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Quantitative Data Analysis With SPSS

10 Quantitative Analysis with SPSS: Getting Started

Mikaila Mariel Lemonik Arthur

This chapter focuses on getting started with SPSS. Note that before you can start to work with SPSS, you need to get your data into an appropriate format, as discussed in the chapter on Preparing Quantitative Data and Data Management . It is possible to enter data directly into SPSS, but the interface is not conducive to data entry and so researchers are better off entering their data using a spreadsheet program and then importing it.

Importing Data Into SPSS

In some cases, existing data will be able to be downloaded in SPSS format (*.sav is the file extension for an SPSS datafile), in which case it can be opened in SPSS by going to File → Open → Data and then locating the location of the file. However, in most cases, researchers will need to import data stored in another file format into SPSS. To import data, go to the file menu, then select import data. Next, choose the type of data you wish to import from the menu that appears. In most cases, researchers will be importing Excel or CSV data (when they have entered it themselves or are downloading it from a general-purpose site like the Census Bureau) or SAS or Stata data (when they are downloading it from a site that makes prepared statistical data files available).

A screenshot showing the visual navigation to import data in SPSS. To navigate by keys: Alt+F opens the file menu; then Alt+D opens the import data menu. Then choose Alt+B to run a query on database data; Alt+E for Excel, Alt+C for CSV, Alt+T for text data, Alt+S for SAS; Alt+a for Stata; Alt+B for dBase--there are two commands using Alt+B; Alt+L for Lotus; Alt+Y for SYLK; Alt+M for Cognos TM1; and Alt+O for Cognos Business Intelligence.

Once you click on a data type, a window will pop up for you to select the file you wish to import. Be sure it is of the file type you have chosen. If you import a file in a format that is already designed to work with statistical software, such as Stata, the importation process will be as seamless as opening a file. Researchers should be sure that immediately after importing, they save their file (File → Save As) so that it is stored in SPSS format and can be opened in SPSS, rather than imported, in the future. It is essential to remember that SPSS is not cloud-resident software and does not have an autosave function, so any time a file is changed, it must be manually saved.

If you import a file in Excel, CSV (comma-separated values) or text format, SPSS will open an import wizard with a number of steps. The steps vary slightly depending on which file type you are importing. For instance, to import an Excel file, as shown in Figure 2, you first need to specify the worksheet (if the file has multiple worksheets—SPSS can only import one worksheet at a time). You can choose to specify a limited range of cells. Checking the checkbox next to “Read variable names from first row of data” will replace the V1, V2, V3, and so on column headers with whatever appears in the top row of data in the Excel file. You can also choose to change the percentage of values that are used to determine data type, remove leading and trailing spaces from string values, and—if your Excel file has hidden rows or columns—you can choose to ignore them. Below the options, a preview of your Excel file will be shown; you can scroll through the preview to see that data is being displayed correctly. Clicking OK will finalize the import.

A different set of options appears when you import a CSV file, as shown in Figure 3. The top of the popup window shows a preview of the data in CSV format. While toggles related to whether the first line contains variable names, removing leading and trailing spaces, and indicating the percentage of values that determine the data type are the same as for importing data from Excel, there are additional options that are important for the proper importing of CSV data. First of all, the user must specify whether values are delimited by a comma, a semicolon, or a tab. While commas are the most common delimiters in CSV files, the other delimiters are possible, and looking at the preview should make clear which of the delimiters is being used in a given file, as shown in the example below.

Comma-delimited:
Semicolon-delimited:
Tab-delimited:

Second, the user must specify whether the period or the comma is the decimal symbol. Data produced in the United States typically uses the period (as in 1238.67), as does data produced in many other English-speaking countries, while most of Europe and Latin America use the comma. Third, the user must specify the text qualifier (single quotes, double quotes, or none). This is the character used to note that the contents of a particular entry in the CSV file are textual (string variables) in nature, not numerical. If your data includes text, it should be clear from the preview which qualifier is being used. Users can also toggle whether data is cached locally or not; caching locally speeds the importation process.

Finally, there is a button for Advanced Options (Text Wizard). The text wizard offers the same window and options that users see if they are importing a text file directly, and this wizard offers more direct control over the importation process over a series of six steps. First, users can specify a predefined format if they have a *.tpf file on their computers (this is rare) and see a preview of what the data in the file looks like. In step two, they can indicate if the file is delimited (as above) or fixed-width (where values are stored in columns of constant size specified within the file); which—if any—row contains the variable names; and the decimal symbol. Note that some forms of fixed-width files may not be supported. Third, they indicate which line of the file contains the first line of data, whether each line represents a case or a specific given number of variables represents a case, and how many cases to import. This last choice includes the option to import a random sample of cases. Fourth, users specify the delimiter and the text qualifier and determine how to handle leading and trailing spaces in string values. Fifth, users can double-check variable names and formats. Finally, before clicking the “Finish” button, users can choose to save their selections as a *.tpf file to be reused or to paste the syntax (to be discussed later in this chapter).

In all cases, once the importation options have been selected and OK or Finish has been clicked, the data is imported. An output window (see Figure 4) may open with various warnings and details about the importation process, and the Data View window (see Figure 5) will show the data, with variable names at the top of each column. At this point, be sure to save the dataset in a location and with a name you will be able to locate later.

Before users are done setting up their dataset, they must be sure that appropriate variable information is included. When datasets are imported from other statistical programs, they will typically come with variable information. But when they are imported from Excel or CSV files, the variable information must be manually entered, typically from a codebook or related document. Variable information is entered using Variable View. Users can switch between Data View and Variable View by clicking the tabs at the bottom of the screen or using the Ctrl+T key combination. As you can see in Figure 6, a screenshot of a completed dataset, Variable View shows each variable in a row, with a variety of information about that variable. When a dataset is imported, each of these pieces of information need to be entered by hand for each variable. To move between columns by key commands, use the tab key; to open variable information that requires a menu for entry, click the space bar twice.

A screenshot of variable view in SPSS. Details are provided in the text.

Name requires that each variable be given a short name, without any spaces. There are additional rules about names, but in short, names should be primarily alphanumeric in nature and cannot be words or use symbols that have meaning for the underlying computer processing. Names can be entered directly.
Type specifies the variable type. To open up the menu allowing the selection of variable types, click on the cell, then click on the three dots [.…] that appear on the right side of the cell. Users can then choose from among numeric, dollar, date, numeric with leading zeros, string, and other variable types.
Width specifies the number of characters of width for the variable itself in data storage, while decimals specifies how many decimal places the variable will have. These can both be entered or edited directly or in the dialog box for Type.

more completely what the variable is measuring. It can be entered directly.

Missing provides for the indication that particular values—like “refused to answer”—should be treated by the SPSS software as missing data rather than as analytically useful categories. Clicking the three dots [.…] opens a dialog box for specifying missing values. When there are no missing values, “no missing values” should be selected. Otherwise, users can select “discrete missing values” and then enter three specific missing values—the numerical values, not the value labels—or they can elect “range plus one optional discrete missing value” to specific a range from low to high of missing values, optionally adding an additional single discrete value.
Columns specifies the width of the display column for the variable. It can be entered directly.
Align specifies whether the variable data will be aligned right, center, or left. Users can click in the cell to make a menu appear or can press spacebar twice and then use arrows to select the desired alignment.
Measure permits the indication of level of measurement from among nominal, ordinal, and scale variables. Users can click in the cell to make a menu appear or can press spacebar twice and then use arrows to select the desired level of measurement. Note that measure is often wrong in datasets and analysts should not rely on it in determining the level of measurement for selection of statistical tests; SPSS does not use this characteristic when running tests.
Some datasets will have additional criteria. For example, the dataset shown in Figure 6 has a column called origsort which displays the original sort order of the dataset, so that if an analyst sorts the variables they can be returned to their original order.

