Learn About Time Series ARIMA Models in SPSS With Data From the NOAA Global Climate at a Glance (1910–2015)

Dataset

Learn About Time Series ARIMA Models in SPSS With Data From the NOAA Global Climate at a Glance (1910–2015)

By: The Odum Institute Published: 2017 | Product: SAGE Research Methods Datasets Part 1

DOI: https://dx.doi.org/10.4135/9781473995321

This dataset is designed for teaching about autoregressive integrated moving average (ARIMA) models for a single time series variable. The dataset is a subset of data derived the United States National Oceanic and Atmospheric Administration (NOAA) Climate at a Glance website, and the example examines the temporal dynamics in average annual land temperatures in Asia from 1910 to 2015. The dataset file is accompanied by a teaching guide, a student guide, and a how-to guide for SPSS.

Methods: Time-series analysis, ARIMA, Statistical modelling

Prerequisites: Time Series ACFs and PACFs

Data Type: Time-series

Software Guide: SPSS

+ More information

You can preview and download the dataset from this tab. The dataset is available in multiple file formats, compatible with most common software packages. You can also view and download the Codebook, which provides information on the structure, contents, and layout of the dataset.

Looks like you do not have access to this content.

Don’t know how to login?

In this tab you will find guides on using this dataset. The Teaching Guide is designed for faculty who are teaching research methods and statistics, with suggestions on how to use the dataset in lab exercises, in homework assignments and as exam questions. The Student Guide introduces the method for students, and can be used in teaching to provide students with an introductory overview of the method or test. The How-to Guide shows how to perform the technique or test using data analysis software.

Looks like you do not have access to this content.

Don’t know how to login?

About This Dataset

Data Source Citation

National Oceanic and Atmospheric Administration. (1910-2015). Climate at a glance [Data file]. Retrieved from http://www.ncdc.noaa.gov/cag/

Full title of originating dataset

Climate at a Glance – Global

Data author(s) and affiliations

National Centers for Environmental Information, National Oceanic and Atmospheric Administration, U.S. Department of Commerce

Dataset source website address

http://www.ncdc.noaa.gov/cag/

Data Universe

Global-scale temperature information from the Global Historical Climatology Network-Monthly (GHCN-M) dataset and the International Comprehensive Ocean-Atmosphere Data Set (ICOADS)

Funding sources/suppliers

National Oceanic and Atmospheric Administration

Data collection dates

1880 to Present

Time frame of analysis

1910 to 2015

Unit of analysis

Geographic Unit

Location covered by data

Global

Links to SRM content

Pickup, M. (2015). Quantitative applications in the social sciences: Introduction to time series analysis. Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781483390857
Ostrom, C. W., Jr. (Ed.). (1990). Time series analysis. (2nd ed.). Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781412986366
Granados, J. (2008). Time series. In S. Boslaugh, & L. McNutt (Eds.), Encyclopedia of epidemiology (pp. 1036–1039). Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781412953948.n457
Velicer, W., Hoeppner, B., & Goodwin, M. (2010). Time-series study. In N. J. Salkind (Ed.), Encyclopedia of research design (pp. 1520–1529). Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781412961288.n465
Boef, S. (2004). Arima. In M. S. Lewis-Beck, A. Bryman, & T. Futing Liao (Eds.), The SAGE encyclopedia of social science research methods (pp. 23–25). Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781412950589.n22
Introduction to multiple time series models. (2007). In P. T. Brandt, & J. T. Williams (Eds.), Multiple time series models (pp. 2–15). Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781412985215.n1
McDowall, D., McCleary, R., Errol E. Meidinger, & Richard A. Hay, Jr. (Eds.). (1980). Interrupted time series analysis. Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781412984607

List of variables

year

Year

africa

Africa

asia

Asia

europe

Europe

northamerica

North America

southamerica

South America

oceania

Oceania

globalland

Global Land

globalocean

Global Ocean

Also using this dataset

Methods Map

Time-series analysis

Explore the Methods Map

You are viewing a preview of the dataset file. To download the full dataset file close this window and select one of the download options presented.

Learn About Time Series ARIMA Models in SPSS With Data From the NOAA Global Climate at a Glance (1910–2015)

Dataset

Teaching and Learning Material

Dataset Info

Looks like you do not have access to this content.

Looks like you do not have access to this content.

Also using this dataset

Methods Map

Also using this dataset

Methods Map

Institutional Login

Administrators and Non-Institutional Users:

Looks like you do not have access to this content.