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SPSS stands for Statistical Package for the Social Sciences. It is a statistical analysis software developed by Hadlai Hull & Dale Bent and released for the first time in the year 1968. After a long period of transition and development SPSS which was originally owned by SPSS Inc was acquired by IBM in the year 2009. Thus, current SPSS versions are no longer called SPSS, but referred by the name IBM SPSS. Although SPSS initially targeted social sciences, it is nowadays used by a lot of other disciplines as well, like marketing, education, nursing, government studies, medicine and data mining. SPSS can be beneficial to scholars and individuals involved in data collection and quantitative research. IBM SPSS can make the whole process of data collection a lot more efficient and easier for the researcher to run statistical tests. IBM SPSS has a number of programmable 4GL features as well. Our SPSS assignment helpers are well-versed in all aspects of SPSS and can handle any assignment on the subject. Get in touch with us any time of the night or day for instant SPSS online help.

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Our SPSS experts can help with the following SPSS tests and more. Contact us with your specific requirement and let us know how soon you need it completed and confirm your order with us. We will take care of the rest.

Analysis of Covariance (ANCOVA)Discriminant Function Analysis
Analysis of Variance (ANOVA)Factor Analysis
SPSS Association TestSPSS Dynamic Factor Analysis
Chi Square TestSPSS Forecasting Analysis
Correlation AnalysisFriedman Test
Cox RegressionKruskal Walis Test
Descriptive StatisticsMANOVA
Multiple RegressionOne Proportion Test
Ordinal Logistic RegressionPaired Sample Tests
Qualitative AnalysisReliability Analysis
Stata ProgrammingScatter Plot
T-TestsSurvival Analysis
Two-Portion TestVariables using Stata


Discriminant Function Analysis is a statistical procedure which uses linear combination of interval variables to predict group membership. At the beginning of the procedure the values of the group membership and the interval variables are known, based on which certain observations and assumptions are made. The main assumption here is that for a trait that is being analyzed, the sample is distributed normally. For the analysis, the classification probabilities are calculated using the posterior probability and typicality probability methods.

Based on relative Mahalanobis’ distances which measure the centroid of each group, the posterior probability tests whether there is a probability that an unknown case belongs to a given group. The typicality probability tests the likeliness of a case belonging to a given group based on variability within the groups. At the end of the analysis a model is achieved that allows the user to predict group memberships knowing only the interval variables.

The procedure is generally used to get a better understanding and helps in simplification of a multivariate set, on the basis of the combining of interval variables. Being a widely used and important statistical procedure, Discriminant Function Analysis has been integrated into most standard statistical software packages for greater applicability.

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Analysis of Covariance (ANCOVA) is a statistical technique that uses the principles of Analysis of Variance (ANOVA) and linear regression simultaneously. Similar to factorial ANOVA, the ANCOVA procedure allows the user to determine the additional information that can be extracted by considering only one independent variable or factor at a time without the influence of other factors. The procedure is used to analyze grouped data which has a response or a dependent variable and two or more predictor variables (covariates). For the analysis, at least one of the predictor variables needs to be continuous (scaled and quantitative) and one is categorical (non-scaled and nominal). The technique is used by analysts to determine and describe the response of a variable as a linear function of the said predictors, where the line coefficients vary among different groups. The principle of the procedure is to include additional factors called covariates as a statistical control tool to explain the variation on the dependent variable, increase the statistical sensitivity of the underlying design and to reduce error variation. The procedure can be used as an extension of multiple regressions in order to compare multiple regression lines as well as an extension of ANOVA.



Analysis of Variance (ANOVA): After you pay for SPSS assignment help on our website to confirm your order with us, it becomes our responsibility to make sure your assignment is assigned to the right expert, done well and most importantly, delivered back to you on time, so that you are able to submit it on time and get maximum scores for it. We deliver high-scoring, error-free and 100% plagiarism-free assignments, exactly as per your requirements.