When entering variable information, it is especially important to include Name, Label, and Values and be sure Type is correct and any Missing values are specified. Other variable information is less crucial, though clearly it is better to fully specify all variable information. Once all variable information is entered and double-checked and the dataset has been saved, it is ready for use.

When a user first opens SPSS, they are greeted with the “Welcome Dialog” (see figure 9). This dialog provides tips, links to help resources, and options for creating a new file (by selecting “new dataset”) or opening recently used files. There is a checkbox for turning off the Welcome Dialog so that it will not be shown in the future.

Alt+D toggles the "don't show this dialog in the future option" on the Welcome Dialog; user using keyboard shortcuts will find it easier to disable and then navigate to the menus to open or create files.

When the Welcome Dialog is turned off, SPSS opens with a blank file. Going to File → Open → Data (Alt+F, O, D) brings up the dialog for opening a data file; the Open menu also provides for opening other types of files, which will be discussed below. Earlier in this chapter, the differences between Data View and Variable view were discussed; when you open a data file, be sure to observe which view you are using.

Alt+N moves the cursor to the Find box, where you can type the text you are searching for. Tab is needed to switch between find and replace. Clicking in variable view behind the dialog box and then using tab moves the focus from column to column in variable view: you will typically want to search either Name or Label. Alt+C toggles "Match case." Alt+H opens additional options, including match must be contained in the cell (Alt+O), match must be to the entire cell (Alt+L); cell begins with match (Alt+B); cell ends with match (Alt+W); search down (Alt+D); and search up (Alt+U). Alt+F clicks the "Find Next" button.

It can be useful to be able to search for a variable or case in the datafile. There are two main ways to do this, both under the Edit menu (Alt+E). [1] The Edit menu offers Find and Go To. Find, which can also be accessed by pressing Ctrl+F, allows users to search for all or part of a variable name. Figure 10 displays the Search dialog, with options shown after clicking on the “show options” button. (Users can also use the Replace function, but this carries the risk of writing over data and so should be avoided in almost all cases.) Be sure to select the column you wish to search—the Find function can only examine one column in Variable View at a time. Most typically, users will want to search variable names or labels. The checkbox for Match Case toggles whether or not case (in other words, capitalization) matters to the search. Expanding the options permits users to specify how much and which part of a cell must be matched as well as search order.

Users can also navigate to specific variables by using the Edit → Go to Case (to navigate to a specific case—or row in data view) and Edit → Go to Variable (to navigate to a specific variable—a row in variable view or a column in data view). Users can also access detailed variable information via the tool Utilities → Variables.

Another useful feature is the ability to sort variables and cases. Both types of sorting can be found in the data menu. Variables can be sorted by any of the characteristics in variable view; when sorting, the original sort order can be saved as a new characteristic. Cases can be sorted on any variable.

SPSS Options

The Options dialog can be reached by going to Edit → Options (or Alt+E, Alt+N). There are a wide variety of options available to help users customize their SPSS experience, a few of which are particularly important. First of all, using various dialogs and menus in the program is much easier if the options Variable List—Display Names (Alt+N) and Alphabetical (Alt+H) are selected under General. You can also change the display language for both the user interface and for output under Language, change fonts and colors for output under Viewer, set number options under Data; change currency options under Currency; set default output for graphs and charts under Charts; and set default file locations for saving files under File locations. While most of these options can be left on their default settings, it is really important for most users to set variables to display names and alphabetical before use. Options will be preserved if you use the same computer and user account, but if you are working on a public computer you should get in the habit of checking every time you start the program.

Getting More Out of SPSS

So far, we have been working only with Data View and Variable View in the main dataset window. But when researchers produce the results of an analysis, these results appear in a new window called Output—IBM SPSS Statistics Viewer. New Output windows can be opened from the File menu by going to Open → Output or from the Window menu by selecting “Go to Designated Viewer Window” (the later command also brings the output window to the foreground if one is already open). Output will be discussed in more detail when the results of different tests are discussed. For now, note that output can be saved in *.spv format, but this format can only be viewed in SPSS. To save output in a format viewable in other applications, go to File → Export, where you can choose a file location and a file format (like Word, PowerPoint, HTML, or PDF). Individual output items can also be copied and pasted.

SPSS also offers a Syntax viewer and editor, which can also be accessed from both the File and Window menus. While syntax is beyond the scope of this text, it provides the option for writing code (kind of like a computer program) to control SPSS rather than using menus and buttons in a graphical user interface. Experienced users, or those doing many similar repetitive tasks, often find working via syntax to be faster and more efficient, but the learning curve is quite steep. If you are interested in learning more about how to write syntax in SPSS, Help → Command Syntax Reference brings up a very long document detailing the commands available.

Finally, the Help menu in SPSS offers a variety of options for getting help in using the program, including links to web resource guides, PDF documentation, and help forums. These tools can also be reached directly via the SPSS website. In addition, many dialog boxes contain a “Help” button that takes users to webpages with more detail on the tool in question.

Go to https://www.baseball-reference.com/ and select 10 baseball players of your choice. In an Excel or other spreadsheet, enter the name, position, batting arm, throwing arm, weight in pounds, and height in inches, as well as, from the Summary: Career section, HR (home runs) and WAR (wins above replacement). Each player should get one row of the Excel spreadsheet. Once you have entered the data, import it into SPSS. Then use Variable View to enter the relevant information about each variable—including value labels for position, batting arm, and throwing arm. Sort your cases by home runs. Finally, save your file.

Media Attributions

import menu
import excel © IBM SPSS is licensed under a All Rights Reserved license
import csv © IBM SPSS is licensed under a All Rights Reserved license
output window © IBM SPSS is licensed under a All Rights Reserved license
spss data view © IBM SPSS is licensed under a All Rights Reserved license
variable-view © IBM SPSS is licensed under a All Rights Reserved license
value labels © IBM SPSS is licensed under a All Rights Reserved license
missing values © IBM SPSS is licensed under a All Rights Reserved license
welcome dialog © IBSM SPSS is licensed under a All Rights Reserved license
find and replace © IBM SPSS is licensed under a All Rights Reserved license
Note that "Search," another option under the Edit menu, does not search variables or cases but instead launches a search of SPSS web resources and help files. ↵

A data type that represents non-numerical data; string values can include any sequence of letters, numbers, and spaces.

The possible levels or response choices of a given variable.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

IBM SPSS Statistics provides a powerful suite of data analytics tools which allows you to quickly analyze your data with a simple point-and-click interface and enables you to extract critical insights with ease. During these times of rapid change that demand agility, it is imperative to embrace data driven decision-making to improve business outcomes. Organizations of all kinds have relied on IBM SPSS Statistics for decades to help solve a wide range of business and research problems .

Explore SPSS Statistics with our interactive tutorials

SPSS Statistics offers a comprehensive set of capabilities in support of the entire analytical process from data preparation to analysis and reporting. It simplifies and accelerates data analytics by offering a simple menu-driven user interface that allows you to get to insights with just a few clicks, without any coding.

Interactive, hands-on tutorials are one of the best ways to experience SPSS Statistics. Here are a few SPSS Statistics learning resources that can get you started:

Statistics 101

If you’re just starting out with IBM Statistics, this introductory tutorial can help you get up to speed. You’ll learn about descriptive statistics, variance, probability, correlation and data visualization. It starts you off gently with a coverage of the fundamentals including descriptive statistics and moves you through five self-paced modules that take you through the steps to data wrangling and more.

Get more information on the SPSS Statistics 101 tutorial here .

View SPSS Statistics in action

IBM experts have put together an array of demo videos and assets that allow you to deep-dive into powerful statistical procedures and tools included in this versatile statistical software. We recommend starting with the overview video below to explore the power of statistical analysis to enable timely and accurate decisions for your organization.

To help you along your learning journey, we have provided a detailed video library that includes demo videos around advanced statistics, data preparation and popular procedures like Regression. Visit the video library .

