to code dummy variables.
The F-test is an overall test that doesn’t identify differences between specific means. But, the model parameters do.
Logic of the F-statistic
The F-statistic tests the overall fit of a linear model to a set of observed data.
F is the ratio of how good the model is compared to how bad it is.
When the model is based on group means, our predictions from the model are those means.
F tells us whether the group means are significantly different.
The same logic as for any linear model:
Statistics
Chapter 13
Comparing means adjusted for other predictors (analysis of covariance)
The linear model to compare means can be extended to include one or more continuous variables that predict the outcome (or dependent variable).
Covariates: the additional predictors.
ANCOVA: analysis of covariance.
Reasons to include covariates in ANOVA:
For example:
Happinessi = b0 + b1Longi + b2Shorti + b3Covariatei + Ɛi
We can add a covariate as a predictor to the model to test the difference between group means adjusted for the covariate.
With a covariate present, the b-values represent the differences between the means of each group and the control adjusted for the covariate(s).
Independence of the covariate and treatment effect
When the covariate and the experimental effect are not independent, the treatment effect is obscured, spurious treatment effects can arise, and at the very least the interpretation of the ANCOVA is seriously compromised.
When treatment groups differ on the covariate, putting the covariate into the analysis will not ‘control for’ or ‘balance out’ those differences.
This problem can be avoided by randomizing participants to experimental groups, or by matching experimental groups on the covariate.
We can see whether this problem is likely to be an issue by checking whether experimental groups differ on the covariate before fitting the model.
If they do not significantly differ then we might consider it reasonable to use it as a covariate.
Homogeneity of regression slopes
When a covariate is used we loot at its overall relationship with the outcome variable:; we ignore the group to which a person belongs.
We assume that this relationship between covariate and outcome variable holds true for all groups of participants: homogeneity of regression slopes.
There are situations where you might expect regression slopes to differ across groups and that variability may be interesting.
What to do when assumptions are violated
But bootstrap won’t help for the F-tests.
There is a robust variant of ANCOVA.
The main analysis
The format of the ANOVA table is largely the same as without the covariate, except that there is an additional
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Chapter 14
Factorial designs
Factorial design: when an experiment has two or more independent variables.
There are several types of factorial designs:
We can still fit a linear model to the design.
Factorial ANOVA: the linear model with two or more categorical predictors that represent experimental independent variables.
The general linear model takes the following general form:
Yi =b0 + b1X1i+b2X2i+... +bnXni+Ɛi
We can code participant’s category membership on variables with zeros and ones.
For example:
Attractivenessi = b0+b1Ai+b2Bi+b3ABi+Ɛi
b3AB is the interaction variable. It is A dummy multiplied by B dummy variable.
Behind the scenes of factorial designs
Calculating the F-statistic with two categorical predictors is very similar to when we had only one.
Therefore, the sum of squares gets further subdivided into
Total sum of squares (SST)
We start of with calculating how much variability there is between scores when the ignore the experimental condition from which they came.
The grand variance: the variance of all scores when we ignore the group to which they belong.
We treat the data as one big group.
The degrees of freedom are: N-1
SST = s2Grand(N-1)
The model sum of squares (SSM)
The model sum of squares is broken down into the variance attributable to the first independent variable, the variance attributable to the second independent variable, and the variance attributable to the interaction of those two.
The model sum of squares: the difference between what the model predicts and the overall mean of the outcome variable.
Statistics
Chapter 15
Repeated measures designs
Repeated measures: when the same entities participate in all conditions of an experiment or provide data at multiple time points.
Repeated measures can also be considered as a variation of the general linear model.
For example.
Ygi = b0i +b1iXgi +Ɛgi
b0i = b0 + u0i
b1i = b1 + u1i
Ygi for outcome g within person i from the specific predictor Xgi with the error Ɛgi
g is the level of treatment condition
i for the individuals
u0i for the deviation of the individual’s intercept from the group-level intercept
The way that people typically handle repeated measures in IBM SPSS is to use a repeated-measures ANOVA approach.
The assumption of sphericity
The assumption that permits us to use a simpler model to analyse repeated-measures data is sphericity.
Sphericity: assuming that the relationship between scores in pairs of treatment conditions is similar.
It is a form of compound symmetry: holds true when both the variances across conditions are equal and the covariances between pairs of conditions are equal.
We assume that the variation within conditions is similar and that no two conditions are any more dependent than any other two.
Sphericity is a more general, less restrictive form of compound symmetry and refers to the equality of variances of the differences between treatment levels.
For example:
varianceA-B = varianceA-C = varianceB-C
Assessing the severity of departures from sphericity
Mauchly’s test: assesses the hypothesis that the variances of the differences between conditions are equal.
If the test is statistically significant, it implies that there are significant differences between the variances of differences and, therefore, sphericity is not met.
If it is not significant, the implication is that the variances of differences are roughly equal and sphericity is met.
It depends upon sample size.
What’s the effect of violating the assumption of sphericity?