There are three types of ANOVA, namely:

  • One-way analysis which compares more than three groups on the basis of one factor variable
  • Two-way analysis in which there are more than two factor variables
  • K-way analysis in which the factor variables are numerous or unknown


Factor Analysis is a technique used to reduce or filter a large number of variables into much lesser number of factors. The key principle of the technique is that numerous observed variables respond on a similar pattern as they are all associated with a latent variable (not directly measurable). The main function of the technique is to extract the maximum common variance from all variables put them in the form of a common score. A part of the General Linear Model, the Factor Analysis has some common and important assumptions such as; the relationship is linear, there is no multicollinearity, relevant variables are included in the analysis, and the correlation between the factors and the variables is true. Factor Analysis a very useful technique and is widely used to investigate variable relationships in concepts which are highly complex in nature. By narrowing down a large number of variables into easy to interpret and underlying factors, this technique allows the analyst or the researcher to investigate concepts that are difficult to measure.


In statistics, Association Test is a broad and informal term used in many different ways. In general terms, Association Test or Test of Association can denote any technique or tool used to establish or determine a relationship between factors and variables. Therefore, an Association Test can be a diagram such as a Scatter Plot which shows the association among variables, as well as a Test of Hypothesis which is used to statistically demonstrate the existence of relationship between variables. Still, the term refers most commonly to the Chi-square test. The major Tests of Association include:

  • Chi-square test: for association; used to determine the relationship between two categorical variables.
  • CHM test: which is used to study data from numerous sources as well as from stratified data from a single source.
  • Fisher’s exact test: used as an alternative to chi-square test when the sample size is smaller.
  • The gamma coefficient: which is used to understand the closeness of two pairs of data-points and to indicate how strong the relationship between the variables is.
  • Goodman Kruska’s gamma: which test ranked variables.


Dynamic Factor Analysis (DFA) is a multivariate statistical technique used widely in econometric and psychological fields to detect and determine common patterns in sets of time series, as well as the relationship between the time series and the relevant explanatory factors. Depending on the context of the analysis, the patterns could be anything from common trends, common cycles and common seasonal effects among others. In terms of common patterns and explanatory variables, DFA is widely used to model short and non-stationary time series. It can be used to determine and demonstrate whether there is a common pattern in a given time series, whether the response variables interact with each other in any way, and to underline the effects of explanatory variables.


The Chi-square test is used to compare two statistical datasets and involves the analysis of data based on observations of a random set of variables. The technique is generally used to test the relationship between two categorical variables. The test is based on the null hypothesis that no relationship exists between the categorical variables in the population which alternatively means that the variables are independent. The Chi-square test is useful in determining relationships where the researcher is required to determine how closely the observed distribution is related to the expected distribution or to estimate the independence of two random variables. The technique is extensively used to analyze the cross tabulations of data such as survey response to predict behavior of the population and forecast a pattern or trend.


Correlation analysis is a bivariate statistical procedure used to determine and establish the strength of relationship between two quantitative or categorical variables. A high correlation means that the variables have a strong or significant relationship with each other, whereas a weak correlation means that the variables have a weak or negligible relationship. In simpler terms, Correlation Analysis is the process of studying and evaluating the strength of that relationship with available statistical data. This technique is completely based on the linear regression analysis which is a statistical approach for determining and modeling the association between a dependent variable known as response, and one or more explanatory or independent variables. Correlation Analysis is widely used in the field of social sciences such as government and healthcare institutions to make predictions, as well as in business for budgeting and planning.


The Friedman Test is used extensively as a non-parametric alternative to one-way ANOVA with repeated measures. The method is used to evaluate the differences between two or more groups when the dependent variable is ordinal. The Friedman Test can also be used for continuous data which has ignored the important assumptions while calculating the one-way ANOVA with repeated measures, such as data with remarkable deviation from normality. The critical factor in choosing the Friedman Test is whether the given data can actually be analyzed using this method, before proceeding with the analysis.

The test is based on the following assumptions:

  • One of the groups is measured on three or more occasions.
  • Group constitutes of a random sample taken from the population.
  • The dependent variable is measured at the ordinal level.
  • Samples are not required to be normally distributed.


The assumptions made for MANOVA tests are:

MANOVA or Multivariate Analysis of Variance can be simply described as an extension of ANOVA, having multiple dependent variables. The basic difference between the two is that while ANOVA is used to test the difference in means among the groups, the MANOVA extends this analysis further by testing for the difference in multiple vectors of means. It then combines the results together to determine a composite variable or weighted linear combination. The MANOVA then compares the newly created combination with the independent variables to determine if it differs by different groups or levels. Thus the main function of MANOVA is to test if the independent grouping variable explains the presence of a statistically significant amount of difference in the dependent variable, along the process.