Are you wondering if SPSS Statistics enables you to deliver visualizations and other output? Watch this video about the output and visualization capabilities of SPSS Statistics to learn how to customize pivot tables and create publication-ready charts, tables and decision trees. Visit the IBM media center to view it.

These are just a few of the tutorials available to help you learn and become proficient with SPSS Statistics. For more basic to advanced tutorials and feature documentation, visit the SPSS product documentation .

Get more from SPSS Statistics with new algorithms and visualization tools

IBM recently launched SPSS Statistics 29. The latest version includes new statistical algorithms, enhancements to existing statistical procedures, new Relationship Maps for data visualization, and several usability improvements to make SPSS Statistics more user friendly for novices and experts alike. You can read all about the new release in this data sheet .

Ready to dive deeper into SPSS Statistics on your own and start turbocharging your research and business analysis?

Try SPSS Statistics at no cost for 30 days.

Get yearly subscription and save more

IBM offers simple subscription options to help you easily get started with SPSS Statistics and scale as your requirements grow. You can even choose the 12 months auto-renewal plan and save 10% on subscription and add-ons .

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Mastering Quantitative Data Analysis in SPSS: A Comprehensive Guide for Students

Quantitative Data Analysis in SPSS: A Roadmap for Students

In the dynamic and constantly evolving field of academic research, the significance of quantitative data analysis is paramount. As researchers grapple with vast and complex datasets, the ability to harness the power of quantitative analysis becomes a linchpin for extracting meaningful insights. This analytical approach not only enables a deeper understanding of patterns and trends within the data but also empowers researchers to make well-informed decisions and draw accurate conclusions. Quantitative data analysis serves as a robust tool in the researcher's toolkit, offering a systematic and objective means of scrutinizing information. It goes beyond the surface-level observations, delving into the statistical intricacies that underlie patterns within datasets. Whether you need help with your SPSS homework or are simply looking to enhance your proficiency in quantitative data analysis, mastering tools like SPSS is essential for conducting rigorous and impactful research in various academic disciplines.

By employing various statistical techniques and tests, researchers can uncover relationships between variables, identify trends, and even predict future outcomes. This analytical prowess is particularly crucial in navigating the complexity of academic inquiries, where the need for precision and reliability in findings is paramount. For students venturing into the intricate realm of data analysis, the Statistical Package for the Social Sciences (SPSS) emerges as a beacon of accessibility and utility. Recognized globally as a cornerstone software for statistical analysis, SPSS caters to a wide range of users, from novices to seasoned statisticians. Its user-friendly interface, coupled with a diverse array of statistical tools, makes it an ideal choice for those seeking to delve into quantitative data analysis without being overwhelmed by complex programming languages or convoluted interfaces.

This blog endeavors to be a guiding light for students aspiring to master the art of quantitative data analysis using SPSS. By offering a comprehensive roadmap, it aims to demystify the intricacies of statistical analysis and provide a structured approach to learning and applying SPSS functionalities. The overarching goal is to empower students with the skills necessary to approach data-driven assignments with confidence and competence. The roadmap outlined in this blog spans various key facets of quantitative data analysis using SPSS. It begins by familiarizing students with the SPSS environment, ensuring they navigate the software with ease. Importantly, it emphasizes the significance of data preparation, laying the foundation for accurate and meaningful analyses. Descriptive statistics and visualization techniques are then explored, providing students with the tools to summarize and present their data effectively. Moving deeper into the world of inferential statistics, the blog introduces students to hypothesis testing and regression analysis, pivotal components of drawing valid conclusions from data. Advanced techniques like factor analysis and cluster analysis are also unveiled, opening doors to more nuanced and intricate analyses. Additionally, the blog highlights the potential for customization through SPSS syntax, allowing advanced users to streamline workflows and conduct complex analyses efficiently.

Understanding the SPSS Environment

Understanding the SPSS environment is a foundational step for any student embarking on the journey of quantitative data analysis. This section provides a comprehensive insight into two critical aspects: navigating the interface and importing/preparing data.

Navigating the Interface

Before delving into the intricacies of data analysis, it is imperative to acquaint oneself with the user-friendly SPSS interface. Designed with intuitiveness in mind, SPSS presents users with a layout comprising menus, toolbars, and a data editor. Each element serves a specific purpose, contributing to the overall ease of use for researchers and analysts.

Menus and Toolbars

SPSS boasts an array of menus and toolbars that act as gateways to its extensive functionalities. These menus, often organized categorically, offer a range of options for data manipulation, analysis, and visualization. The toolbars, strategically placed for quick access, provide shortcuts to frequently used commands. By familiarizing oneself with these menus and toolbars, users gain efficiency in navigating the software and executing tasks seamlessly.

Variable View and Data View Tabs

The 'Variable View' and 'Data View' tabs constitute the heart of the SPSS interface, providing a dynamic workspace for users. In 'Variable View,' researchers define and manage variables, specifying their types, labels, and measurement scales. This step is crucial for ensuring that the software interprets and analyzes data accurately. On the other hand, 'Data View' presents the dataset in a spreadsheet format, allowing users to input, modify, or review the actual data. Understanding the distinction and interaction between these views is fundamental for organizing and exploring datasets effectively.

Importing and Preparing Data

A robust analysis hinges on the quality of the data under scrutiny. SPSS facilitates this by offering a straightforward process for importing and preparing diverse datasets.

Importing Various File Formats

SPSS supports a multitude of file formats, including Excel, CSV, and more. Learning to import data seamlessly from these formats into SPSS is a crucial skill. This capability ensures that researchers can work with data generated from various sources, promoting versatility in analysis. As data comes in different structures, this feature enables users to adapt and integrate information seamlessly into their projects.

Preprocessing the Dataset

Preparing a dataset for analysis involves addressing several considerations. Handling missing values is a critical step in maintaining data integrity. SPSS provides tools to identify and manage missing data effectively. Checking for outliers, another essential task, involves assessing data points that deviate significantly from the norm. SPSS equips users with statistical measures and visualizations to identify and manage outliers appropriately. Additionally, transforming variables to meet specific analysis requirements is part of the preprocessing stage. This might include converting variables to different scales or creating new variables based on existing ones. A well-prepared dataset ensures that subsequent analyses are accurate and meaningful, setting the stage for informed decision-making.

Descriptive Statistics and Visualization

Descriptive statistics and data visualization are integral components of quantitative data analysis, playing a pivotal role in unraveling the intricate patterns and trends within datasets. Let's delve deeper into each aspect, exploring the significance of descriptive statistics and the art of visualization in the context of SPSS.

Descriptive Statistics

Descriptive statistics serve as the bedrock of quantitative analysis, offering a concise summary of the essential characteristics within a dataset. In the realm of SPSS, mastering these statistical measures is fundamental for any student engaging in data analysis. SPSS provides a robust set of tools designed to calculate key measures, including the mean, median, and standard deviation.

The mean, or average, is a measure of central tendency that represents the arithmetic average of all values in a dataset. It provides a quick overview of the central position of the data. The median, on the other hand, offers an alternative measure of central tendency that is less sensitive to extreme values, making it particularly useful for skewed distributions. Standard deviation, a measure of variability, indicates how spread out the values in a dataset are relative to the mean. Together, these statistics paint a comprehensive picture of the central tendency and dispersion within the data.

Data Visualization

Data Visualization emerges as a powerful companion to descriptive statistics. SPSS provides an array of graphical tools, each tailored to convey different aspects of the data. Histograms, for example, offer a visual representation of the distribution of a variable, providing insights into its shape and central tendency. This is particularly useful when dealing with continuous data, allowing researchers to discern patterns that might be less apparent in tabular form. Scatterplots, another visualization tool in SPSS, enable the exploration of relationships between two variables. By plotting points on a graph, researchers can identify patterns, trends, or potential outliers. This visual representation aids in the interpretation of correlations and associations, enhancing the depth of analysis.