A lack of sphericity creates a loss of power and an F-statistic that doesn’t have the distribution that it’s supposed to have.
It also causes some complications for post
Statistics
Chapter 16
Mixed designs
Situations where we combine repeated-measures and independent designs.
Mixed designs: when a design includes some independent variables that were measured using different entities and others that used repeated measures.
A mixed design requires at least two independent variables.
Because by adding independent variables we’re simply adding predictors to the linear model, you can have virtually any number of independent variables if your sample size is gin enough.
We’re still essentially using the linear model.
Because there are repeated measures involved, people typically use an ANOVA-style model. Mixed ANOVA
All the sources of potential bias in chapter 6 apply.
You can apply the Greenhouse-Geisser correction and forget about sphericity.
Effect sizes are more useful when they summarize a focused effect.
A straightforward approach is to calculate effect sizes for your contrasts.
Statistics
Chapter 17
Multivariate analysis of variance (MANOVA)
Multivariate analysis of variance (MANOVA) is used when we are interested in several outcomes.
The principles of the linear model extend to MANOVA in that we can use MANOVA when there is one independent variable or several, we can look at interactions between outcome variables, and we can do contrasts to see which groups differ.
Univariate: the model when we have only one outcome variable.
Multivariate: the model when we include several outcome variables simultaneously.
We shouldn’t fit separate linear models to each outcome variable.
Separate models can tell us only whether groups differ along a single dimension, MANOVA has the power to detect whether groups differ along a combination of dimensions.
Choosing outcomes
It is a bad idea to lump outcome measures together in a MANOVA unless you have a good theoretical or empirical basis for doing so.
Where there is a good theoretical basis for including some, but not all, of your outcome measures, then fit separate models: one for the outcomes being tested on a heuristic and one for the theoretically meaningful outcomes.
The point here is not to include lots of outcome variables in a MANOVA just because you measured them.
A matrix: a grid of numbers arranged in columns and rows.
A matrix can have many columns and rows, and we specify its dimensions using numbers.
For example: a 2 x 3 matrix is a matrix with two rows and three columns.
The values within a matrix are components or elements.
The rows and columns are vectors.
A square matrix: a matrix with an equal number of columns and rows.
An identity matrix: a square matrix in which the diagonal elements are 1 and the off-diagonal elements are 0.
The matrix that represents the systematic variance (or the model sum of squares for all variables) is denoted by the letter H and is called the hypothesis sum of squares and cross-products matrix (or hypothesis SSCP).
The matrix that represents the unsystematic variance (the residual sums of squares for all variables) is denoted by the letter E and called the error sum of squares and cross-products matrix (or error SSCP).
The matrix that represents the total amount of variance present for each outcome variable is denoted by T and is called the total sum of squares and cross-products matrix (or total SSCP).
Cross-products represent a total value for the combined error between two variables.
Whereas the sum of squares of a variable is
Statistics
Chapter 18
Exploratory factor analysis
In factor analysis, we take a lot of information (variables) and a computer effortlessly reduces this into a simple message (fewer variables).
Latent variable: something that cannot be accessed directly.
Measuring what the observable measures driven by the same underlying variable are.
Factor analysis and principal component analysis (PCA) are techniques for identifying clusters of variables.
Three main uses:
If we measure several variables, or ask someone several questions about themselves, the correlation between each pair of variables can be arranged in a table.
Factor analysis attempts to achieve parsimony by explaining the maximum amount of common variance in a correlation matrix using the smallest number of explanatory constructs.
Explanatory constructs are known as latent variables (or factors) and they represent clusters of variables that correlate highly with each other.
PCA differs in that it tries to explain the maximum amount of total variance in a correlation matrix by transforming the original variables into linear components.
Factor analysis and PCA both aim to reduce the R matrix into a smaller set of dimensions.
Graphical representation
Factors and components can be visualized as the axis of a graph along which we plot variables.
The coordinates of variables along each axis represent the strength of relationship between that variable and each factor.
In an ideal world a variable will have a large coordinate for one of the axes and small coordinates for any others.
Factor loading: the coordinate of a variable along a classification axis.
If we square the factor loading for a variable we get a measure of its substantive importance to a factor.
Mathematical representation
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Chapter 19
Categorical outcomes: chi-square and loglinear analysis
Analysing categorical data
Sometimes we want to predict categorical outcome variables. We want to predict into which category an entity falls.
With categorical variables we can’t use the mean or any similar statistic because the mean of a categorical variable is meaningless: the numeric values you attach to different categories are arbitrary, and the mean of those numeric values will depend on how many members each category has.
When we’ve measured only categorical variables, we analyse the number of things that fall into each combination of categories (the frequencies).
Pearson’s chi-square test
To see whether there’s a relationship between two categorical variables we can use the Pearson’s chi-square test.
This statistic is based on the simple idea of comparing the frequencies you observe in certain categories to the frequencies you might expect to get in those categories by chance.
X2 = Σ(observedij-modelij)2 / modelij
i represents the rows in the contingency table
j represents the columns in the contingency table.