  • Measure of central tendency: which include the ways to describe the central position of a frequency distribution for a dataset or group
  • Level and measurement of variables: the independent variables are categorical and the dependent variables are quantitative or scaled
  • Absence of multicollinearity: which means that the dependent variables are not highly correlated.
  • Normality: means that the data has multivariate normality.
  • Homogeneity of variance: means that there is equal variance between the groups.


An extended form of the simple linear regression, the multiple regression technique allows the analyst to predict the value of one variable on the basis of the values of two or more other variables. The variable whose value is determined in this technique is called the dependent variable and the other variables whose values are used in the calculation are called the independent variables, predictors, explanatory variables or regressor variables. The technique also allows the analyst to calculate the overall fit or variance explained of the model as well as the relative contribution of each independent variable to the total variance explained.

Before choosing to use multiple regression to analyze the data, the analyst needs to ensure that the following assumptions are met by the data in order to generate accurate results:

  • The dependent variable is measured on a continuous scale.
  • There are two or more independent variables which can be either continuous (interval or ratio variable) or categorical (nominal or ordinal variable).
  • There should be complete independence of observations which can be checked easily using the Durbin-Watson statistic in the SPSS Statistics program.
  • The dependent variable should have a linear relationship with each of the independent variables individually as well as collectively.
  • The data must assume homoscedasticity.
  • The data must not have multicollinearity.
  • The data should not have significant outliers or points that are highly influential.
  • There should be approximately normal distribution of errors.


The Two Sample Proportion Test is a statistical procedure to determine whether there are differences in the proportions of two observed groups. At the end of this procedure, the range of values which may include the difference value is calculated. The null hypothesis for the test is that the difference in the population proportions is equivalent to the hypothesized value or difference and is denoted by P1=P2. The two-tailed alternative hypothesis assumes that the difference between the population proportions is not equivalent to the hypothesized value or difference. The first one-tailed alternative hypothesis assumes that the difference between the population proportions is higher than the hypothesized value, whereas the other one-tailed alternative hypothesis states that the difference between the population proportions is smaller than the hypothesized value.

The main assumptions for the test are:

  • The data is simple and collected randomly for both the populations.
  • A binomial distribution is followed in both populations.
  • The binomial distribution can be approximated by the normal distribution when the values of both mean and variance are greater than 10.
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    The Cox Regression, also known as proportional hazards regression is a statistical method to analyze the effect that numerous factors have upon the time taken by a given event to occur. The method is referred to as Cox regression for survival analysis for an event such as death. Upon building the model from observed values, the method can be used to make predictions for new or further inputs. The best Cox models are considered to be those which include observations in which the event did not occur as well as the observations in which the event occurred. The Cox regression method can be used for both quantitative and categorical variables. The method is used to analyze various risk factors for survival. It is also widely in analysis where there is a problem of participant heterogeneity when ideally there should be participant homogeneity, i.e., the participants should possess similar characteristics relevant to the research. On the downside, the Cox regression is considered one of the tougher statistical methods to understand and extremely challenging to calculate by hand, needing statistical software packages such as the SPSS to perform the analysis.


    A non-parametric statistical procedure, the Kruskal-Wallis Test is used for analysis of datasets where the required assumptions of a one-way ANOVA test are not fulfilled. Similar to the one-way ANOVA Test, the Kruskal—Wallis Test assesses for any significant differences on a continuous dependent variable caused by another variable which is categorical and independent. The difference between the two is that the Kruskal—Wallis Test does not make the assumptions that the there is normal distribution of the dependent variable and the scores across the groups have approximately equal variance, whereas the one-way ANOVA makes both these assumptions. As the method does not include these assumptions, it can be used for continuous as well as ordinal-level dependent variables. The test is based on the null hypothesis that the samples are taken from populations that are identical in characteristics. The alternative hypothesis for the test is that at least one sample is taken from a dissimilar group.