Mastering the art of visualization not only facilitates a deeper understanding of the data but also serves as a powerful means of communication. Researchers often need to convey complex findings to diverse audiences, and visualizations can simplify intricate concepts. A well-crafted graph or chart can tell a compelling story, making it easier for others to grasp the essence of the data without delving into intricate statistical details.

Inferential Statistics: Unleashing the Power of Tests

Inferential statistics stands as a powerful realm within quantitative analysis, providing researchers with the tools to draw broader conclusions about populations based on sample data. This section explores two pivotal components of inferential statistics in SPSS—hypothesis testing and regression analysis.

Hypothesis Testing

At the heart of inferential statistics lies hypothesis testing, a fundamental process for researchers to make informed decisions about their data. SPSS facilitates this critical step by offering a diverse array of statistical tests. These tests, including t-tests, ANOVA (Analysis of Variance), and chi-square tests, are tailored to different research scenarios. T-tests are particularly useful when comparing the means of two groups, providing insights into whether observed differences are statistically significant. ANOVA, on the other hand, extends this comparison to multiple groups, assessing if there are significant differences among them.

Chi-square tests, often employed in categorical data analysis, help researchers understand the association between variables. Crucial to wielding these tests effectively is a deep understanding of their application. Knowing when to employ a t-test versus an ANOVA can significantly impact the accuracy and relevance of your findings. Furthermore, comprehending the nuances of interpreting p-values, confidence intervals, and effect sizes is essential for drawing meaningful conclusions from hypothesis tests.

Regression Analysis

Moving beyond hypothesis testing, regression analysis in SPSS emerges as a potent tool for researchers aiming to unravel intricate relationships between variables. Unlike descriptive statistics that merely summarize data, regression allows for predictive modeling. SPSS provides a user-friendly platform for researchers to delve into this complex analysis. Regression analysis assesses the influence of one or more independent variables on a dependent variable. This technique becomes invaluable when attempting to predict outcomes based on a set of predictors. Within SPSS, researchers navigate through regression coefficients, evaluating the strength and direction of relationships.

Assessing model fit ensures that the chosen regression model adequately represents the data, while identifying outliers becomes crucial for refining the model's accuracy. By mastering regression analysis in SPSS, researchers can unearth patterns and trends within their data, providing a deeper understanding of the factors influencing their variables of interest. Whether exploring economic trends, human behavior, or scientific phenomena, regression analysis proves indispensable for researchers seeking not only to understand but also to predict outcomes.

Advanced Techniques and Custom Analysis

In the realm of quantitative data analysis, delving into advanced techniques and custom analyses elevates researchers' capabilities to unravel intricate patterns and relationships within datasets. Two prominent tools in SPSS that facilitate this advanced exploration are Factor Analysis and Cluster Analysis.

Factor Analysis and Cluster Analysis

As researchers move beyond the basics, Factor Analysis and Cluster Analysis emerge as powerful instruments within the SPSS toolkit. Factor Analysis, a multivariate statistical method, plays a pivotal role in uncovering latent variables that may not be directly observable but influence the observed variables. This technique identifies underlying structures in the dataset, helping researchers condense complex information into a more manageable form. For instance, in social sciences, Factor Analysis might reveal latent constructs like socioeconomic status or psychological traits that contribute to observed behaviors.

Cluster Analysis, on the other hand, is instrumental in grouping similar cases based on selected variables. This method enables the identification of patterns and similarities within the data, highlighting clusters or subgroups that might share common characteristics. In marketing research, for instance, Cluster Analysis could be employed to segment customers based on their purchasing behavior, allowing businesses to tailor their strategies to specific consumer groups. These advanced techniques offer a deeper understanding of the nuances present in datasets, allowing researchers to move beyond surface-level observations.

Customizing Analysis with Syntax

For those seeking to harness the full potential of SPSS, mastering syntax becomes a game-changer. SPSS syntax refers to a series of commands written in a specialized language that allows users to automate tasks and conduct more intricate analyses than what the graphical user interface (GUI) offers. This level of customization empowers advanced users to tailor their analyses precisely to their research questions. By writing and executing syntax commands, researchers can automate repetitive tasks, ensuring consistency and reducing the likelihood of errors. For instance, if a researcher needs to perform a complex analysis on multiple datasets, using syntax allows them to create a streamlined and replicable process.

This not only saves time but also enhances the reproducibility of the analysis, a crucial aspect of robust research methodology. Moreover, delving into syntax opens the door to more sophisticated analyses that may not be readily available through the graphical interface. Users can implement complex statistical procedures, manipulate data structures, and even create customized visualizations, providing a level of flexibility that is indispensable for advanced research endeavors. While it may seem daunting initially, the efficiency gained through syntax mastery pays dividends in the form of enhanced analytical capabilities and a more nuanced understanding of the data.

In conclusion, mastering quantitative data analysis in SPSS is a valuable skill for students embarking on research journeys. The roadmap provided in this blog offers a structured approach to understanding the SPSS environment, conducting descriptive and inferential analyses, and exploring advanced techniques. By following this guide, students can gain confidence in tackling assignments and contribute meaningfully to the world of academic research. As technology continues to advance, proficiency in tools like SPSS becomes increasingly essential for staying ahead in the field of data analysis. Continuous practice, exploration, and a curious mindset will pave the way for students to excel in quantitative data analysis using SPSS.

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Understanding the Basics of SPSS

Data entry and data import, descriptive statistics, hypothesis testing, correlation and regression, data visualization, data transformation and variable recoding.

Understanding the basics of SPSS is crucial for any data analysis project. SPSS (Statistical Package for the Social Sciences) is a powerful software widely used in various fields to perform statistical analyses and interpret data. It provides an intuitive interface, making it accessible to both beginners and experienced researchers. By learning the fundamentals of data entry, importing, and cleaning, users can ensure accurate and reliable analyses. Moreover, mastering descriptive statistics, hypothesis testing, and data visualization will enable researchers to draw meaningful insights from their data. This foundational knowledge sets the stage for more advanced statistical analyses and a successful SPSS journey.

The following topics are essential to know:

Data entry and data import are critical steps in the SPSS workflow. Properly organizing and entering data is essential for accurate analysis and valid results. SPSS offers various methods to input data, including manual entry or importing from external sources like Excel or CSV files. Understanding how to handle missing data and outliers during this process is crucial to ensure data integrity. Additionally, knowing how to label variables and assign value labels improves data clarity and interpretation. By mastering data entry and import, researchers can avoid data errors, save time, and lay a solid foundation for a successful SPSS assignment.

Some of the assignments you can expect on data entry and data import include:

Data Entry Accuracy Assessment: To solve a data entry accuracy assessment assignment, carefully enter the provided dataset into SPSS while minimizing errors. Double-check the data for accuracy and correct any mistakes. Use validation techniques such as cross-referencing with the original data source. Analyze any discrepancies and document your approach to ensure transparency. This exercise helps improve data entry skills and emphasizes the importance of accurate data handling for reliable statistical analysis.
Data Import and Cleaning: To solve a data import and cleaning assignment, start by importing the dataset into SPSS from various file formats (Excel, CSV). Address missing values, duplicates, and outliers. Check data consistency and validity. Employ functions for data cleaning, like recoding variables or imputing missing values. Document your steps clearly. Lastly, validate the cleaned dataset for accuracy and usability before proceeding with any further analysis.
Merging Datasets: To solve an assignment on merging datasets in SPSS, follow these steps. First, ensure datasets have a common identifier (e.g., ID). Use the "Merge Files" function, select appropriate merge type (e.g., inner, outer), and identify the matching variable. Check for duplicate records and resolve inconsistencies. Use the "Split File" option for separate analyses. Validate the merged dataset by comparing results with the original files. A successful assignment requires understanding data relationships and using SPSS tools accurately for a comprehensive analysis.
Longitudinal Data Handling: To solve an assignment on longitudinal data handling, first, understand the dataset's structure and time points. Organize the data in SPSS, ensuring it's in the appropriate format (wide or long). Use the "Restructure Data" or "Split File" functions to perform time-series analysis. Apply statistical techniques such as repeated measures ANOVA or growth curve modeling to examine trends and changes over time. Finally, interpret and present the findings, showcasing a clear understanding of the data's longitudinal nature and demonstrating analytical skills.