As model we use ‘expected frequencies’.
To adjust for inequalities, we calculate frequencies for each cell in the table using the column and row totals for that cell.
By doing so we factor in the total number of observations that could have contributed to that cell.
Modelij = Eij = (row totali x column totalj) / n
X2 has a distribution with known properties called the chi-square distribution. This has a shape determined by the degrees of freedom: (r-1)(c-1)
r = the number of rows
c = the number of columns
Fischer’s exact test
The chi-square statistic has a sampling distribution that is only approximately a chi-square distribution.
The larger the sample is, the better this approximation becomes. In large samples the approximation is good enough not to worry about the fact that it is an approximation.
In small samples, the approximation is not good enough, making significance tests of the chi-square statistic inaccurate.
Fischer’s exact tests: a way to compute the exact probability of the chi-square statistic in small samples.
The likelihood ratio
An alternative to Pearson’s chi-square.
Based on maximum-likelihood theory.
General idea: you collect some data and create a model for which the probability of obtaining the observed set of data is maximized, then you compare this model to the probability of obtaining those data under the null hypothesis.
The resulting statistic is based on comparing observed frequencies with those predicted by the
Here is a short explanation how to do tests in SPSS. These are the tests needed for the third block of WSRt and psychology at the second year of the uva.
This magazine contains all the summaries you need for the course WSRt at the second year of psychology at the Uva.
Discovering statistics using IBM SPSS statistics
Chapter 20
Categorical outcomes: logistic regression
This summary contains the information from chapter 20.8 and forward, the rest of the chapter is not necessary for the course.
Logistic regression is a model for predicting categorical outcomes from categorical and continuous predictors.
A binary logistic regression is when we’re trying to predict membership of only two categories.
Multinominal is when we want to predict membership of more than two categories.
The linear model can be expressed as: Yi = b0 + b1Xi + errori
b0 is the value of the outcome when the predictors are zero (the intercept).
The bs quantify the relationship between each predictor and outcome.
X is the value of each predictor variable.
One of the assumptions of the linear model is that the relationship between the predictors and outcome is linear.
When the outcome variable is categorical, this assumption is violated.
One way to solve this problem is to transform the data using the logarithmic transformation, where you can express a non-linear relationship in a linear way.
In logistic regression, we predict the probability of Y occurring, P(Y) from known (logtransformed) values of X1 (or Xs).
The logistic regression model with one predictor is:
P(Y) = 1/(1+e –(b0 +b1X1i))
The value of the model will lie between 1 and 0.
You need to test for
Multinomial logistic regression predicts membership of more than two categories.
The model breaks the outcome variable into a series of comparisons between two categories.
In practice, you have to set a baseline outcome category.
Evidence-based treatment and practice: New opportunities to bridge clinical research and practice, enhance the knowledge base, and improve patient care
American Psychologist, 63, 146-159.
A central issue is the extent to which findings from research can be applied to clinical practice.
Empirically support or evidence-based treatment (EBT) refers to the interventions or techniques that have produced therapeutic change in controlled trials. Evidence-based practice (EBP) is a broader tem and refers to clinical practice that is informed by evidence about interventions, clinical expertise, and patient needs, values, and preferences and their integration in decision making about individual care.
Concerns about evidence-based treatments
An concern about EBTs is that key conditions and characteristics of treatment research depart markedly from those in clinical practice and bring into question how and whether to generalize the results to practice.
Another concern about research in psychotherapy pertains to the focus on symptoms and disorders as the primary way of identifying participants and evaluation treatment outcomes. In clinical practice, much of psychotherapy is not about reaching a destination (eliminating symptoms), but it is about the ride (the process of coping with life). Psychotherapy research rarely addresses the broader focus of coping with multiple stressors and negotiating the difficult shoals of life, both of which are aided by speaking with a trained professional.
There are concerns about the methods of analysis or the results among several studies. They question whether these are satisfactory bases for concluding that treatment is effective or efficacious. These concerns are: 1) Conclusions about treatment that are based on studies showing statistical differences are difficult to translate into effects on the lives of participants in the study, let alone generalize to patients seen in practice. 2) The outcome measures in most psychotherapy studies raise fundamental concerns. Changes on rating scales are difficult to translate into changes in everyday life. Many valid and reliable measures of psychotherapy are ‘arbitrary metrics’, we do not know how changes on standardized measures translate to functioning in everyday life. 3) Typically, in a single study, multiple measures are used to evaluate outcome, and only some of these show that the treatment and control conditions are statistically different. An EBT may have support for its effects, but within individual studies and among multiple studies, the results are often mixed.
There are inherent limitations in the ways EBTs are discussed. Large segments of the literature usually are grouped together.
A central concern about EMBTs involves the generalization of the results
.....read moreA power primer.
Cohen (1992)
Psychological Bulletin
The tables of this article are missing
Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects.
Statistical power analysis exploits the relationships among the four variables involved in statistical inference.
Each is a function of the other three. It is most useful to determine the N necessary to have a specified power for given α and ES.