    The technique makes the following assumptions:

    • The samples are randomly drawn from the population.
    • The observations are independent of each other.
    • At least an ordinal scale is used for the measurement of dependent variables.


    Descriptive Statistics refers to the analysis of the data in a form that is easy to comprehend and understand for the common reader or user. It helps the researcher in describing, showing and summarizing the data in such a way that things like patterns in the data might become apparent to the reader. Simply put, descriptive statistics allow the researcher or the analyst to describe the findings of the numerical data in words. They do not however, allow the analyst to derive any conclusion other than that of the data analyzed, nor do they allow conclusions to be made from the hypothesis that might have been tested. Nevertheless, descriptive statistics are very important in data analysis as they give a presentable meaning to the numerical data and help the reader in visualizing what the data is actually trying to indicate. It is especially important for the analyst to include adequate description of the analysis when there are a lot of numbers, graphs and formulae involved.

    Descriptive statistics can be of two types:

    • Independent random sampling: which means the assumption that the observations are independent of each other, no pattern is applied in sample selection and the sample is absolutely random.

    • Measures of scores: which allow the analyst to summarize a group of data by describing the way in which the scores are spread


    The One Proportion Test, better known as the One-Sample proportion test is used to ascertain whether there is a significant difference between a population proportion (P1) and the hypothesized value (P0). The hypothesis here is called the hypothesis of inequality and it can be expressed as the proportions, their ratio, odds ratio, or their difference. However the result of all the four hypotheses is the same test statistics. The method uses either the exact test or any of the approximate z-tests to calculate the sample size and the statistical power of testing a single proportion. Calculations using the binomial as well as the hyper-geometric distributions are used to derive the exact test results. The statistical power of the analysis of various statistical tests can be compared to find the most suitable test for a particular dataset or situation. Another advantage of the method is that it has the capability to calculate power using both the binomial enumeration and the normal approximation for any test. Binomial calculations are used when the sample size is small or the proportion is extreme (less than 0.2 or more than 0.8), whereas the normal approximation is appropriate for larger sample sizes and smaller proportions (roughly between 0.2 and 0.8).


    Often considered as a generalization of the multiple linear regression or the binomial logistic regression, the Ordinal Logistic Regression or simply the ordinal regression is a statistical procedure used to make prediction on an ordinal dependent variable using one or more given dependent variables. The ordinal logistic regression, like all other types of regression, is also capable of using the interactions between the independent variables to predict the value of the dependent variable. The procedure allows the analyst to identify the independent variable which has a statistically significant impact on the dependent variable. The analyst can also interpret and determine the odds that one group had a relatively higher or lower impact on the dependent variable than the other group when analyzing categorical independent variables.

    The assumptions that need to be considered in this procedure are:

    • The dependent variable is measured at the ordinal level.
    • Measures of scores: which allow the analyst to summarize a group of data by describing the way in which the scores are spread
    • The data has no multicollinearity
    • The odds are proportional


    The Paired Sample Test also called the Paired Sample t-test or the dependent t-test is a statistical method to determine if the mean difference between two observation sets is zero. Each subject or entity in a paired sample t-test is measured two times thus generating pairs of the observations. The paired sample t-test is commonly applied in case-control studies and repeated-measures designs. The null hypothesis in this method assumes that the true mean difference is zero for the paired samples, whereas the alternate hypothesis assumes that the true mean difference between the samples is not equal to zero. All differences that can be observed are explained using random variation in this model. A two-tailed hypothesis can also be applied in situations where it does not matter whether the difference is positive or negative.

    The paired sample t-test being a parametric procedure makes the following assumptions that must be considered before choosing the method for analysis:

    • The dependent variable is continuous
    • The observations are completely independent of each other.
    • There is an approximate normal distribution of the dependent variable.
    • There are no significant outliners in the dependent variable.


