spss statistics assignment help

spss statistics assignment help

The Importance of Using SPSS Statistics in Research: A Comprehensive Guide

1. Introduction to SPSS Statistics

If accomplishing all the intended objectives of this chapter is to be feasible, then one should be able or at least have some special interests in statistics, particularly statistical analysis with an SPSS software program. SPSS originated from an initial project then developed into a powerful statistical tool that’s very handy in transforming raw or unaudited data into the desired or useful information, mainly through its user-friendly interface. Worth noting is that, despite the continued progress in the innovations of SPSS, one should note that the well-structured attributes of simple interface plus other common features will still remain. Before advocating a move from one stage to another, it is very important to make it clear that the seemingly simple nature of SPSS control panels in no way warrants usage on autopilots in that very logical and practical responses expected can only come from a very decisive and sincere SPSS user. Indeed it is not right to question “why should I use SPSS”, but the apt question is “how” and “how best can I” use SPSS in ensuring that I carry out an honest and highly convincing task. Answering these questions correctly is the main objective in this book.

2. Key Features and Functions of SPSS

SPSS files are for the most part composed of a sheet, called a pivot table, which displays a data column for each variable in your data set. When the data set is completed, you will have many rows, each of which is organized quantitatively or qualitatively, into variables. If you are experienced with spreadsheets like Microsoft Excel, you will find SPSS familiar, but the rows and the columns are reversed. Indeed, the rows of the SPSS pivot table are called cases in the context of SPSS, indicating that each row contains case-specific information that sets a particular entity or observation, which is the subject of the study, in a unique context for some of its attributes.

Once we have set up data in SPSS, we can implement data management strategies to prepare the information within the data file for either informational or inferential analysis. For instance, we can check for missing values and, if possible, change these so that they do not impact subsequent statistical testing. We can also recode the values within an existing variable, for example, changing income brackets into an ordinal scale. Using the graphical user interface, we carry out these and other activities with the click of a button. SPSS uses menus, dialog boxes, and dialog box tabs to lead us through data management activities. SPSS can be customized to appeal to the many different users who apply it in various fields of research.

3. Data Input and Management in SPSS

Due to its powerful data recording capacity, SPSS Statistics has long been the program of choice among researchers across multiple disciplines. To prepare the data in a form that is fit for use in SPSS for statistical examination, it is necessary to find the way the data can be input and managed within SPSS Statistics. Recoding the levels of the original variable, categorizing both the variables, creating new variables, creating data string values, and handling missing data are some of the features that will be important in designing an extensive design for a study. It is possible to perform such simple steps easily and effectively by applying the labels, values, and missing data options. Exploring the efficacy and susceptibility relating to the software of SPSS Statistics, from a management point of view, to the data in the longitudinal spheres, we note that it is an effective guide for the processing of the dataset.

SPSS Statistics has been designed in such a way that its file space is easy to read, is limited in volume, and at the same time uses significant features. From the File menu, the Open feature is very successful. Data Management allows you to unlock a limited part of the data, lock a part of it using a simple click, or re-lock the available table. SPSS Statistics also make it easier to resize or organize the layout of the variables and the data stored in their files quickly. Variable names and labels are used to represent the data and the variables most accurately. The variable window is a useful tool in collecting data and quickly laying them out to assist in maximizing the display capacity. Cross-tabulations will provide essential information in terms of data-recording for a vital issue.

4. Statistical Analysis Techniques in SPSS

Essential in reporting internal consistency reliability in social sciences, specifically business, psychology, and most social science research.

Cronbach’s Alpha

Essential while determining groups or ‘clusters’ in a dataset.

Cluster Analysis

Another tool that can be used for the purpose of factor identification.

Principal Component Analysis

Essential while evaluating the reliability of measurement scales and items. SPSS guides the user in identifying and deleting items that are not reliable.

Reliability Analysis

The methodology employed to learn various factors that are essential to explain the observed values of a dataset.

Factor Analysis

Essential when the utility of two or more independent variables to discriminate among groups is in the purview of study.

Discriminant Analysis

Useful in ascertaining the relationship between two or more variables and for making predictions. Stepwise regression, essential when there is a hierarchy in data, can also be performed with ease.

Simple (Linear) and Multiple Regressions

Essential for determining the strength of relationship between two quantitative variables. SPSS provides the facility to use a test of statistical significance too.

Correlation

Essential when dichotomous data that provide frequencies are to be compared with an expected distribution. Its utility is superior to a simple frequency analysis.

Chi-Square

Essential when the researcher intends to find the effects of more than one independent variable on more than one dependent variable at the same time. Its utility and action are over and above the capabilities of the above-listed analyses.

MANOVA

Also finds differences between means, but is used when the effects of at least two factors are to be considered in the data.

Factorial ANOVA

Essential for comparing the effect of one factor on an outcome variable, it should be used in association with the post hoc test for a more intricate analysis.

One-Way ANOVA

Through this test, the user can essentially find out if there is a significant difference between the means of two groups. It is very useful while comparing pre-intervention and post-intervention datasets.

T-Test in SPSS

It holds all the basic features one needs to perform quick descriptive statistical evaluation of a dataset. The results are effectively treated too, such as in graphical (output to charts) or even tabular formats.

Descriptive Statistics

Being a comprehensive statistical tool, SPSS Statistics provides an array of reliable techniques which allow any kind of user, irrespective of their skill set, to perform judicious data analysis and prove or refute a hypothesis with clinically sound factual deductions. A comprehensive list of these analysis techniques is included in the following list.

5. Interpreting and Presenting Results in SPSS

However, there are two important points in summarizing SPSS descriptive analysis window. First, the variables should be measured at the interval or ratio level of measurement if you are to interpret the mean, standard deviation, standard error of the mean, confidence, skewness, and kurtosis. Second, it is the descriptive measures on the descriptive analysis window intended for interval or ratio data except for mean. It is also recommended that you right click and check statistics on your chart if you want to report more than one measure in a chart.

Summarized descriptive analysis. The importance of summarizing is to compress large data into meaningful summaries. Descriptive analysis gives you an enumeration of the sample sizes; mean or average score; median (which cuts the scores into two halves’ percentiles); mode (frequent score); standard deviation which shows how scores are spread around the mean score; standard error of the mean which shows how much error in the sample means get from the population means; confidence interval of the mean measure of how much confidence we have in our sample means; kurtosis (which tells you how much data is peaked); and skewness (which is like kurtosis, but it measures asymmetry) are the summary statistics given by SPSS in the window or from the trek or from a frequency distribution.

This chapter gives an in-depth guide on presenting descriptive, bivariate, and inferential analysis on SPSS. It is important for you to refer to chapter 3 and 4 of this book in order to appreciate the different kinds of outputs in descriptive, bivariate, and inferential analysis. It is from these outputs that we will illustrate important aspects of presenting results on SPSS, discuss the different formats and styles of the outputs, and interpret the results.