The significance criterion α
α represents the maximum risk of mistakenly rejecting the null hypothesis (committing a Type I error). This is usually .05. α risk may be defined as one or two sided.
Power
The statistical power of a significance test is the long-term probability, given the population ES, α, and N of rejection the H0. When the ES is nit equal to zero, H0 is false, so failure to reject it also incurs an error (Type II error). For any given ES, α, and N, its probability of occurring is β. Power is 1 – β, the probability of rejecting a false H0.
Taken the conventional α = .05, power of .80, there is a α:β ratio of 4:1 of the two kinds of risks.
Sample size
In research planning, the investigator needs to know the N necessary to attain the desired power for the specified α and hypothesized ES. N increases with an increase in the power desired, a decrease in the ES and in α.
For statistical tests involving two or more groups, N is the necessary size for each group.
Effect size
The effect size (ES) is the degree to which the H0 is believed to be false.
In the Neyman-Pearson method of statistical inference, an alternative hypothesis H1 is counterpoised against H0. The degree to which H0 is false is indexed by the discrepancy between H0 and H1 and is called the ES. Each statistical test has its own ES index. All the indexes are scale free and continuous, ranging upward from zero. For all, the H0 is that ES = 0.
To convey the meaning of any given ES index, it is necessary to have some idea of its scale.
The ES index for the t test of the difference between independent means is d, the difference expressed in units of the within-population standard deviation.
.....read more
Measures of clinical significance.
Kraemer, Morgan, Leech, Gliner, Vaske & Harmon (2003)
Journal of the American Academic of Child & Adolescent Psychiatry
Behavioural scientists are interested in answering three questions when examining the relationships between variables
Researchers suggest that using one of three type of effect size measures assist in interpreting clinical significance
A statistical significant outcome indicates that there is likely to be at least one relationship between the variables. p indicates the probability that an outcome this extreme could happen, if the null hypothesis is true. It doesn’t provide information about the strength of the relationship or whether it is meaningful.
It is possible, with a large sample, to have a statistically significant result from a weak relationship between variables. Outcomes with lower p values are sometimes misinterpret as having stronger effects than those with higher p’s.
Non-statistically significant results do not ‘prove’ the null hypothesis. These might be due to determinants of low power.
The presence or absence of statistical significance does not give information about the size or importance of the outcome. This makes it critical to know the effect size.
The r family
One method of expressing effect sizes is in terms of strength of association. This can be done with statistics such as the Pearson product moment correlation coefficient, r, used when both the independent and the dependent measures are ordered. Such effect sizes vary between -1.0 and + 1.0. 0 represents no effect.
The d family
Used when the independent variable is binary (dichotomous) and the dependent variable is ordered.
When comparing two groups, the effect size d can be computed by
.....read moreStatistics
Chapter 13
Comparing means adjusted for other predictors (analysis of covariance)
The linear model to compare means can be extended to include one or more continuous variables that predict the outcome (or dependent variable).
Covariates: the additional predictors.
ANCOVA: analysis of covariance.
Reasons to include covariates in ANOVA:
For example:
Happinessi = b0 + b1Longi + b2Shorti + b3Covariatei + Ɛi
We can add a covariate as a predictor to the model to test the difference between group means adjusted for the covariate.
With a covariate present, the b-values represent the differences between the means of each group and the control adjusted for the covariate(s).
Independence of the covariate and treatment effect
When the covariate and the experimental effect are not independent, the treatment effect is obscured, spurious treatment effects can arise, and at the very least the interpretation of the ANCOVA is seriously compromised.
When treatment groups differ on the covariate, putting the covariate into the analysis will not ‘control for’ or ‘balance out’ those differences.
This problem can be avoided by randomizing participants to experimental groups, or by matching experimental groups on the covariate.
We can see whether this problem is likely to be an issue by checking whether experimental groups differ on the covariate before fitting the model.
If they do not significantly differ then we might consider it reasonable to use it as a covariate.
Homogeneity of regression slopes
When a covariate is used we loot at its overall relationship with the outcome variable:; we ignore the group to which a person belongs.
We assume that this relationship between covariate and outcome variable holds true for all groups of participants: homogeneity of regression slopes.
There are situations where you might expect regression slopes to differ across groups and that variability may be interesting.
What to do when assumptions are violated
But bootstrap won’t help for the F-tests.
There is a robust variant of ANCOVA.
The main analysis
The format of the ANOVA table is largely the same as without the covariate, except that there is an additional
.....read moreStatistics
Chapter 16
Mixed designs
Situations where we combine repeated-measures and independent designs.
Mixed designs: when a design includes some independent variables that were measured using different entities and others that used repeated measures.
A mixed design requires at least two independent variables.
Because by adding independent variables we’re simply adding predictors to the linear model, you can have virtually any number of independent variables if your sample size is gin enough.
We’re still essentially using the linear model.
Because there are repeated measures involved, people typically use an ANOVA-style model. Mixed ANOVA
All the sources of potential bias in chapter 6 apply.
You can apply the Greenhouse-Geisser correction and forget about sphericity.