Descriptive statistics play a fundamental role in data analysis by providing a concise summary of the main features within a dataset. These statistics, including measures like mean, median, mode, standard deviation, and variance, offer valuable insights into the central tendency, spread, and distribution of the data. Understanding descriptive statistics in SPSS allows researchers to gain a clear understanding of their data before moving on to more complex analyses. Additionally, visual representations, such as histograms and box plots, help researchers identify patterns and outliers, making it easier to make informed decisions and draw meaningful conclusions from the data at hand.

Here are the types of assignments you will get on descriptive statistics and how you can solve them:

Central Tendency Assignment: To solve a central tendency assignment, import the dataset into SPSS, calculate the mean, median, and mode using the "Descriptive" option, and interpret the results. The mean represents the average, the median is the middle value, and the mode is the most frequent value in the dataset, providing insights into the central tendencies of the data.
Measures of Dispersion Assignment: To solve a measures of dispersion assignment, import the dataset into SPSS, then calculate the range, standard deviation, and variance using the "Descriptive" option. Interpret the results to understand the spread of the data, identifying the variability and distribution characteristics.
Frequency Distribution Assignment: To solve a frequency distribution assignment, import the dataset into SPSS, then use the "Frequencies" option to generate frequency tables for the variables of interest. Additionally, create histograms to visualize the distribution. Analyze the frequency tables and histograms to identify patterns and trends in the data.
Correlation Assignment: To solve a correlation assignment, first, import the dataset into SPSS. Choose the variables you want to explore for correlation. Use the "Correlations" option to calculate correlation coefficients. Interpret the results to determine the strength and direction of the relationship between the variables, considering statistical significance using p-values.

Hypothesis testing is a fundamental concept in statistics and plays a pivotal role in research and decision-making processes. In SPSS, researchers can examine whether their hypotheses are supported or refuted based on empirical evidence. By setting up null and alternative hypotheses and using appropriate statistical tests like t-tests or ANOVA, analysts can draw conclusions about the population from a sample. Understanding p-values, significance levels, and the correct interpretation of results are essential to avoid drawing incorrect conclusions. Hypothesis testing in SPSS empowers researchers to make data-driven decisions and contributes to the validity and reliability of their research findings.

Types of Hypothesis Testing Assignments:

One-Sample T-Test Assignment: In this assignment, you are given a dataset with a single sample, and you need to test whether the sample mean differs significantly from a hypothesized value. Use SPSS to perform a one-sample t-test. Enter the data, set the null hypothesis, select the t-test option, and interpret the result based on the p-value and significance level.
Independent Samples T-Test Assignment: In this assignment, you are provided with two separate datasets representing independent groups, and you need to determine if there is a significant difference in the means of the two groups. Input the data, set the null hypothesis, select the t-test option, and interpret the outcome based on the p-value and significance level.
Paired Samples T-Test Assignment: In this assignment, you are given two related datasets, and your task is to examine if there is a significant difference between the means of the paired samples. Use SPSS to execute a paired samples t-test. Enter the paired data, set the null hypothesis, select the t-test option, and interpret the results using the p-value and significance level.
One-Way ANOVA Assignment: In this assignment, you are provided with a dataset containing multiple groups, and you need to ascertain if there are significant differences in means across those groups. Employ SPSS to perform a one-way ANOVA. Enter the data, set the null hypothesis, select the ANOVA option, and interpret the result based on the p-value and significance level. Additionally, post-hoc tests may be required to identify specific group differences.

Correlation measures the relationship between two or more variables, while regression predicts the value of a dependent variable based on one or more independent variables. These topics are often encountered in research and data analysis. Knowing how to perform correlation and regression analyses in SPSS will enable you to explore relationships and make predictions from your data.

Simple Correlation Analysis Assignment: For this assignment, calculate and interpret the correlation coefficient between two variables using SPSS. Identify the strength and direction of the relationship and present your findings in a clear and concise manner.
Multiple Regression Assignment: In this task, perform multiple regression analysis in SPSS to predict a dependent variable based on two or more independent variables. Select relevant variables, run the regression, and interpret the coefficients to draw meaningful conclusions.
Correlation and Regression Comparison Assignment: Compare and contrast correlation and regression analyses in SPSS. Explain their purposes, assumptions, and interpretations. Provide examples to demonstrate their applications in different scenarios.
Real-Life Data Analysis Assignment: Obtain a dataset with variables suitable for correlation and regression analysis. Clean the data, perform the appropriate analysis in SPSS, and interpret the results. Discuss the practical implications of the findings in a real-world context.

Data visualization plays a pivotal role in understanding complex datasets and communicating insights effectively. SPSS offers a wide range of visualization options, such as histograms, scatter plots, and bar charts, allowing researchers to present data in a visually engaging manner. By choosing the appropriate charts, researchers can identify patterns, trends, and outliers, making it easier to draw conclusions from the data. Furthermore, visualizations aid in conveying findings to a broader audience, making complex statistical information more accessible and comprehensible. A skillful use of data visualization in SPSS enhances the clarity and impact of research results, thereby strengthening the overall research narrative.

Types of data visualization assignments:

Creating Descriptive Visualizations: In this type of assignment, you may be asked to generate descriptive visualizations for a given dataset using SPSS. Start by importing the data and exploring its variables. Use appropriate chart types such as histograms, bar charts, and pie charts to visualize the distribution of categorical and numerical variables. Customize the visuals by adding labels, titles, and color schemes to improve clarity. For numerical data, consider box plots and scatter plots to identify outliers and patterns. Present the visualizations along with a brief interpretation of the main insights.
Comparative Visualizations: In a comparative visualization assignment, you might need to compare two or more groups or variables. Use grouped bar charts, stacked bar charts, or line graphs to demonstrate the differences between the groups. Apply color coding and legends to make the visualizations more informative. For more advanced analyses, consider using heatmaps or radar charts to display multivariate comparisons. Explain the key findings and any significant trends or patterns observed in the data.
Time-Series Visualizations: Time-series visualizations involve displaying data points over time. Use line graphs or area charts to represent the trends and changes in the data over specific time intervals. Pay attention to the x-axis labels and format to ensure the time is displayed accurately. Utilize different line styles or colors for multiple time series. If applicable, add annotations or callouts to highlight important events or occurrences during the time period. Analyze the visualizations to draw conclusions about any temporal patterns or fluctuations.
Geospatial Visualizations: In geospatial visualization assignments, you will be working with spatial data and representing it on maps. Import the geographic data into SPSS and link it with your dataset. Use choropleth maps to display numerical data for different regions or territories. You can also use bubble maps to show variations in data based on the size of the bubbles in different locations. Customize the map legend, color scales, and data ranges to enhance the visualization's clarity. Analyze the geospatial visualizations to draw insights about spatial patterns and regional differences in the data.

Data transformation and variable recoding are vital skills in SPSS for preparing data for analysis. Data transformation involves converting variables into different formats or scales, such as logarithmic or square root transformations, to meet statistical assumptions. Variable recoding allows researchers to combine or modify existing variables, simplifying the analysis. These techniques are useful when dealing with skewed data or categorical variables. By mastering these methods, researchers can enhance the accuracy and reliability of their analyses and derive more insightful results from their data.