    Qualitative analysis is an integral part of the decision making process and deals with the ‘why’ and ‘how’ part of process based on in-depth reasoning and the quality of the results determined. It describes the characteristics and quality of the sample which is usually taken using questionnaires, observations, notes or interviews. Sample collected through such methods can be hard to quantify and measure but the researcher can use coding to categorize the data to identify patterns that correspond to the research question. Due to its focus on quality of the data, many researchers prefer qualitative analysis using smaller sample sizes rather than quantitative analysis with larger sample sizes. The data is classified into patterns so as to arrange information and conclude the results. The method also uses data in various forms such images, sounds and texts, rather than just numerical data. To conduct a qualitative analysis the researcher must have a deep knowledge of the target sample or subject. Qualitative analysis is widely used in the field of research in business, marketing, medicine, psychology and nursing, given the fact that it gives the researcher a deeper and detailed understanding of the subject by analyzing factors and variables that are not just numbers.


    Reliability Analysis is based on the necessity of a scale to constantly reflect the construct it is being used to measure. The statistical procedure of Reliability Analysis is used to calculate various commonly used measures of scale reliability while also generating information about the impact and relationship each individual item in the scale has with one another. Intra class correlation coefficients are often used to calculate the inter-rater reliability estimates. The reliability analysis is of particular use for a researcher when two or more observations under the research are equivalent both in terms of the construct that is being measured and the outcome. An example of a case where reliability analysis can be used is when the researcher needs to determine whether the questionnaire measures the needs of the customer in a useful manner. The procedure can be based on a number of models namely, Alpha (Cronbach), Split-half, Guttman, Parallel and Strict parallel.

    The assumptions made in this procedure are:

    • The observations are independent of each other.
    • Errors should not be correlated between items.
    • The pairs of items should have bivariate normal distribution.
    • The items should be linearly related to the score, and thus the scale needs to be additive.


    Stata is popular econometric programming language and software package used extensively in the fields of research in economics, political science and biomedicine to evaluate the inherent patterns in data. The software enables the researcher to analyze, manage and present the data in graphical and visual forms. The package incorporates both command line and a standard graphical user interface, making the software as well as the programming using this language more intuitive. Programming using Stata is available for most of the popular operating systems such as Windows, UNIX and Mac OS on computers with 32-bit and 64-bit capabilities. It is not necessary for a researcher to be expert in programming to use the Stata programming language and commands as most of the commands are same as in the software. However, the researcher should know the appropriate commands for the desired tasks and the right sequence to put them in. Stata Programming uses two different types of files; Do-files which are run from the command prompt or line using the Do command, and the Ado-files which work like usual Stata commands using simply the file name in the command prompt or line.


    In the Stata programming language, the variables from the data being analyzed are usually organized in columns and the different observations of these variables are organized in corresponding rows. The Stata/IC package, also known as the Intercooled Stata is capable of handling up to 2,047 variables in a single analysis, while as many as 32,766 variables can be handled by the Stata/SE version of the package. Stata/SE also enables the analyst to use longer string variables while writing a program.


    A Scatter Plot is a plot diagram in which every value for two independent variables in the dataset is represented by a dot on a graph with at least a horizontal and a vertical axis. The position of the dot on the axes corresponds to the actual value of an individual data point. The scatter plot is typically used to determine the relationship between the numeric variables. Apart from indicating the values of the individual data points, the dots on the scatter plot also show patterns when studied as a whole. Relationships of correlation are commonly identified in a scatter plot. Here the researcher tries to estimate or determine what would be a reasonable prediction for a vertical value on the basis of a particular horizontal value. The relationship between the variables established on a scatter plot can be described mainly in six ways namely, positive, negative, linear, non-linear, strong and weak. Scatter plots can also be used to determine other patterns in the datasets, identify gaps in the data, and to identify any outliers.


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    Survival Analysis refers to a number of statistical approaches used to determine the time taken for a specified event of interest to occur. Survival analysis has a wide range of applications in the fields of medical and healthcare such as cancer studies where it is used to estimate the patient survival time, sociology where it is used in event-history analysis, and engineering where it is often used for the analysis of failure time. For patient studies it is important to understand that not all patients respond to a treatment in the same way and some of them may not experience the specified event (e.g. death) at the end of the period of observation. This means that the survival time may remain unknown for some patients and this phenomenon, known as censoring provides valid inferences and thus must be considered and included in the analysis. The general t-test is based on testing of hypotheses involving numerical population parameters, whereas the underlying population parameter in a survival analysis is in the form of a curve called the survival curve. When the t-test is used in survival analysis it is denoted as S (t) and defined as the probability that an individual will survive more than the time t.

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