Effect sizes are more useful when they summarize a focused effect.
A straightforward approach is to calculate effect sizes for your contrasts.
Discovering statistics using IBM SPSS statistics
Chapter 20
Categorical outcomes: logistic regression
This summary contains the information from chapter 20.8 and forward, the rest of the chapter is not necessary for the course.
Logistic regression is a model for predicting categorical outcomes from categorical and continuous predictors.
A binary logistic regression is when we’re trying to predict membership of only two categories.
Multinominal is when we want to predict membership of more than two categories.
The linear model can be expressed as: Yi = b0 + b1Xi + errori
b0 is the value of the outcome when the predictors are zero (the intercept).
The bs quantify the relationship between each predictor and outcome.
X is the value of each predictor variable.
One of the assumptions of the linear model is that the relationship between the predictors and outcome is linear.
When the outcome variable is categorical, this assumption is violated.
One way to solve this problem is to transform the data using the logarithmic transformation, where you can express a non-linear relationship in a linear way.
In logistic regression, we predict the probability of Y occurring, P(Y) from known (logtransformed) values of X1 (or Xs).
The logistic regression model with one predictor is:
P(Y) = 1/(1+e –(b0 +b1X1i))
The value of the model will lie between 1 and 0.
You need to test for
Multinomial logistic regression predicts membership of more than two categories.
The model breaks the outcome variable into a series of comparisons between two categories.
In practice, you have to set a baseline outcome category.
Statistics
Chapter 11
Moderation, mediation, and multi-category predictors
The conceptual model
Moderation: for a statistical model to include the combined effect of two or more predictor variables on an outcome.
This is in statistical terms an interaction effect.
A moderator variable: one variable that affects the relationship between two others.
Can be continuous or categorical.
We can explore this by comparing the slope of the regression plane for X ad low and high levels of Y.
The statistical model
Moderation is conceptually.
Moderation in the statistical model. We predict the outcome from the predictor variable, the proposed variable, and the interaction of the two.
It is the interaction effect that tells us whether moderation has occurred, but we must include the predictor and moderator for the interaction term to be valid.
Outcomei = (model) + errori
or
Yi = (b0 + b1iX1i + b2iX2i + … + bnXni) + Ɛi
To add variables to a linear model we literally just add them in and assign them a parameter (b).
Therefore, if we had two predictors labelled A and B, a model that tests for moderation would be expressed as:
Yi = (b0 + b1Ai + b2Bi + b3ABi) + Ɛi
The interaction is ABi
Centring variables
When an interaction term is included in the model the b parameters have a specific meaning: for the individual predictors they represent the regression of the outcome on that predictor when the other predictor is zero.
But, there are situation where it makes no sense for a predictor to have a score of zero. So the interaction term makes the bs for the main predictors uninterpretable in many situations.
For this reason, it is common to transform the predictors using grand mean centring.
Centring: the process of transforming a variable into deviations around a fixed point.
This fixed point ca be any value that you choose, but typically it’s the grand mean.
The grand mean centring for a given variable is achieved by taking each score and subtracting from it the mean of all scores (for that variable).
Centring the predictors has no effect on the b for highest-order predictor, but will affect the bs for the lower-order predictors.
Order: how many variables are involved.
When we centre variables, the bs represent the effect of the predictor when the other predictor is at its mean value.
Centring is important when your model contains an interaction term because it makes the bs for lower-order effects interpretable.
There are good reasons for not caring about the lower-order effects when the higher-order
Voorbij het oordeel van de dodo
Huibers (2015)
Tijdschrift voor Psychotherapie
DOI: 10.1007/s12485-015-0027-6
Om de effectiviteit van psychotherapie te verhogen zijn er drie routes. Dit zijn: 1) Het bedenken van nieuwe therapieën 2) Inzicht vinden in de onderliggende werkingsmechanismen 3) Onderzoek doen naar het profiel van patiënten die baad hebben van therapie.
The great psychotherapy debate
Over de werkingsmechanismen van psychotherapie is weinig bekend.
Een mediator is de term voor het werkingsmechanisme of het veranderproces waarmee we het effect van de therapie willen verklaren.
De temporele relatie is wat er vooraf gaat aan het andere. Om dit te kunnen bepalen moeten we tijdens de behandeling vaak zowel de mediator als de uitkomstvariabele meten. Zo kun je ontdekken welke eraan vooraf gaat. Indien de mediator aan de uitkomstvariabele vooraf gaat, weet je nog niet zeker of de mediator werkelijk het veranderingsmechanisme is. De verandering op de mediator kan op zijn beurt weer worden veroorzaakt door een andere factor.
In onderzoek naar werkingsmechanismen zijn twee elementen belangrijk. Dit zijn: 1) Statistische mediatie, het effect van de behandeling op de uitkomst loopt via de mediator 2) Temporele relatie, de verandering van de mediator moet eerder optreden dan de verandering in uitkomstvariabele.