Log Transformation for Skewed Data: To solve an assignment on log transformation for skewed data, first, identify the skewed variable. Calculate the natural logarithm (ln) of each value in the variable to create a new transformed variable. This process helps normalize the data, making it suitable for analysis that requires normally distributed data.
Recoding Categorical Variables: To solve an assignment on recoding categorical variables, start by identifying the specific categorical variable and the desired outcome (e.g., binary or multi-category recoding). Create a new variable, assign codes to each category accordingly, and recode the data. Validate the recoded variable's accuracy and use it in subsequent analyses for simplified interpretations.
Standardization of Variables: To solve an assignment on standardization of variables, calculate the mean and standard deviation for each variable. For each data point, minus the mean and divide the answer by the standard deviation. This process will transform the variables into a common scale with a mean of 0 and a standard deviation of 1, allowing for fair comparisons and unbiased analysis.
Binning Continuous Variables: To solve an assignment on binning continuous variables, first, determine suitable bin intervals based on the data's distribution and context. Then, divide the range of the continuous variable into these intervals and create a new categorical variable. Assign data points to the corresponding bins, facilitating analysis and interpretation in distinct groups.

Mastering the essential topics in SPSS and knowing how to approach SPSS assignments will empower you to handle various data analysis tasks confidently. By understanding the basics of SPSS, data entry, hypothesis testing, correlation, regression, data visualization, and data transformation, you will be well-prepared to tackle a wide range of statistical problems. Through practice and hands-on experience with SPSS, you can enhance your analytical skills and become proficient in using this powerful statistical software for research and data analysis.

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Developing country work experience and/or familiarity with emergency.
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Monitoring & Evaluation Officer, (NO-2), Maputo, Mozambique, Temporary Appointment (364 days) in Mozambique

An exciting opportunity has arisen within UNICEF Mozambique for a passionate and committed Monitoring and Evaluation Officer, NOB level based in Maputo (open to nationals only) seeking new challenges in a rewarding programming context to bring results for children. The Monitoring and Evaluation Officer reports to the Chief, Child Health and Nutrition, for general guidance and supervision.

Open access
Published: 23 August 2024

Unveiling missed nursing care: a comprehensive examination of neglected responsibilities and practice environment challenges

Somayeh Babaei 1 ,
Kourosh Amini ORCID: orcid.org/0000-0003-2363-894X 2 &
Farhad Ramezani-Badr 3

BMC Health Services Research volume 24 , Article number: 977 ( 2024 ) Cite this article

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Metrics details

The global variable of missed nursing care and practice environment are widely recognized as two crucial contextual factors that significantly impact the quality of nursing care. This study assessed the current status of missed nursing care and the characteristics of the nursing practice environment in Iran. Additionally, this study aimed to explore the relationship between these two variables.

We conducted an across-sectional study from May 2021 to January 2022 in which we investigated 255 nurses. We utilized the Missed Nursing Care Survey, the Nursing Work Index-Practice Environment Scale, and a demographic questionnaire to gather the necessary information. We used the Shapiro‒Wilk test, Pearson correlation coefficient test, and multiple linear regression test in SPSS version 20 for the data analyses.

According to the present study, 41% of nurses regularly or often overlooked certain aspects of care, resulting in an average score of 32.34 ± 7.43 for missed nursing care. It is worth noting that attending patient care conferences, providing patient bathing and skin care, and assisting with toileting needs were all significant factors contributing to the score. The overall practice environment was unfavorable, with a mean score of 2.25 ± 0.51. Interestingly, ‘nursing foundations for quality of care’ was identified as the sole predictor of missed nursing care, with a β value of -0.22 and a p -value of 0.036.

Conclusions

This study identified attending patient care interdisciplinary team meetings and delivering basic care promptly as the most prevalent instances of missed nursing care. Unfortunately, the surveyed hospitals exhibited an undesirable practice environment, which correlated with a higher incidence of missed nursing care. These findings highlight the crucial impact of nurses’ practice environment on care delivery. Addressing the challenges in the practice environment is essential for reducing instances of missed care, improving patient outcomes, and enhancing overall healthcare quality.

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Introduction

Missed Nursing Care (MNC) is the failure to provide any necessary aspect of patient care, partially or entirely, or delay in delivering it [ 1 ]. MNCs can have severe side effects on patients, including safety threats [ 2 ] and even mortality [ 3 ]. It also significantly decreases the quality of nursing care [ 4 ]. MNC can also have adverse and destructive effects on nurses, including decreased job satisfaction, increased absenteeism, and the intention to leave their jobs [ 5 ]. As a result, MNCs have become a key focus of nursing researchers in recent years and are widely recognized as a significant global problem [ 6 ].

A literature review revealed that MNCs are multidimensional and vary significantly in frequency and elements across different research communities [ 7 ]. In Iran, information regarding MNCs is limited. According to our search, only one reliable study [ 8 ] has been conducted on this topic in the last five years. Chegini et al. conducted a study that showed that the percentage of participants who missed care was 72.1%. The most common tasks of missed nursing care included patient discharge planning and teaching, emotional support for patients and their families, interdisciplinary care conferences, and patient education regarding their illness, tests, and diagnostic procedures. Although the study by Chegini et al. has provided valuable information, the generalizability of its results is limited due to its small sample size. The study included nurses from only medical-surgical wards and used the census sampling method.

MNC is influenced by various individual and organizational factors [ 9 ]. In a systematic review, Chiappinotto et al. identified significant factors contributing to MNC, such as low nurse-to-patient ratios, high workloads, and poor work environments. Moreover, stress, job dissatisfaction, and inadequate education among nurses were recognized as crucial elements. Furthermore, patient clinical instability was found to further worsen MNC [ 10 ]. However, some researchers argue that organizational and environmental factors are more influential in causing MNC than individual factors [ 11 ].

Another influential organizational variable on nursing performance is the practice environment (PE) [ 12 ]. PE in nursing is inclusive of material and human resources, a cooperative environment, and other elements related to the environment that directly or indirectly affect how care is provided [ 13 ]. PE is involved in nurses’ burnout [ 14 ], job satisfaction, stay in nursing [ 15 ], and overall quality of nursing care [ 16 ]. Like in MNCs, evidence suggests that PE varies across different hospitals and wards within a hospital [ 17 ]. For instance, a study conducted by Choi & Boyle in the U.S. demonstrated that pediatric wards had more favorable PEs than did medical-surgical wards. However, previous studies have shown that MNCs differ across poor, moderate, and suitable PEs. Weak PE has been found to increase MNCs [ 18 ], while optimal PE reduces MNCs [ 17 ]. Due to the global significance of MNCs and PEs for quality of care and the variability of these two variables due to different sociocultural factors, it is essential to understand the weaknesses of MNCs and PEs in every community thoroughly. Therefore, this study aimed to determine the status of MNCs, the characteristics of PEs, and the relationships between these two variables among nurses working in two teaching hospitals.

The present study was cross-sectional from May 30, 2021, to January 19, 2022. The study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The study included nurses employed in the medical-surgical, emergency, and intensive care units of two major teaching hospitals in Zanjan Province. This province is situated in the northwestern region of Iran and has a population of approximately 1,016,000 people. To be eligible for participation in the study, the nurses needed to meet the following specific inclusion criteria:

A minimum of three months of work experience in the desired ward.

Holding a bachelor’s degree or higher.

Consent to participate in the study was obtained.

We utilized Formula 1 for a finite population to determine the sample size. The values used in this formula were N (total population) = 553, power (the probability of correctly rejecting the null hypothesis) = 0.80, standard deviation (SD) = 13.97, d (margin of error or precision) = 1.2, and Z (standardized value for the corresponding level of confidence) = 1.96. The formula indicated that a minimum sample size of 246 was required based on these variables. During the research, we found that a recent study comparable to our work was conducted by Park et al. [ 18 ]. For our research, we utilized the standard deviation of the variables in Formula 1. Their study recorded the mean and standard deviation of the MNC and PE as 84.06 ± 13.79 and 2.92 ± 0.25, respectively. We included the higher standard deviation (related to MNCs) to ensure a larger sample size. We prepared 270 questionnaires and distributed them among the selected nurses. We also considered the possibility of spoiled questionnaires and distributed extra questionnaires accordingly. Fifteen questionnaires were excluded from the study due to incomplete data, leaving a total of 255 questionnaires that were used for data analysis out of the 270 that were distributed.

