The SPSS originally known as Statistical Package for Social Sciences (but later changed to Statistical Product and Service Solutions) is a statistical tool widely used to perform simple to complex statistical analysis. The SPSS software is mainly used by students, researchers and data miners. The widespread use of SPSS is attributed to its efficiency in not only analyzing data but also managing and documenting statistical data. Additionally, the accessibility of all the features and services offered by the SPSS has also increased its market value over the years.
SPSS Statistics help is a tab within the Statistics Heroes platform that offers SPSS aid to high school, college, undergraduate and postgraduate students, market, government, education and health researchers. Through Statistics SPSS help, clients are able to receive professional help and advice at affordable rates from our team of experts. Statistics SPSS help services mainly include SPSS data entry and SPSS data analysis
Statistics SPSS data entry
Data entered into SPSS can either be scale, nominal, ordinal or interval. Variables measured on a continuous scale are referred to as scale variables such as age in years, height in centimeters and weight in pounds. Nominal variables have two or more levels whereby the difference in the levels does not mean a thing. For instance, gender measured as either male or female is a nominal variable.
Ordinal variables are variables which are measured on a grouped scale whereby the difference between the level is significant. For instance, when income is measured on a scale of low, medium or high it can be categorized as an ordinal variable. Interval variables on the other hand are variables measured on a specific range say 1 to 10 whereby the distances between the scale attributes are equal and zero is arbitrary. A perfect example of an interval variables is temperature. A temperature of 20 is 5 degrees higher than a temperature of 15.
Entering data in SPSS can be quite tedious especially for novices while to some experts, it can be time-consuming. Also, categorizing variables into the different levels of measures can be confusing but no need to worry, our experts at SPSS Statistics help got you covered. The data entered into SPSS can be individual or for an organization.
Statistics SPSS data analysis.
The main motive of collecting any data is to test it against a given or existing hypothesis. A hypothesis is defined as a claim made regarding a population, for example “The weight of school-going teenagers in North Carolina is 50Kgs”. Data analysis helps you test whether your theory is true or not. Data analysis also helps you identify whether your results align with previously conducted research.
There are mainly two types of hypotheses: the null hypothesis and the alternative/research hypothesis. The research hypothesis is the claim that you are testing against your data to either reject or accept. Often, the null hypothesis is basically the opposite of the research hypothesis. Research hypotheses can either be comparative, differential or predictive. As a result, different statistical approaches are used. Statistics SPSS help services under SPSS data analysis are based on these approaches.
SPSS regression analysis help
Regression is a statistical technique used to examine the relationship between a dependent variable and one or more independent variables. Regression analysis is mainly used when we have predictive kind of research hypothesis. Type of regression analysis differ based on the nature of the dependent variable, number of independent variables and nature of the independent variables. In regard to regression analysis, some of the types of regressions that our team of experts at SPSS Statistics help will assist you with include (but not limited to);
Simple linear regression, Multiple Linear regression, Stepwise Regression, Forward/Backward Regression, and Hierarchical Regression. Further we do Logistic Regression, Binomial regression, Poisson regression, and Panel data regression.
SPSS correlation Help
Correlation analysis is a statistical technique used to assess whether there exists a relationship between data variables and if the relationship exists, how strong or weak is it. Correlation coefficients indicates the nature and strength of a linear relationship. The coefficient ranges from -/+ 0.1 to -/+1 with values close to -/+1 indicating a strong relationship and those close to -/+0.1 indicating a weak relationship. Through Statistics SPSS help, our team of experts will help you with correlation related questions such as which types of correlations to use and under what circumstances. Additionally, the team will also perform correlation analysis for you and interpret the results at a very affordable price.
SPSS T-test Help
T-test or Students’ t-test is a statistical test used to examine difference between two samples. Additionally, t-test is also used to examine whether the sample parameters differ from the population. It is important to note that the t-test is used when the sample size is considerably small (n≤30) and follows a normal distribution. When the normality assumption is violated, the non-parametric version of the t-test is used. The non-parametric version of the paired/matched pairs t-tests is the Wilcoxon test while that of the independent samples t-test is the Mann-Whitney test. When the data is normally distributed, the are 3 types of parametric t-tests that we will perform in SPSS include;
- One sample t-test which compares a sample population.
- Paired samples t-test which compares dependent samples
- Independent samples t-tests which compares unrelated samples
SPSS ANOVA Help
ANOVA (Analysis of variance) is similar to t-test. The only difference is that ANOVA test compares differences between 2 or more 2 groups. However, in most cases the independent samples t-test is used for two groups. For instance, if you wanted to examine whether there exists a difference between 3 colleges based on academic performance, the ANOVA test would be the most appropriate test. Similar to any other parametric test, the ANOVA makes the following assumptions:
- The dependent variable is continuous and is approximately normal
- The samples to be examined are unrelated.
- The independent variables should have more than 2 categories.
- The population variances must be equal (Homogeneity of variances).
- No significant outliers.
When the normality assumption is violated the non-parametric version of the Anova test, Kruskal-Wallis test is used. There are different types of Anova test which our team of experts at Statistics SPSS help will assist you with in terms of analysis and interpretation. These include;
- One-way ANOVA.
- Two or three-way ANOVA.
- Repeated measures ANOVA.
- Factorial ANOVA (Within-subjects and between-subjects).
- ANCOVA and MANOVA
SPSS Chi square Test
Chi square test is a statistical test used to examine association between categorical variables. A categorical variable is a variable measured to two or more levels. The variable can either be ordinal or nominal. SPSS allows 3 main types of chi square test which our team of experts will perform and interpret which are One -sample chi square test/goodness of fit, Chi square test of homogeneity and Chi square test of independence.
Often the Chi square test statistic and the P value are used to either individually or together to either reject or “accept” the null hypothesis. If the calculated chi square test statistic is less than or equal to the critical chi square value (value in chi square table), the null hypothesis fails to be rejected. Conversely, if the P value of the chi square test statistic is less the level of significance (say 0.05), the null hypothesis is rejected and the alternative assumed.
SPSS reliability analysis
Reliability analysis is a statistical approach used mainly by researchers to examine the internal consistency among items measuring a given variable. For instance, a variable say social connectedness can be measured using 10 items. In order to understand whether there is internal consistency, reliability analysis is used. The SPSS uses Cronbach Alpha to quantify the internal reliability among variable items. The value of the Cronbach Alpha ranges from 0.1 to 0.9. A value between 0.1 to 0.3 indicates low internal consistency, a value of 0.3 to 0.6 indicates a moderate internal consistency while a value of 0.7 to 0.9 indicates a high internal consistency. Statistically, it is recommended that the items should have Cronbach Alpha value of 0.7 or above. Depending on reliability analysis statistics a value below 0.7 would indicate addition or removal of items measuring the given variable.
Our team of professionals at SPSS help have performed reliability analysis for researchers from across the globe for the more than 10 years hence they will not only conduct reliability analysis for you but also interpret reliability analysis results and advice you accordingly.