Een moderator wijst op verschillende uitkomsten in verschillende behadelingen. Het is een interessante factor om te bepalen voor welke mensen een bepaalde soort therapie werkt. bn
An introduction to Meta-analysis
Individual studies
Effect size
The effect size is a value which reflects the magnitude of the treatment effect or the strength of a relationship between two variables. It can represent any relationship between two variables. It is the unit of currency in a meta-analysis.
You compute the effect size for each study, and then work with the effect sizes to assess the consistency of the effect across studies and to compute a summary effect.
In graphs, the effect size for each study is represented by a square, with the location of the square representing both the direction and magnitude of the effect.
Precision
In a schematic, the effect size for each study is bounded by a confidence interval. This reflects the precision with which the effect size has been estimated in that study.
Study weights
In a schematic, the size of each square reflects the weight that is assigned to the corresponding study. There is a relationships between a study’s precision and that study’s weight in the analysis. Studies with good precision are assigned more weight. Precision is primarily driven by simple size.
Other elements can be used as well to assign weights.
p-values
For each study, a p-value for a test of the null is shown. A p-value will fall under .005 only if the 95% confidence interval does not include the null value.
The summary effect
Typically, you report the effect size of a summary effect, as well as a measure of precision and a p-value.
Effect size
In a plot, a summary effect is shown on the bottom line. It is the weighted mean of the individual effects. The mechanism used to assign the weights depends on our assumptions about the distribution of effect sizes form which the studies were sampled. 1) Fixed-effect model, there is assumed that all studies in the analysis share the same true effect size. The summary effect is the estimate of this common effect size 2) Random-effects model, there is assumed that the true effect sizes vary from study to study. The summary effect is our estimate of the mean of the distribution of effect sizes
Precision
The summary effect is represented by a diamond. The location
.....read moreMeta-analysis in mental health reserach
Cuipters, P (2016)
Advantages of meta-analysis are: 1) The statistical power to detect effects is higher than for individual studies,this makes a more precise and accurate estimation of true effects possible 2) It is possible to explore inconsistencies between studies and to examine whether the effects of the intervention differs among specific subgroups of studies. 3) It is possible to make an estimate of the number of studies that were conducted but not published
Problems with meta-analyses are: 1) Garbage in, garbage out, they can never be better than the studies they summarize 2) They combine apples and oranges, there are always differences between studies. 3) The file drawer problem, not all relevant studies are published and are often not included in meta-analyses 4) Researcher allegiance, the agenda-driven bias of researchers who conduct the meta-analyses.
Publication bias is the problem that not all the studies that are conducted in a certain area are actually published. Publication of studies which show significant effect and large effects of intentions are favoured. This can lead to an over-estimation of the effect.
There are several other types of reporting bias 1) Time lag bias, some studies are published later than others, depending on the nature and direction of the results 2) Outcome reporting bias 3) Language bias, when studies in another language are not identified and these studies differ in terms of nature and direction of the results.
Testing for publication bias with indirect methods: the funnel plot
In some cases it is possible to examine publication bias directly.
If it is not possible to compare published with unpublished trials, it is possible to get an indirect impression whether there is publication bias or not. These estimates are based on the assumption that large studies can make a more precise estimate of the effect size. Random variations of the effect sizes are larger in studies with few participants, a difference that can be represented graphically in a ‘funnel plot’. In this plot the effect size is represented at the horizontal axis and the size of the study on the vertical axis. If the effect sizes differ from the mean effect size only by chance, they should divert in both directions, both positive and negative.
There are several tests for the asymmetry of the funnel plot.
There is also a method to impute the missing studies and estimate the effect size after imputation of these missing studies.
.....read moreEvaluating statistical and clinical significance of intervention effects in single-case experimental designs: an SPSS method to analyse univariate data
Marija, de Haan, Hogendoorn, Wolters and Huizenga (2015)
Single-case experimental designs are useful to investigate individual client progress. This can help the clinician to investigate whether an intervention works as compared with a baseline period or another intervention, and whether symptom improvement is clinically significant.
In single-case experimental designs (SCEDs), a single participant is repeatedly assessed on one or multiple indices (symptoms) during various phases. Advantages of this are: 1) It can be used to test novel interventions. 2) In heterogenous populations, it can be the only way to investigate treatment outcomes. 3) It offers the possibility to systematically document knowledge of researchers and clinicians.
The AB design consist of two phases; baseline and a treatment.
The AB method can be conceptualized as an interrupted time series. To analysis the differences between baseline and treatment two requirements must be fulfilled. These are: 1) the overall pattern in the time series has to be modelled adequately, an adequate model consists of two linear functions, one for baseline and one for the treatment. Each of these functions is described by an intercept and a slope. 2) adequate modelling of potential correlations between residuals is needed, this is adequate modelling for that which remains after the overall pattern has been accounted for. Autocorrelation is the correlation between residuals of the observations. The residuals are not independent. If residuals are correlated, the correlations are likely to decrease with increasing separation between time points.