We utilized a systematic random method to select the nurses for the study. In the first step, a list of nurses working in the desired wards was taken, and the sampling frame was prepared. In the second step, each nurse was assigned a number from a table of random numbers. This process generated a new sampling frame. In the third step, we calculated the distance between the study samples, denoted as ‘K’, using the formula K = N/n.’ We computed K by dividing the total population (N) of 553 by the sample size (n) of 270, approximately 2. To select the participants, we utilized a systematic random method. A new sampling frame was generated in the first step, as described earlier. The first nurse was selected randomly from this new sampling frame, and the subsequent samples were taken at a distance of two people from the previous nurse.

To collect the data, we used three different questionnaires: (a) a demographic profile form, (b) the Missed Nursing Care (MISSCARE) Survey, and (c) the Nursing Work Index-Practice Environment Scale (NWI-PES). The demographic profile included various variables, including sex, age, marital status, educational degree, work experience, position, shift work, employment type, and ward type.

In this study, we utilized the MISSCARE survey (MISSED) to assess MNC. We chose the MISSED based on its extensive utilization and strong psychometric properties, as evidenced in the literature. As noted by Chiappinotto et al. [ 10 ], 34 out of the 58 studies reviewed utilized a version of the MISSCARE survey, highlighting its reliability and validity in assessing MNC. The MISSCARE Survey consists of two parts: Part ‘A’ and Part ‘B’. Part ‘A’ included the most missed care components, while Part ‘B’ included the reasons for missing nursing care. We utilized part ‘A’ of the questionnaire, which constituted 24 items of the MISSCARE Survey. Each of the 24 items comprises five answer options: 1) rarely or never missed, 2) occasionally missed, 3) frequently missed, 4) always missed, and 5) nonapplicable. Kalisch & Williams included the option of ‘nonapplicable’ to account for nurses who operate in situations where certain care activities may not be performed [ 19 ]. The total score range of this survey is 24–96, where higher scores indicate a greater probability of missed care. In line with the findings of a previous study [ 17 ], we considered the combination of “frequently missed” and “always missed” options as missed care to demonstrate the frequency of missed nursing care. The MISSCARE Survey has undergone psychometric analysis, and its applicability has been approved for the nursing community in Iran [ 20 ]. The internal consistency of the tool was measured based on Cronbach’s alpha coefficient (α = 0.88) in this study.

The psychometric analysis of the NWI-PES has been conducted, and its usage has been approved [ 21 ]. Developed by Lake in 2002 and authorized by the National Quality Forum (NQF), this scale comprises thirty-one items and operates on a four-point Likert scale, with scores ranging from four to one. The response options were strongly agree = 4, somewhat agree = 3, somewhat disagree = 2, and strongly disagree = 1. According to [ 22 ], the possible score range of the whole scale and its subscales is one to four. The NWI-PES comprises five subscales:

The nurses’ participation in hospital affairs was evaluated with nine items.

‘Staffing and resource adequacy’, which includes four items.

The three items used were “Collegial nurse‒physician relations”.

‘Nursing foundations for quality of care’ with ten items.

The five items asked about nurses’ ability, leadership, and support.

A scale midpoint greater than 2.5 is considered an acceptable PE [ 22 ]. The NWI-PES demonstrated high internal consistency, with a Cronbach’s alpha of 0.93. The Cronbach’s alpha for each of the subscales of the NWI-PES was computed. The results were as follows: ‘nurse participation in hospital affairs,’ α = 0.88; ‘nursing foundations for quality-of-care,’ α = 0.72;‘staffing and resource adequacy,’ α = 0.87; ‘collegial nurse‒physician relations,’ α = 0.90; and ‘nurse manager ability, leadership, and support of nurses,’ α = 0.84.

We computed the means and standard deviations of the MNC and PE scores and utilized the Shapiro‒Wilk test to determine the normality of the data distribution. The results revealed that the data followed a normal distribution. We employed the Pearson correlation coefficient to determine the correlation between PEs and MNCs. Furthermore, we conducted a multiple linear regression test to examine whether changes in the MNC score, as the dependent variable, were associated with changes in the PE subscale scores. Before conducting the multiple linear regression analysis, we confirmed that the assumptions were met and evaluated. We confirmed the assumption of independent errors by using the Durbin–Watson test. Homoscedasticity and linearity assumptions were assessed through P-P plots. The hypothesis of multicollinearity was examined by determining the variance inflation factor (VIF) and tolerance [ 23 ]. The VIF ranged from 1.006 (TOL = 0.99) for ‘collegial nurse‒physician relations’ to 1.04 (TOL = 0.96) for ‘nursing foundations for quality-of-care.’ Independent t tests and ANOVA were used to evaluate the associations between demographic variables and MNCs. The statistical analysis of the data was conducted using SPSS software version 24, and a P value lower than 0.05 was used to indicate statistical significance.

Participants’ characteristics

The majority of the participants were females (84.3%), were married (68.2%), and were employed on a 5-year contract (46.7%). The majority of the participants were females (84.3%), were married (68.2%), and were employed on a 5-year contract (46.7%).

In addition, almost all of the participants (95.7%) had a Bachelor of Science in Nursing (BSN) degree, and a significant proportion (45.8%) worked in medical-surgical wards. Most of the respondents (91.4%) were staff nurses, and 89.8% of them worked in rotational shift work. The.

The participants’ average age and work experience were 33.94 ± 7.40 and 9.25 ± 7.14, respectively (Table 1 ).

Missed nursing care

The overall mean score for MNCs, with a score ranging from 24 to 96, was 32.34 ± 7.43. Of the total nurses, 41% reported that they always or frequently missed at least one aspect of nursing care. Based on the findings, the items with the highest mean score in descending order were attending an interdisciplinary patient care conference, patient bathing or skin care, assisting with toileting needs within 5 min of request, mouth care, and feeding the patient when the food was still warm (Table 2 ).

The mean MNC score was significantly greater for male nurses than for female nurses (X̄1 = 36.25, X̄2 = 31.56; t = -3.738, p < 0.001). Other demographic and occupational variables of the nurses, such as age, marital status, degree, work experience, position, rotational shift work, type of employment, and working place, had no significant association with MNCs ( p > 0.05).

Practice environment characteristics

The overall mean score for PE was 2.25 ± 0.51. Among the different subscales of the PE scale, the highest mean score was observed for ‘collegial nurse‒physician relations’ (M = 2.45, SD = 0.72). Furthermore, the mean scores for “nursing foundations for quality of care”, “nurse manager ability, leadership, and support of nurses”, and “nurse participation in hospital affairs” were 2.43 ± 0.58, 2.23 ± 0.65, and 2.16 ± 0.58, respectively. The lowest mean score was observed for ‘staffing and resource adequacy’ (M = 1.81, SD = 0.64).

Correlations between practice environment characteristics and missed care

The study’s results indicate a significant and negative correlation between the mean score of PEs and the overall mean score of MNCs ( r = -0.18, p = 0.002). There was a strong link between the overall mean score of MNCs and two of the five NWI-PES subscales: “nursing foundations for quality of care” ( r = -0.21, p < 0.001) and “nurse manager ability, leadership, and support of nurses” ( r = -0.16, p = 0.006).

Predicting missed nursing care based on practice environment subscales

According to Table 3 , linear regression analysis showed that only “nursing foundations for quality of care” (β = -0.22, p = 0.036) of the five NWI-PES subscales could predict MNC.

The main objective of this study was to determine the status of MNCs, the characteristics of PEs, and the relationships between these two variables among Iranian nurses working in two teaching hospitals. The findings showed that 41% of nurses reported frequently or always missing at least one aspect of nursing care. A systematic review also reported that 55–98% of nurses missed at least one course of nursing care [ 24 ]. However, the overall mean score of MNCs in our study was 32.3. A literature review revealed that our study’s mean MNC score was lower than that reported in the United States, Turkey, and Australia, except for Iceland [ 25 ]. By comparing our study results with those from other countries [ 26 ], it can be concluded that low MNCs were reported in our study. Like in many previous studies, in this study, we used the self-reporting method. The reason for the lower mean score of MNCs in our study compared to that in other studies might be due to two biases: “acquiescence response style” (tendency to respond positively) and “social desirability bias” (tendency to present oneself socially to be acceptable, but it does not fully reflect the reality of the individual). Due to the two biases mentioned earlier, the ‘truth-telling’ in our survey might have been compromised. This is because we used the self-reporting method to collect data, and the nature of MNCs is one of the essential aspects of ethics in nursing. The study findings indicated that patients who participated in interdisciplinary conferences had the highest mean score. However, not attending training classes can decrease knowledge and make nursing care less updated, ultimately reducing the quality of care provided to patients [ 27 ]. This finding is consistent with that of another study conducted in Brazil [ 7 ]. Based on our field experiences and observations, several factors, including the following, seem to play a significant role in missing nursing care:

Time limitation due to a nursing shortage.

Inappropriate timing of training classes or conferences and conflicts in daily schedules.

There is a lack of support and encouragement from managers, especially hospital managers.

Inappropriate and nonequipped venues for classes.

Improper teaching methods and giving lectures instead of using new teaching methods.

There is a lack of proper alert reminders for nurses regarding the date, time, and place of meetings.

Our study revealed that the lowest scores for missed care were related to items such as ‘bedside glucose monitoring as ordered”, ‘peripheral IV/central line site care and assessments according to hospital policy’, and ‘vital signs assessments as ordered.’ The lower scores associated with this care could be attributed to the use of an accurate system for recording patients in patient files and additional unique records above patients’ beds in the current research environment, which helps staff remember and check this care more often. However, these care tasks are crucial parts of a patient’s vital nursing care and should be performed during each work shift to monitor the patient’s hemodynamic status. This information about each patient was provided to the assigned nurse during the shift handover. A lack of ‘blood sugar control’ was also indicated in the studies of Smith et al. [ 17 ] in the U.S.

Our study revealed a low PE score among the participating nurses. Given that nurses have greater responsibility for caring and have essential tasks such as performing technical procedures, making decisions, and leading patient care, such tasks are affected by poor practices. Consequently, patient and family satisfaction decreases, and adverse patient outcomes, such as mortality and infection, may increase. Azevedo Filho et al. also demonstrated a poor nursing practice environment in Brazil [ 13 ], consistent with our study results. In another study [ 17 ], the average PE score was significantly greater than that in our study and that of Azevedo Filho et al. [ 13 ]. The high score in the Smith et al. research population could be because the surveyed hospitals were magnet hospitals. In magnet hospitals, there is more focus on creating a healthier and more desirable work environment. Our study revealed a significant inverse correlation between PE characteristics and MNCs. In other words, missed nursing care increases significantly in patients with unfavorable PEs. However, this relationship was not strong. Several researchers have emphasized the importance of providing qualified nursing services and improving the nursing work environment [ 17 ].

Among the different dimensions of PE, “nursing foundations for quality of care” and “nurse manager ability, leadership, and support of nurses” had significant relationships with MNCs. These findings suggest that targeted interventions aimed at improving each dimension of PE can help reduce the incidence of MNCs. Additionally, the ability of nursing managers and leaders should be accompanied by reduced missed care because nursing managers are responsible for managing the working conditions of nurses, determining their duties, coordinating existing resources, and developing basic nursing settings for the quality of patient care [ 28 ].

Our study on the relationship between nurses’ occupational and demographic variables and MNCs contradicts the findings of Blackman et al. [ 29 ], who indicated that men’s mean score for missed care is significantly greater than women’s. A study conducted in Iran also showed that female nurses’ quality of nursing care is greater than that of male nurses [ 30 ]. Women tend to care for patients more carefully, and less missed care is provided by women. Except for gender, the results of our study suggested no correlation between MNCs and other occupational and demographic variables of nurses.

Limitations

The study offers insights into missed nursing care and its relationship with the practice environment. However, several limitations should be considered. The study’s cross-sectional design creates potential biases, which may limit our ability to establish causation. Additionally, the reliance on self-reports introduces the likelihood of response bias. Furthermore, the study focused on specific hospitals in Zanjan Province, which may restrict the generalizability of the findings to a broader context. Confounding factors, which are inherent to observational studies, might influence the observed relationships. Despite the abovementioned limitations, the study provides valuable contributions to comprehending the complex dynamics between the practice environment and missed nursing care.

According to our study, nurses consistently neglect a significant portion of nursing care, with patient-related team meetings and training sessions being the most overlooked. This is a noteworthy finding. The findings highlight a possible lack of awareness or inadequacy in planning critical sessions, which demands increased attention. Notably, basic nursing care is the second most commonly overlooked aspect of care. The unfavorable practice environment identified in the hospitals under study highlights the urgent need for improvement by planners and senior managers. Notably, our findings demonstrated a significant statistical relationship between the practice environment and unattended nursing care. This indicates that improving the practice environment could help reduce the number of missed care cases. Notably, managerial competencies, particularly leadership, are vital in preventing overlooked nursing care. These results provide essential insights for the field, highlighting the importance of targeted improvements in practice environments to improve patient care outcomes. Our research provides a foundation for future research and interventions to optimize nursing care delivery.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Analysis of Variance

Missed Nursing Care

National Quality Forum

Practice Environment

Variance Inflation Factor

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Acknowledgements

We want to thank all the nurses who participated in this study. Their invaluable contributions were crucial in making this research possible. We would also like to thank the hospitals in Zanjan Province for their cooperation and support during the data collection. Furthermore, we would like to acknowledge the Zanjan University of Medical Sciences’ Biomedical Research Ethics Committee for approving and overseeing the ethical aspects of this research. We are grateful for their collaboration and commitment to advancing healthcare research, which made this study possible.

This work was supported by the Research and Technology Deputy of Zanjan University of Medical Sciences, Zanjan, Iran (grant number: A-11-86-17).

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Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Zanjan University of Medical Sciences, Zanjan, Iran

Somayeh Babaei

Department of Psychiatric Nursing, School of Nursing and Midwifery, Zanjan University of Medical Sciences, Mahdavi St., Zanjan, 4515789589, Iran

Kourosh Amini

Department of Critical Care Nursing, School of Nursing and Midwifery, Zanjan University of Medical Sciences, Zanjan, Iran

Farhad Ramezani-Badr

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Contributions

Study design: KA. Data collection: SB. Data analysis: KA, FR. Study supervision: KA. Manuscript writing: KA, SB, FR. Critical revisions for important intellectual content: KA, FR.

Corresponding author

Correspondence to Kourosh Amini .

Ethics declarations

Ethics approval and consent to participate.

The research proposal with the code IR.ZUMS.REC.1399.053 was approved by the Zanjan University of Medical Sciences’ Biomedical Research Ethics Committee (ZUMS.REC). We obtained written informed consent from all participants and preserved the confidential identity of each participant throughout the study. Before using the two MISSCARE Survey and Practice Environment Scale questionnaires, permission was obtained from the developers of the participants (Professor Kalisch and Professor Lake, respectively) through email.

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Babaei, S., Amini, K. & Ramezani-Badr, F. Unveiling missed nursing care: a comprehensive examination of neglected responsibilities and practice environment challenges. BMC Health Serv Res 24 , 977 (2024). https://doi.org/10.1186/s12913-024-11386-1

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