Analyses investigating treatment efficacy
Y(i) = b0 +b1 * phase(i) + b2*time_in_phase (i) + b3 * phase(i) * time_in_phase(i) + error(i)
Y(i) is the outcome variable at time point i.
Phase(i) denotes the phase in which time point I is contained (0 for baseline and 1 for treatment).
Time_in_phase (i)is the time points in each phase.
Error (i) is the residual at point i.
b0 is the baseline intercept
b1 is the treatment-baseline difference in intercepts
b2 is the baseline slope
b3 is the treatment-baseline difference in slopes
Intercept differences between phases can be assessed by testing whether b1 differs from 0. Slope differences can be assessed by testing b3.
B0 and b1 refer to symptom scores when time_in_phase is zero. This depends on the coding of time_in_phase.
It is best to both describe the general trend adequately and to account for remaining correlations. The
.....read moreN=1 studies in onderzoek en praktijk.
De Psycholoog, 3, 11-20
Door frequent te meten bij een individu ontstaat er een N =1 studie. De informatie kan worden gebruikt om de behandeling optimaal in te richten en maximaal resultaat te verkrijgen.
N =1 studies zijn studies waarbij de ernst van de klachten worden gemeten bij één cliënt tijdens de baseline en de behandeling. Dit kan informatie opleveren of en wanneer de behandeling werkt.
Kenmerken van N=1 studies zijn: 1) Het is een studie op één deelnemer. Het is mogelijk een reeks N =1 studies uit te voeren waarbij meerdere deelnemers met dezelfde onderzoeksvraag worden onderzocht. De gevonden resultaten worden bij elkaar gevoegd. 2) Er wordt regelmatig en betrouwbaar gemeten 3) Er zijn duidelijk gedefinieerde fasen. Fase A is een baselinefase Fase B is een fase waarin er behandeling plaatsvindt. In een ABC design krijgt een cliënt twee verschillende behandelingen (B en C).
Werkzaam
N = 1 studies kunnen gebruikt worden voor het testen van nieuwe behandelprocedures en om de doeltreffendheid van bestaande behandelingen te testen in de praktijk.
In N=1 studies kunnen veranderingsprocessen worden onderzocht. Werkingsmechanismen zijn processen waardoor de behandeling effect behaalt.
Voor het onderzoek naar werkingsmechanismen zijn een aantal voorwaarden waaraan moet worden voldaan: 1) de deelnemer moet de behandeling hebben ontvangen 2) tijdens de behandeling moet er verbetering op zijn getreden in de onderzochte klachten 3) er moet worden aangetoond dat er verandering is opgetreden op het voorgestelde werkingsmechanisme 4) de verandering op het werkingsmechanisme moet voorafgaan aan het behandeleffect.
De N=1 methodologie sluit aan bij de klinische praktijk.
Een nadeel van de N=1 methodologie is: gegevens verkregen via een N=1 studie hebben een lagere bewijskracht dan gegevens verkregen uit een RCT.
N=1 studies en RCT studies kunnen elkaar aanvullen.
Voordelen N=1 studies zijn: 1) De omgeving van de studie moet minder aan methodologische eisen voldoen. De cliënt is zijn eigen controle Het kan worden onderzocht in heterogene groepen 2) Analyses worden op individueel niveau uitgevoerd, waardoor er geen informatie verloren gaat 3) N=1 studies worden op kleine schaal uitgevoerd.
De werkingsmechanismes die naar voren komen in N=1 studies kunnen daarna in RCTs worden onderzocht.
Routinematig meten
Routine Outcome motinitoring (ROM) maakt herhaaldelijk meten mogelijk. Het kan gebruikt worden om gegevens te verzamelen over stoornis, cliënt en behandeluitkomsten.
.....read moreThe empirical status of empirically supported psychotherapies: Assumptions, findings, and reporting in controlled clinical trials
Psychological Bulletin, 130
ESTs are empirically supported therapies.
ESTs are typically designed for a single Axis I disorder, and patients are screened to maximize homogeneity of diagnosis and minimize co-occurring conditions that could increase variability of treatment response. Treatments are manualized and of brief and fixed duration to minimize within-group variability. Outcome assessment focuses primarily on the symptom that is the focus of the study.
RCT methodologies to validate ESTs require a set of additional assumptions that are neither well validated nor applicable to most disorders because: 1) psychopathology is highly malleable 2) most patients can be treated for a single problem or disorder 3) psychiatric disorders can be treated independently of personality factors 4) experimental methods provide a gold standard for identifying useful psychotherapeutic packages
A substantial body of data shows that, with or without treatment, relapse rates for all but a handful of disorders are high. There is also a dose-response relationship. Longer treatments are more effective.
Most psychopathological vulnerabilities are highly resistant to change. They tend to be rooted in personality and temperament. The modal patient treated with brief treatments for most disorders relapses or seeks additional treatment.
In RCTs, including patients with substantial comorbidities would vastly increase the sample size necessary to detect treatment differences if comorbidity bears any systematic relation to outcome.
Three issues are: 1) The empirical and pragmatic limits imposed by reliance on DSM diagnoses 2) The problem of comorbidity 3) the way the different functions of assessing comorbidity in controlled trials and clinical practice may place limits on generalizability
The pragmatics of DSM-IV diagnosis
Three costs of the DSM are 1) the diagnoses are themselves created by committee consensus on the basis of the available evidence rather than by strictly empirical methods 2) The assumption that patients typically present with symptoms of a specific Axis I diagnosis and identify
.....read moreIn this beautiful magazine, the summaries you need for the course evidence-based working in clinical practice in the third year of the uva, are bundled.
A logistic regression model is a model with a binary response variable (like 'agree' or 'don't agree'). It's also possible for logistic regression models to have ordinal or nominal response variables. The mean is the proportion of responses that are 1. The linear probability model is P(y=1) = α + βx. This model often is too simple, a more extended version is:
The logarithm can be calculated using software. The odds are:: P(y=1)/[1-P(y=1)]. The log of the odds, or logistic transformation (abbreviated as logit) is the logistic regression model: logit[P(y=1)] = α + βx.
To find the outcome for a certain value of a predictor, the following formula is used:
The e to a certain power is the antilog of that number.
A straight line is drawn next to the curve of a logistic graph to analyze it. β is maximal where P(y=1) = ½. For logistic regression the maximal likelihood method is used instead of the least squares method. The model expressed in odds is:
The estimate is:
With this the odds ratio can be calculated.
There are two possibilities to present the data. For ungrouped data a normal contingency table suffices. For grouped data a row contains data for every count in a cel, like just one row with the number of subjects that agreed, followed by the total number of subjects.
An alternative of the logit is the probit. This link assumes a hidden, underlying continuous variable y* that is 1 above a certain value T (threshold) and that is 0 below T. Because y* is hidden, it's called a latent variable. However, it can be used to make a probit model: probit[P(y=1)] = α + βx.
Logistic regression with repeated measures and random effects is analyzed with a linear mixed model: logit[P(yij = 1)] = α + βxij + si.
The multiple logistic regression model is: logit[P(y = 1)] = α + β1x1 + … + βpxp. The further βi is from 0, the stronger
.....read moreDiscovering statistics using IBM SPSS statistics
Chapter 20
Categorical outcomes: logistic regression
This summary contains the information from chapter 20.8 and forward, the rest of the chapter is not necessary for the course.
Logistic regression is a model for predicting categorical outcomes from categorical and continuous predictors.
A binary logistic regression is when we’re trying to predict membership of only two categories.
Multinominal is when we want to predict membership of more than two categories.
The linear model can be expressed as: Yi = b0 + b1Xi + errori
b0 is the value of the outcome when the predictors are zero (the intercept).
The bs quantify the relationship between each predictor and outcome.
X is the value of each predictor variable.
One of the assumptions of the linear model is that the relationship between the predictors and outcome is linear.
When the outcome variable is categorical, this assumption is violated.
One way to solve this problem is to transform the data using the logarithmic transformation, where you can express a non-linear relationship in a linear way.
In logistic regression, we predict the probability of Y occurring, P(Y) from known (logtransformed) values of X1 (or Xs).
The logistic regression model with one predictor is:
P(Y) = 1/(1+e –(b0 +b1X1i))
The value of the model will lie between 1 and 0.
You need to test for
Multinomial logistic regression predicts membership of more than two categories.
The model breaks the outcome variable into a series of comparisons between two categories.
In practice, you have to set a baseline outcome category.
Week 4: Logistic Regression Analysis (LRA)
LRA can be used when the dependent variable (Y) is binary and the predictors (X1, X2) interval level (or binary).
The research question is: Can Y be predicted fromX1and/orX2?
Is there a significant association between grade and passing/failing the exam? (report test statistic, df, and p value)?
Here, we look at the Variables in the Equation table at the Wald of the grade. If it’s significant, then yes there is a significant association. An example of how this can be reported:
Yes, Wald χ2(1) = 7.090,p=.006
Write down the logistic regression equation
For example:
if the constant B is -4.200
the grade B is: .671
Then the equation looks like this:
(From now on, sorry for the weird format of the formulas)
For what grade is the probability of passing the MVDA exam equal to the probability of failing the MVDA exam?
Passing= 50%
Failing=50%
P=1/2 =
In order for to be 1, -4.200+ .671(Grade) has to be equal to 0. This is because e to the power of 0 is 1.
So, -4.200 + 0.671(g)=0
0.671(g)=4.200
g=6.259
Therefore, the grade where there is an equal chance for passing and failing is 6.259.
Calculate the probabilities and odds of passing for X= 0,5, 10
X P Odds (rounded up)
0 =0.0148
=
= 0.015
5 = 0.3005
= 0.429
10 = 0.9248
=11.5
How to calculate the odds ratio?
Example:
X P Odds Odds ratio
1 .0285 .02931 =1.958
2 .0543 .0574 1.958
Therefore, if X increases 1 unit, the odds are going to increase by x 1.958 (times 1.958).
What is the odds ratio of X of
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